Redesigning assessments for AI-enhanced learning: a framework for educators in the generative AI era
Open accessAbstract
The emergence of generative artificial intelligence (Gen AI) in education offers both opportunities and challenges, particularly in the context of student assessment. This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose. A qualitative methodology was employed, gathering data through semi-structured interviews and focus groups, along with examples of redesigned assessments. Sixty-one faculty members participated in the study, and the data were analyzed using both deductive and inductive thematic approaches. Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies. However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns. The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era. Based on these insights, the study developed a conceptual framework titled “Against, Avoid, Adopt, and Explore”. Future research is needed to validate this framework and further refine its application in educational contexts.
AI snapshot
Written from the paper’s full text.
TL;DR
This qualitative study examines why Palestinian higher education faculty redesign assessments in response to generative AI and proposes the “Against, Avoid, Adopt, and Explore” (AAAE) framework. It identifies motivations such as academic integrity, future-career preparation, technological adaptation, institutional policy, and ethics, alongside challenges including equity, time, resistance, and lack of training.
Key findings
- Faculty motivations clustered into five themes: preparing students for future work, technological adaptation, academic integrity, institutional policy, and ethical considerations.
- Challenges clustered into five themes: maintaining academic integrity, time and resource demands, equity and accessibility concerns, resistance to change, and lack of clear guidelines and training.
- The study developed the AAAE framework—Against, Avoid, Adopt, and Explore—for redesigning assessments with different levels of Gen AI integration.
- Redesigned assessment types included product-process assessment, competency-based assessment, and authentic assessment; the paper reports 155 faculty attended workshops, 25 were interviewed, 36 participated in four focus groups, and 61 total participants contributed redesigned assessments.
Methods
- Qualitative design using semi-structured interviews, focus group sessions, and document analysis of redesigned assessments.
- Participants were faculty from Palestinian universities: 25 interview participants purposively sampled from 155 workshop attendees, plus four focus groups of nine participants each (36), totaling 61 participants.
- Data were analyzed with deductive and inductive thematic approaches following Braun and Clarke’s six-step procedure, using NVivo; the paper reports 11.65 hours of interview data, 3.75 hours of focus group discussion, and 89% interrater reliability.
Limitations
- The paper states that future research is needed to validate the AAAE framework and further refine its application in educational contexts.
- The text makes evident that the study is qualitative and context-specific, focusing on early adopters in Palestine who attended the authors’ workshops, which limits generalizability to other settings.
- The text makes evident that the study reports perceptions and redesigned assessment artifacts rather than long-term learning or employability outcomes; the paper itself calls for future research on those impacts.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 28 cited sources checked · 0 supported · 0 flagged
- Findingown data
Many assessments highlighted the deliberate use of Gen AI tools, requiring students to utilize these tools and critically evaluate the outputs.
“Additionally, many assessments highlighted the deliberate use of Gen AI tools, requiring students to utilize these tools and critically evaluate the outputs”
Quote in the paperSubstantial claim - Findingown data
Participants' challenges in redesigning AI-resistant assessments were categorized into five main themes: maintaining academic integrity, time and resource demands, equity concerns, resistance to change, and lack of training.
“The researchers categorized these challenges into five main themes, including maintaining academic integrity, time and resource demands, equity concerns, resistance to change, and lack of training (Table A4).”
Quote in the paperSubstantial claim - Findingown data
Another challenge mentioned by participants was the time and effort required to redesign AI-resistant assessments, including creating innovative tasks, training educators, and adopting new evaluation methods.
“Another challenge mentioned by participants was the time and effort required to redesign AI-resistant assessments, including creating innovative tasks, training educators, and adopting new evaluation methods.”
Quote in the paperNot a substantial claim - Findingown data
The study highlighted a significant challenge: addressing equity and accessibility concerns.
“However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns.”
Quote in the paperNot a substantial claim - Findingown data
This approach was consistently reported as a key strategy to ensure the integrity of student work.
“This approach was consistently reported as a key strategy to ensure the integrity of student work.”
Quote in the paperNot a substantial claim
- Findingown data
The extent of Gen AI integration varied between partial and full depending on the assessment type.
“The extent of integration varied between partial and full depending on the assessment type, as reflected in the interviews, discussions, and assessment instructions.”
Quote in the paperNot a substantial claim - Findingown data
Most participants reported that they still used traditional assessment methods such as traditional exams, oral exams, and presentations to measure lower-level thinking, including remembering and related skills, to reduce academic dishonesty.
“Most participants reported that they still used traditional assessment methods such as traditional exams, oral exams, and presentations to measure lower-level thinking, including remembering and other related skills to reduce academic dishonesty.”
Quote in the paperNot a substantial claim - Findingown data
Participants focused on exploratory assessments that encourage learners to uncover the potential of Gen AI in the learning process.
“Participants focused on exploratory assessments that encourage learners to uncover the potential of Gen AI in the learning process.”
Quote in the paperSubstantial claim - Findingown data
These assessments often leveraged Gen AI to refine the final version of the work or generate ideas, particularly in project-based assessments.
“These assessments often leveraged Gen AI to refine the final version of the work or generate ideas, particularly in project-based assessments.”
Quote in the paperNot a substantial claim - Findingown data
After analyzing and categorizing the assessments, a clear consensus emerged regarding AI-resistant assessments.
“After analyzing and categorizing the assessments, as outlined in Figure 1, a clear consensus emerged regarding AI-resistant assessments.”
Quote in the paperSubstantial claim - Findingown data
Even participants who predominantly used traditional assessment methods incorporated Gen AI into their tasks.
“Interestingly, even participants who predominantly used traditional assessment methods incorporated Gen AI into their tasks.”
Quote in the paperNot a substantial claim - Findingown data
The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era.
“The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era.”
Quote in the paperSubstantial claim - Findingown data
This lack of clear guidelines and training was considered an important challenge by the majority of the participants.
“This was considered an important challenge by the majority of the participants”
Quote in the paperSubstantial claim - Findingown data
Many participants developed various types of assessments that integrated Gen AI.
“Many participants developed various types of assessments that integrated Gen AI.”
Quote in the paperSubstantial claim - Findingown data
Equity and accessibility were important concerns for most participants.
“Equity and accessibility were important concerns for most of the participants.”
Quote in the paperSubstantial claim - Findingown data
The study highlighted a significant challenge: the need for professional development.
“However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns.”
Quote in the paperNot a substantial claim - Findingown data
A key motivation for redesigning assessments was aligning with institutional policies.
“Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies.”
Quote in the paperNot a substantial claim - Findingown data
Some participants were disinclined to change their assessment modes due to familiarity with traditional assessments.
“Some participants were disinclined to change their assessment modes due to familiarity with the traditional assessments.”
Quote in the paperNot a substantial claim - Findingown data
Many participants reported difficulties ensuring that redesigned assessments effectively distinguish between AI-assisted work and authentic student contributions.
“Many participants reported difficulties ensuring that redesigned assessments effectively distinguish between AI-assisted work and authentic student contributions.”
Quote in the paperSubstantial claim- Rests on an assumption: Gen AI tools can mimic high-quality academic writing, making it challenging to verify originality.
- Findingown data
A key motivation for redesigning assessments was adapting to technological advancements.
“Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies.”
Quote in the paperSubstantial claim - Findingown data
These additional demands strain already limited resources, as supported by the discussion in the focus group sessions.
“These additional demands strain already limited resources, as supported by the discussion in the focus group sessions”
Quote in the paperNot a substantial claim - Findingown data
A key motivation for redesigning assessments was maintaining academic integrity.
“Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies.”
Quote in the paperSubstantial claim - Findingown data
A key motivation for redesigning assessments was preparing learners for future careers.
“Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies.”
Quote in the paperNot a substantial claim - Findingown data
Participants' motivations for redesigning assessments in response to Gen AI integration were categorized into five main themes: preparing students for future work, technological adaptation, academic integrity, institutional policy, and ethical considerations.
“The researchers categorized these motivations into five main themes: preparing students for future work, technological adaptation, academic integrity, institutional policy, and ethical considerations (Table A3).”
Quote in the paperSubstantial claim - Findingown data
Many participants placed importance on the product-process approach, focusing on learners working with AI tools as a form of human-AI collaboration.
“Many participants placed importance on this approach, focusing on learners working with AI tools as a form of human AI collaboration.”
Quote in the paperSubstantial claim - Findingnone given
Many institutions lack clear policies or guidelines on incorporating or resisting AI in assessments, leaving the faculty to navigate this challenge.
“Many institutions lack clear policies or guidelines on incorporating or resisting in assessments, leaving the faculty to navigate this challenge.”
Quote in the paperSubstantial claim - Findingown data
Faculty participants in the study reported four key principles-against, avoid, adapt, and explore-for redesigning assessments in the Gen AI era.
“They identified four key principles, namely against, avoid, adapt, and explore, that participants reported during discussion sessions and interviews for redesigning assessments.”
Quote in the paperSubstantial claim - Findingown data
Many participants identified institutional policies as the driving force behind their decision to redesign assessments.
“Many participants identified institutional policies as the driving force behind their decision to redesign assessments.”
Quote in the paperNot a substantial claim - Findingown data
When AI-resistant tasks are introduced, there is often backlash because students are used to traditional exams.
“Traditional exams are what they're used to-when we introduce AI-resistant tasks, there's often backlash.(Observation from interviews)”
Quote in the paperNot a substantial claim - Findingown data
Group projects in which students collaborate to create content using AI helped them reflect on the strengths and limitations of the tools.
“Group projects where students collaborate to create content using AI helped them reflect on the strengths and limitations of the tools.(F5)”
Quote in the paperSubstantial claim - Findingcitation
Participants listed disparities in access to AI tools and digital infrastructure as particularly salient concerns.
“Echoing the findings of Sembey et al. (2024), participants listed disparities in access to AI tools and digital infrastructure as particularly salient concerns.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Sembey et al. (2024) source
Not checked in the free run.
- Study design and conductown data
Coding data from individual interviews were cross-referenced with focus group data to confirm or refine themes, ensuring consistency and validity.
“These initial codes were then cross-referenced with data from focus group discussions to confirm or refine themes, ensuring consistency and validity.”
Quote in the paperSubstantial claim - Premisecitation
One of the most significant challenges of Gen AI tools in assessment is their inability to detect plagiarism and cheating.
“According to Rudolph et al. (2023), one of the most significant challenges of Gen AI tools in assessment is their inability to detect plagiarism and cheating.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Rudolph et al., 2023
Not checked in the free run.
- Study design and conductreasoning
Two distinct data collection methods ensured triangulation of the findings, strengthening credibility.
“Two distinct data collection methods ensured the triangulation of the findings, strengthening credibility.”
Quote in the paperNot a substantial claim - Premisereasoning
A gap remains in understanding Gen AI's effective incorporation into assessment practices, especially in higher education.
“However, a gap remains in understanding Gen AI's effective incorporation into assessment practices, especially in higher education.”
Quote in the paperSubstantial claim - Premisecitation
Existing research predominantly focuses on AI applications for automating grading or providing instant feedback, with limited attention to how AI can fundamentally transform the nature and methodology of assessment itself.
“While existing research predominantly focuses on AI applications for automating grading or providing instant feedback, limited attention has been given to how AI can fundamentally transform the nature and methodology of the assessment itself.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Sajja et al., 2024
Not checked in the free run.
- cites Ruiz-Rojas et al., 2023
Not checked in the free run.
- Premisecitation
Social constructivism provides a theoretical foundation for AI-resistant assessments by emphasizing learning as a collaborative and context-driven process.
“Social constructivism, grounded in the work of Vygotsky's work, provides a theoretical foundation for AI-resistant assessments by emphasizing learning as a collaborative and context-driven process.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Vygotsky
Not checked in the free run.
- Premisecitation
AI evaluations frequently encounter ethical issues related to bias, privacy, and the ownership of data.
“Notably, AI evaluations frequently encounter ethical issues related to bias, privacy, and the ownership of data.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Khlaif et al., 2024 source
Not checked in the free run.
- Premisecitation
AI-supported education systems may amplify racial, economic, and social gaps by potentially relying on data with systematic biases against certain groups.
“Likewise, AI-supported education systems may amplify racial, economic, and social gaps by potentially relying on data with systematic biases against certain groups.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zawacki-Richter et al., 2019
Not checked in the free run.
- Premisecitation
AI-resistant assessments promote students' ownership of their learning journey through documentation of their processes and critical reflection.
“AI-resistant assessments promote students' ownership of their learning journey through the documentation of their processes and critical reflection regarding their decisions.”
Quote in the paperSubstantial claim0 of 2 sources checked - Study design and conductreasoning
The triangulation process allowed comparison of findings across sources, ensuring that the final themes were robustly reflective of diverse perspectives.
“This triangulation process allowed us to compare findings across sources, ensuring that the final themes were robustly reflective of diverse perspectives.”
Quote in the paperSubstantial claim - Interpretationown data
Gen AI can serve as a tool for brainstorming and co-creation, enabling learners to refine and expand their ideas collaboratively.
“Gen AI can serve as a tool for brainstorming and co-creation, enabling learners to refine and expand their ideas collaboratively.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 90
- May combine two claims. 52
- The quote only partly states this claim. 4
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThis type of assessment encourages students to engage in developing the prompts to enhance the outcome within the adopted and explored principles.
“Vital to integrating Gen AI in teaching, this type of assessment encourages students to engage in developing the prompts to enhance the outcome within the adopted and explored principles”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingnone givenThis approach aimed to cultivate higher-order thinking, critical reflection, and the ability to adapt AI capabilities for innovative problem solving while also emphasizing the responsible use of technology.
“This approach aimed to cultivate higher-order thinking, critical reflection, and the ability to adapt AI capabilities for innovative problem solving while also emphasizing the responsible use of technology”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe majority of participants noted they are forced to adapt to new policies and rethink improving the assessment process.
“The majority of the participants noted they are forced to adapt to new policies and rethink improving the assessment process”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataParticipants highlighted that AI-resistant assessments entail designing tasks that AI cannot easily solve, thereby encouraging deeper cognitive engagement from students.
“Participants also highlighted that these assessments entail designing tasks that AI cannot easily solve, thereby encouraging deeper cognitive engagement from students.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S1 (asserted). May combine two claims.
Findingown dataThese approaches aim to evaluate students' higher-order thinking skills, which participants claim Gen AI tools struggle to address effectively.
“These approaches aim to evaluate students' higher-order thinking skills, which participants claim Gen AI tools struggle to address effectively.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataMost participants highlighted the need to emphasize creativity, critical thinking, and analytical skills, areas in which Gen AI falls short.
“Most participants highlighted the need to emphasize creativity, critical thinking, and analytical skills, areas in which Gen AI falls short.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataSome participants were motivated to align Palestinian higher education with global trends.
“Some participants were motivated to align Palestinian higher education with global trends.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataSome participants emphasized the importance of developing and implementing innovative assessment methods that minimize the use of Gen AI.
“Some participants emphasized the importance of developing and implementing innovative assessment methods that minimize the use of Gen AI.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataSeveral participants mentioned promoting active and authentic learning by engaging students in real-world tasks and project-based assessments.
“Several participants mentioned promoting active and authentic learning by engaging students in real-world tasks and project-based assessments.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataFaculty members emphasized that while AI provides valuable support in tasks like brainstorming and drafting, it cannot supplant essential cognitive processes such as critical thinking and self-directed learning.
“Faculty members emphasized that while AI provides valuable support in tasks like brainstorming and drafting, it cannot supplant essential cognitive processes such as critical thinking and self-directed learning.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingown dataMany faculty members expressed a desire to guarantee that student work reflects their understanding and the skills taught in the course, even when they incorporate AI into their learning.
“Many faculty members expressed a desire to guarantee that student work reflects their understanding and the skills taught in the course, even when they incorporate AI into their learning”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOther participants mentioned that learners accustomed to traditional assessments may resist transitioning to AI-resistant formats.
“Other participants mentioned that learners accustomed to traditional assessments may resist transitioning to AI-resistant formats.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
FindingreasoningThis disparity creates challenges in designing fair assessments.
“This disparity creates challenges in designing fair assessments”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataIncorporating AI tools for enhanced learning was reported by several participants in focus group sessions, with AI improving learners' engagement, learning, and understanding, as reported by many interview participants.
“Incorporating AI tools for enhanced learning was also reported by several participants in the focus group sessions, with AI being a source to improve learners' engagement and learning and understanding, as reported by many of the participants in the interviews”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants highlighted several justifications for integrating Gen AI into assessments, including teaching students how to use AI responsibly, fostering an understanding of AI ethics, enhancing students' engagement in the generated content, and encouraging its use in specific parts of the assessment process.
“Participants highlighted several justifications for integrating Gen AI into assessments, including teaching students how to use AI responsibly, fostering an understanding of AI ethics, enhancing students' engagement in the generated content, and encouraging its use in specific parts of the assessment process”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenThese exploratory assessments involved activities such as problem solving, co-design, learner-AI collaboration, and fostering creativity.
“These assessments involved activities such as problem solving, co-design, learner-AI collaboration, and fostering creativity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataIn this type of assessment, students were not allowed to use any Gen AI tool.
“In this type of assessment, students were not allowed to use any Gen AI tool”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataSeveral participants emphasized that redesigning assessments is crucial to distinguishing between AI-assisted work and authentically student-generated work.
“Additionally, several participants emphasized that redesigning assessments is crucial to distinguishing between AI-assisted work and authentically student-generated work”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataParticipants highlighted the importance of addressing ethical issues and promoting responsible AI use among students, ensuring they understand AI tools' capabilities and limitations.
“Participants highlighted the importance of addressing ethical issues and promoting responsible AI using among students, ensuring they understand AI tools' capabilities and limitations.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenSince students have diverse backgrounds, not all have equal access to AI tools or the digital infrastructure.
“Since students have diverse backgrounds, not all have equal access to AI tools or the digital infrastructure.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFaculty members designed rubrics to accompany the assessments, which served as guidelines for students on how to use Gen AI in their work and outlined the criteria faculty would use to evaluate the final product.
“Furthermore, it was observed that faculty members designed rubrics to accompany the assessments (Appendix B).These rubrics served as guidelines for students on how to use Gen AI in their work, and they outlined the criteria faculty would use to evaluate the final product.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataThe majority of participants indicated that AI-resistant assessments must address the challenges of the Gen AI era, mitigating the AI misuse risk, while leveraging its potential to support productive learning.
“The majority of participants indicated that these assessments must address the challenges of the Gen AI era, mitigating the AI misuse risk, while leveraging its potential to support productive learning.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingown dataMany participants expressed concern that, without proper guidance, students might misuse or rely on AI inappropriately, compromising their learning.
“Many participants expressed concern that, without proper guidance, students might misuse or rely on AI inappropriately, compromising their learning”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingown dataEducators highlighted the need to redesign assessments to ensure that students engage meaningfully with the material and develop original ideas, even when leveraging AI as a supplementary tool.
“As part of their ethical responsibility, educators highlighted the need to redesign assessments to ensure that students engage meaningfully with the material and develop original ideas, even when leveraging AI as a supplementary tool”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataMany participants reported an intention to equip learners with practical AI skills and competencies for their future careers.
“Many participants also reported their intention to equip learners with practical AI skills and competencies for their future careers”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataMany participants identified enhancing creativity and innovation in student work because students can collaborate with AI in innovative ways, encouraging unique problem-solving approaches.
“Many participants identified enhancing creativity and innovation in student work since students can collaborate with AI in innovative ways, encouraging unique problem-solving approaches”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFaculties often need to overcome institutional inertia and skepticism from colleagues and learners, as mentioned by some participants.
“However, faculties often need to overcome institutional inertia and skepticism from colleagues and learners, as mentioned by some participants”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataThe focus was placed on emphasizing critical engagement with Gen AI, encompassing the final product and the process involved in completing a task, as reported by a faculty member in educational sciences.
“The focus was placed on emphasizing critical engagement with Gen AI, encompassing the final product and the process involved in completing a task, as reported by a faculty member in educational sciences.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
FindingreasoningExamples of innovative assessment methods that minimize Gen AI use included performance-based assessments, individual portfolios, and reflection papers.
“Examples included performance-based assessments, individual portfolios, and reflection papers.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataSome participants presented a new competency-based assessment approach to assess learners' application of theoretical knowledge in practical situations.
“Some of the participants presented a new competency-based assessment approach to assess learners' application of theoretical knowledge in practical situations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataDeveloping innovative assessments requires resources that are not available in most cases.
“Developing innovative assessments requires resources that are simply not available in most cases.(Observation from focus groups)”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenOne professor reported that AI enhanced virtual partners' ability to assess clinical decision-making and problem-solving skills in high-pressure scenarios.
“One professor reported that AI enhanced virtual partners' ability to assess clinical decision-making and problem-solving skills in high-pressure scenarios.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationParticipants identified noteworthy obstacles concerning the misuse of AI: maintaining academic integrity, addressing equity issues, overcoming resistance to change, and preventing the obfuscation of student contributions.
“In line with previous studies, including Swiecki et al. (2022) and Rudolph et al. (2023), participants identified noteworthy obstacles concerning the misuse of AI: maintaining academic integrity, addressing equity issues, overcoming resistance to change, and preventing the obfuscation of student contributions.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Swiecki et al. (2022) source
Not checked in the free run.
- cites Rudolph et al. (2023)
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Findingown dataNot all students are familiar with AI, and some lack reliable Internet access, making equitable assessment difficult.
“Not all students are familiar with AI, and some don't even have reliable Internet.It's hard to make assessments equitable under these circumstances.(F13)”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Findingnone givenResistance to change and disparities in access to AI tools and digital infrastructure further complicate the process.
“Resistance to change and disparities in access to AI tools and digital infrastructure further complicate the process.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataOne of the key insights that emerged was the need for authentic assessments that reflect real-world challenges, requiring students to apply knowledge in complex, context-rich situations.
“One of the key insights that emerged was the need for authentic assessments that reflect real-world challenges, requiring students to apply knowledge in complex, contextrich situations”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataSome colleagues do not see the urgency of redesigning assessments, and students often push back on tasks that demand more effort.
“Some colleagues don't see the urgency of redesigning assessments, and students often push back on tasks that demand more effort.(F25)”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataThere is no institutional framework to guide faculty in integrating AI into assessments.
“We're left to figure this out on our own; there's no institutional framework to guide us.(FG1)”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. The quote only partly states this claim.
FindingreasoningBuilding on the four principles and the degrees of Gen AI integration in the assessments, the researchers developed the Against, Avoid, Adopt, and Explore (AAAE) conceptual framework.
“Building upon these principles and the degrees of Gen AI integration in the assessments, they developed a conceptual framework illustrated in Figure 1.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataStudents from rural areas face issues accessing online resources, let alone experimenting with AI.
“Students from rural areas face issues even accessing online resources, let alone experimenting with AI. (Paraphrased from focus group discussions)”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. The quote only partly states this claim.
Findingown dataThe researchers classified the redesigned assessments developed by participants into four categories (against, avoid, adapt, explore).
“Additionally, the researchers classified the redesigned assessments into these four categories.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationMany participants focused on addressing Gen AI's weaknesses, entailing redesigning assessments to account for elements like critical thinking and originality, promoting improvements to student engagement and learning experiences via AI's use as a brainstorming or co-creation tool.
“In line with previous studies (Aziz et al., 2020;Khlaif et al., 2024;Waladi & Lamarti, 2024) on technological advancements to improve assessment authenticity and creativity, many participants focused on addressing Gen AI's weaknesses.This entails redesigning assessments to account for elements like critical thinking and originality, promoting improvements to student engagement and learning experi”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Aziz et al., 2020
Not checked in the free run.
- cites Khlaif et al., 2024 source
Not checked in the free run.
- cites Waladi & Lamarti, 2024
Not checked in the free run.
Classified as probably not a substantial claim.
FindingreasoningInstitutional policies and ethical considerations play a central role in driving the redesign of assessments.
“Institutional policies and ethical considerations play a central role in driving the redesign of assessments.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataFaculty are expected to innovate while balancing heavy workloads, with insufficient time to experiment and redesign.
“We're expected to innovate while balancing heavy workloads-there's just not enough time to experiment and redesign.(FG4)”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataEthical considerations featured prominently, with importance placed on responsible AI use.
“In addition, the vitality of ethical considerations also featured prominently, with importance placed on responsible AI use.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingnone givenAI can answer factual questions but cannot evaluate or synthesize ideas.
“In my course, AI can answer factual questions, but it can't evaluate or synthesize ideas.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingown dataIt is becoming increasingly difficult to identify where the student's work ends and the AI's begins.
“It's becoming increasingly difficult to identify where the student's work ends and the AI's begins.(F5)”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataParticipants expressed the importance of authentic assessment for enhancing creativity, collaboration, and critical thinking, but noted that faculty should design accompanying rubrics.
“The participants expressed the importance of authentic assessment for enhancing creativity, collaboration, and critical thinking, but they noted that the faculty should design accompanying rubrics.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingnone givenThe study identifies significant challenges related to the integration of Gen AI, including maintaining academic integrity, addressing equity issues, and preventing the obfuscation of student contributions.
“At the same time, the study identifies significant challenges related to the integration of Gen AI.These include maintaining academic integrity, addressing equity issues, and preventing the obfuscation of student contributions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S1 (asserted).
Findingown dataMinimal training is provided for understanding how to integrate or avoid AI in assessments.
“There's minimal training provided for understanding how to integrate or avoid AI in assessments.(General comment from faculty)”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Findingnone givenUsing tools like ChatGPT in projects allowed students to explore real-world tasks and learn collaboratively.
“Using tools like ChatGPT in projects allowed students to explore real-world tasks and learn collaboratively.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe transcribed files were shared with participants for member checking, allowing them to review and amend their statements as needed, thereby enhancing accuracy.
“The transcribed files were shared with participants for member checking, allowing them to review and amend their statements as needed, thereby enhancing accuracy.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAI-resistant assessments align with educational principles by fostering academic integrity, inclusivity, and real-world applicability, ensuring that students' learning outcomes demonstrate human capabilities.
“AI-resistant assessments align with educational principles by fostering academic integrity (Kostanek & Li, 2025), inclusivity (Ahmed et al., 2024), and real-world applicability, ensuring that students' learning outcomes demonstrate human capabilities (Abubakar et al., 2024;Awadallah Alkouk & Khlaif, 2024).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Kostanek & Li, 2025
Not checked in the free run.
- cites Ahmed et al., 2024
Not checked in the free run.
- cites Abubakar et al., 2024 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe study was conducted following approval from the Institutional Review Board (IRB) at An Najah University Committee, with approval number Intr.April.2024/18.
“The study was conducted following approval from the Institutional Review Board (IRB) at An Najah University Committee, with approval number Intr.April.2024/18.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. The quote only partly states this claim.
Study design and conductown dataRedesigned assessments were systematically collected and evaluated to ensure alignment with AI-resistant design and workshop principles.
“Each assessment was systematically collected and evaluated to ensure alignment with two key aspects, as follows.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataParticipants for interviews were selected using purposive sampling to ensure diversity and representation.
“From a pool of 155 faculty members who attended training sessions, 25 participants were selected for interviews using purposive sampling to ensure diversity and representation.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningInstitutional representation was an important consideration, with participants drawn from multiple universities to capture a variety of institutional policies and technology-in-education resources.
“Institutional representation was another important consideration, with participants drawn from multiple universities to capture a variety of institutional policies and technology-in-education resources.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataDocumented examples of AI-resistant assessments were analyzed as a third source, validating and contextualizing the codes generated from the verbal data.
“Additionally, documented examples of AI-resistant assessments were analyzed as a third source, validating and contextualizing the codes generated from the verbal data.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenPriority for focus group selection was given to faculty members with substantial experience adapting assessments to AI considerations.
“Experience with AI-Resistant Assessment Design: Priority was given to faculty members with substantial experience adapting assessments to AI considerations.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe study emphasized disciplinary diversity, involving faculties from various academic fields from humanities to engineering.
“Additionally, the study emphasized disciplinary diversity, involving faculties from various academic fields from humanities to engineering.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe interviews and focus group discussions were conducted in Arabic, and the researchers employed a backward translation process paired with conceptual equivalence to ensure linguistic and contextual accuracy.
“As the interviews and focus group discussions were conducted in Arabic, the researchers employed a backward translation process paired with conceptual equivalence to ensure linguistic and contextual accuracy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningFocus group participants were chosen based on their willingness to engage collaboratively.
“Openness to Collaboration: Participants were chosen based on their willingness to engage collaboratively, ensuring meaningful contributions to group discussions.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationImplementing AI-resistant assessments strengthens critical thinking, encourages independent problem-solving, and enhances key skills including adaptability and ethical reasoning.
“Implementing AI-resistant assessments strengthens critical thinking, encourages independent problem-solving, and enhances key skills including adaptability and ethical reasoning (Kostanek & Li, 2025).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kostanek & Li, 2025
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe analysis closely aligned with the study's research questions, providing a structured and methodical framework for uncovering key findings.
“The analysis closely aligned with the study's research questions, providing a structured and methodical framework for uncovering key findings.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe audio recordings were transcribed and subsequently validated by the participants to ensure accuracy and reliability.
“The researchers transcribed the audio recordings, which were subsequently validated by the participants to ensure accuracy and reliability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningArtifact analysis provided a complementary perspective that allowed the researchers to contextualize and validate the themes derived from the qualitative data.
“This artifact analysis provided a complementary perspective, allowing the researchers to contextualize and validate the themes derived from the qualitative data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisereasoningIn the long run, AI-resistant assessments can foster critical thinking, enhance human-AI collaboration, and prepare individuals for future use of AI as a supportive tool.
“In the long run, AI-resistant assessments can foster critical thinking, enhance human-AI collaboration, and prepare individuals for future use of AI as a supportive tool.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe researchers adopted a code-recode strategy in which they coded the data independently three times and compared the results to maintain consistency, ensuring dependability.
“The researchers adopted a code-recode strategy in which they coded the data independently three times and compared the results to maintain consistency, ensuring dependability.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningBy intentionally cross-verifying data and examining convergence and divergence among sources, the researchers upheld methodological rigor and strengthened the credibility of the study's findings.
“By intentionally cross-verifying data and examining convergence and divergence among sources, we upheld methodological rigor and strengthened the credibility of the study's findings.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing). May combine two claims.
PremisecitationAI applications cannot analyze higher-order thinking skills, leading to a narrow perception of the aspects of learning a student has achieved.
“Azevedo and Gašević (2019) claimed that AI applications cannot analyze higher-order thinking skills, leading to a narrow perception of the aspects of learning a student has achieved.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Azevedo and Gašević, 2019
Not checked in the free run.
May combine two claims.
Study design and conductreasoningConfirmability was achieved by documenting the research process and data analysis, ensuring participants' perspectives were faithfully represented without undue influence from researchers' interpretations.
“Confirmability was achieved by documenting the research process and data analysis, ensuring participants' perspectives were faithfully represented without undue influence from researchers' interpretations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.
Study design and conductown dataAll participants provided informed consent, and the protocol emphasized voluntary participation, confidentiality, anonymity, and the right to withdraw.
“All participants provided informed consent, which was facilitated by a consent form included in the interview and focus group protocols.”
- Also stated in: § 3.8. Ethical Considerations · plain
“Participants were informed that their agreement to proceed would serve as consent, and they were reminded of their right to withdraw from the study at any time.”
Quote in the paperNot a substantial claim- Also stated in: § 3.8. Ethical Considerations · plain
May combine two claims.
PremisecitationAI tools used in student assessments are a 'black box' due to lack of transparency in evaluation criteria, making it difficult to understand how assessment results are reached.
“Couldry (2020) described the hidden nature of AI tools when used in student assessments as a”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Couldry, 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningParticipants were selected through purposive sampling to ensure the relevance and applicability of findings across similar contexts.
“For transferability, participants were selected through purposive sampling to ensure the relevance and applicability of findings across similar contexts.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenInterview participants were chosen to reflect a range of experiences with Gen AI tools, from novice to substantial AI expertise.
“Participants were chosen to reflect a range of experiences with Gen AI tools, from novice to those with substantial AI expertise, granting comprehensive understanding of different approaches to integrating AI in assessments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe interview protocol was based on the study's research questions, a pilot interview, and input from experts in computer science and educational technology.
“The interview protocol was based on the study's research questions, a pilot interview, and input from experts in computer science and educational technology.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe study developed a conceptual framework titled 'Against, Avoid, Adopt, and Explore'.
“Based on these insights, the study developed a conceptual framework titled”
Quote in the paperSubstantial claim- Rests on an assumption: The workshops' participants are early adopters whose perspectives capture the collective wisdom of practitioners regarding assessment adaptation.
May combine two claims.
Study design and conductown dataThe calculated interrater reliability was 89%, demonstrating robust agreement among coders.
“The calculated interrater reliability was 89%, demonstrating robust agreement among coders.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationGen AI applications can be used by teachers to assess student learning, but this capability is limited to measuring aspects like recall and does not extend to evaluating implicit aspects of personality, including critical and creative thinking.
“While Williamson and Eynon (2020) indicated that Gen AI applications can be used by teachers to assess student learning, this capability is not comprehensive across all learning aspects.Indeed, it is limited to measuring aspects like recall and did not extend to evaluating implicit aspects of personality, including critical and creative thinking.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Williamson and Eynon, 2020
Not checked in the free run.
May combine two claims.
PremisecitationAI-resistant assessments mitigate plagiarism risks and excessive dependence on AI.
“This strategy mitigates plagiarism risks and excessive dependence on AI, promoting an informed approach to technology use (Eze, 2024;Shivshankar & Acharya, 2025).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Eze, 2024
Not checked in the free run.
- cites Shivshankar & Acharya, 2025
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Interpretationnone givenThe framework addresses critical issues such as academic integrity, student engagement, alignment with 21st-century skills, and the Sustainable Development Goals (SDGs).
“This framework addresses critical issues such as academic integrity, student engagement, alignment with 21st-century skills, and the Sustainable Development Goals (SDGs).”
Quote in the paperSubstantial claim- Rests on an assumption: The workshops' participants are early adopters whose perspectives capture the collective wisdom of practitioners regarding assessment adaptation.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationown dataFaculty must prepare students to be familiar with AI tools, or students will fall behind.
“I have to prepare my students in my courses to be familiar with AI tools; if I do not do so, they will be behind AI, and you know AI is everywhere now.(F2)”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenTasks requiring critical thinking and creativity address skills that AI tools currently cannot authentically replicate.
“We design tasks requiring critical thinking and creativity, skills that AI tools currently cannot authentically replicate.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningEmbedding ethical reasoning into AI-enhanced assessments is essential to prepare students for real-world challenges and align educational practices with broader global movements toward sustainability.
“Embedding ethical reasoning into AI-enhanced assessments is essential to prepare students for real-world challenges and align educational practices with broader global movements toward sustainability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningThe examples in Figure 2 illustrate the potential of AI to transform assessments in ways that prepare students for real-world challenges, offering a dynamic and interactive learning experience.
“The examples in Figure 2 illustrate the potential of AI to transform assessments in ways that prepare students for real-world challenges, offering a dynamic and interactive learning experience.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationcitationThe proposed Against, Avoid, Adopt, and Explore (AAAE) framework builds on previous work to assist educators in navigating these issues and granting equitable attention to ethical considerations, institutional demands, and student needs.
“The proposed Against, Avoid, Adopt, and Explore (AAAE) framework builds on previous work (e.g., Lye & Lim, 2024;Grassini, 2023;Waladi & Lamarti, 2024) to assist educators in navigating these issues and granting equitable attention to ethical considerations, institutional demands, and student needs.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Lye & Lim, 2024
Not checked in the free run.
- cites Grassini, 2023
Not checked in the free run.
- cites Waladi & Lamarti, 2024
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenEncouraging learners to use AI ethically ensures they develop the judgment needed to navigate these tools effectively.
“Encouraging learners to use AI ethically ensures they develop the judgment needed to navigate these tools effectively.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenStudents might rely too heavily on AI, risking the undermining of their learning processes.
“Students might rely too heavily on AI, risking the undermining of their learning processes.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataHigher-order thinking, creativity, and active learning are key areas that can be influenced when transforming assessment approaches via Gen AI.
“Higher-order thinking, creativity, and active learning are key areas that can be influenced when transforming assessment approaches via Gen AI.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningBy integrating AI into assessments, educators can design tasks that promote critical thinking and originality while improving student engagement.
“By integrating AI into assessments, educators can design tasks that promote critical thinking and originality while improving student engagement.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenWorkshops on ethical AI integration help clarify the boundaries and expectations for using these tools.
“Workshops on ethical AI integration help clarify the boundaries and expectations for using these tools.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThis transformation requires a fundamental reevaluation of traditional assessment paradigms, ensuring that human-AI collaboration becomes a cornerstone of future educational practices.
“However, this transformation requires a fundamental reevaluation of traditional assessment paradigms, ensuring that human-AI collaboration becomes a cornerstone of future educational practices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThese findings underscore the importance of clear, fair, and accessible institutional policies that guide the ethical use of AI in education.
“These findings underscore the importance of clear, fair, and accessible institutional policies that guide the ethical use of AI in education.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenRedesigning assessments to focus on the process rather than just the product helps mitigate over-reliance on AI.
“Redesigning assessments to focus on the process and not just the product helps in mitigating over-reliance on AI.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIncluding students in redesigning assessments that integrate AI encourages transparency and ownership.
“Including students in redesigning assessments that integrate AI encourages transparency and ownership.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThrough thoughtful implementation, the AAAE framework provides a valuable foundation for addressing the complexities of the Gen AI era.
“Through thoughtful implementation, the AAAE framework provides a valuable foundation for addressing the complexities of the Gen AI era.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationSuch a finding emphasizes the need to embed ethical reasoning into AI-enhanced assessments if educators are to sufficiently prepare students for the real-world challenges that await us.
“Such a finding emphasizes the need to embed ethical reasoning into AI-enhanced assessments if educators are to sufficiently prepare students for the real-world challenges that await us (Waladi & Lamarti, 2024).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Waladi & Lamarti, 2024
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThese challenges highlight the critical need for clarity, fairness, and accessibility in guidelines and institutional policies that support ethical AI use.
“These challenges highlight the critical need for clarity, fairness, and accessibility in those guidelines and institutional policies that support ethical AI use.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Interpretationown dataThis study highlights the opportunities and challenges of redesigning assessment practices in the Gen AI era, drawing connections and highlighting the dynamic, interrelated nature of such challenges.
“This study highlights the opportunities and challenges of redesigning assessment practices in the generative artificial intelligence (Gen AI) era, drawing connections and highlighting the dynamic, interrelated nature of such challenges.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationThis finding aligns with global educational practices, reflecting broader trends in aligning educational practices with global movements like the United Nations' Sustainable Development Goals.
“This finding aligns with global educational practices, reflecting broader trends in aligning educational practices with global movements like the United Nations' Sustainable Development Goals (George & Wooden, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites George & Wooden, 2023
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenInformed consent was obtained from all subjects involved in the study.
“Informed consent was obtained from all subjects involved in the study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe proposed AAAE framework provides educators with a structured approach to navigating these complexities, offering equitable consideration of ethical considerations, institutional demands, and student needs.
“The proposed Against, Avoid, Adopt, and Explore (AAAE) framework provides educators with a structured approach to navigating these complexities, offering equitable consideration of ethical considerations, institutional demands, and student needs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of An Najah University (protocol code Intr.April.2024/18, 15 April 2024).
“The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of An Najah University Committee (protocol code Intr.April.2024/18 and 15 April 2024).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study used three data sources, prioritizing the analysis of redesigned assessments developed by participants for their courses.
“Here, the researchers utilized three data sources, prioritizing the analysis of redesigned assessments developed by participants for their courses.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenOne of the key opportunities highlighted is the ability of Gen AI to enhance higher-order thinking, creativity, and active learning.
“One of the key opportunities highlighted is the ability of Gen AI to enhance higherorder thinking, creativity, and active learning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataStudents need to collaborate with AI in ways that emphasize their original contributions and unique problem-solving skills.
“Students need to collaborate with AI in ways that emphasize their original contributions and unique problem-solving skills.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The discourse surrounding Gen AI in education has been mainly concentrated in Global North contexts, overlooking its potential adaptation and implementation in unique cases with economic and political limitations, like Palestine and some other countries in the Middle East.
“Moreover, the discourse surrounding Gen AI in education has been mainly concentrated in Global North contexts, overlooking its potential adaptation and implementation in unique cases with economic and political limitations, like Palestine and some other countries in the Middle East.”
The paper relies on this without support.
The challenges and resource constraints faced by educational systems in Palestine necessitate tailored approaches to AI integration.
“The challenges and resource constraints faced by educational systems in Palestine necessitate tailored approaches to AI integration.”
The paper relies on this without support.
Current Gen AI tools have limitations, such as social biases in data sets.
“While Gen AI might demonstrate the potential to reform education by enabling the context-based evaluation of student learning outcomes, current Gen AI tools have limitations, such as social biases in data sets.”
The paper relies on this without support.
The workshops' participants are early adopters whose perspectives capture the collective wisdom of practitioners regarding assessment adaptation.
“By examining the perspectives and experiences of higher education faculty who participated in a series of workshops on Gen AI conducted by the authors, this research captures the collective wisdom of practitioners regarding the adaptation of assessments to the Gen AI era.”
The paper relies on this without support.
Gen AI tools can mimic high-quality academic writing, making it challenging to verify originality.
“Gen AI tools can mimic high-quality academic writing, making it challenging to verify originality”
The paper relies on this without support.
The four principles identified from participants (against, avoid, adapt, explore) comprehensively capture the range of assessment redesign approaches.
“They identified four key principles, namely against, avoid, adapt, and explore, that participants reported during discussion sessions and interviews for redesigning assessments.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Political and economic limitations impede infrastructure access in resource-constrained regions like Palestine and Somalia.
“where political and economic limitations impede infrastructure access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Section 1.1 (Research Gap; page unavailable) says AI-in-education discourse is concentrated in Global North contexts and identifies economic and political limitations in Palestine. Section 4.2.3 (Equity and Accessibility Concerns; page unavailable) reports unequal access to AI tools and digital infrastructure. The copy does not identify Somalia or establish that political and economic limitations cause the reported infrastructure-access problems. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLLMs support higher-order cognitive functions aligned with analysis, evaluation, and creation in Bloom's Taxonomy, whereas AWE tools focus on lower-order skills related to remembering and understanding linguistic mechanics.
“Notably, LLMs support higher-order cognitive functions aligned with analysis, evaluation, and creation in Bloom’s Taxonomy, whereas AWE tools focus on lower-order skills related to remembering and understanding linguistic mechanics (Evangelista, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
The Introduction says advanced generative AI models can perform text analysis and demonstrate creativity in writing and problem solving; the Discussion also connects Gen AI use with higher-order thinking. Our copy does not make the claimed comparison with AWE tools or assign either type of tool to Bloom’s Taxonomy levels (searched: AWE, Bloom’s Taxonomy). (LLM, conf=0.82)
Experimental — accuracy not yet measuredReal-time oral defenses ensure authentic understanding by requiring extemporaneous argument articulation.
“Real-time oral defenses ensure authentic understanding by requiring extemporaneous argument articulation (Evangelista, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
The AI-Resistant Assessment section includes oral evaluations among authentic, process-driven tasks intended to show students’ engagement with the material. It does not specify real-time oral defenses or extemporaneous argument articulation, or establish that they ensure authentic understanding. No page number was provided for the passage. (LLM, conf=0.84)
Experimental — accuracy not yet measuredUnsupervised traditional assessments in AI-infused environments face challenges in accurately measuring student competencies, leading to concerns about the validity of current assessment models.
“Furthermore, unsupervised traditional assessments in AI-infused environments face challenges in accurately measuring student competencies, leading to concerns about the validity of current assessment models [ 100].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [100]
Section 4.2.1 (page unavailable) reports that faculty struggle to distinguish AI-assisted work from students’ authentic contributions, supporting the concern that assessments may not reflect student competencies. The paper does not specifically identify unsupervised traditional assessments as the setting or explicitly assess the validity of current assessment models. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe digital divide further widens inequalities in education, reinforcing socioeconomic disparities that affect student performance and opportunities, especially in rural and marginalised communities.
“The digital divide further widens inequalities in education, reinforcing socioeconomic disparities that affect student performance and opportunities, especially in rural and mar -ginalised communities [ 100].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [100]
Section 4.2.3 (page unavailable) says unequal access to AI tools and reliable internet makes fair assessment difficult, and the Discussion identifies disparities in digital infrastructure as an equity concern. The paper also says AI-supported systems may amplify economic and social gaps, but it does not establish effects on student performance and opportunities or identify rural and marginalised communities as especially affected. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Cross-stakeholder consensus supports AI functioning as a near-peer rather than replacing human effort, favoring process-oriented assessment.
“Despite divergent perspectives, cross-stakeholder consensus supports AI functioning as a “near-peer” rather than replacing human effort, favoring process-oriented assessment (Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Section 4.1.5 says faculty viewed AI as a support for brainstorming and drafting, not a replacement for students’ critical thinking or independent learning. Section 4.4.1 describes assessing students’ engagement with AI and the process behind their work. But the study gathered faculty perspectives (Sections 3.2–3.3); it does not establish cross-stakeholder consensus. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGeographic inequities in AI integration are pronounced, with academic discourse concentrated in Global North contexts and resource-constrained regions like Palestine and Somalia overlooked.
“Geographic inequities are pronounced: while AI tools have achieved global reach, academic discourse remains concentrated in Global North contexts, overlooking challenges in resource-constrained regions like Palestine and Somalia where political and economic limitations impede infrastructure access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Section 1.1 (Research Gap; page not provided) says discourse on generative AI in education is concentrated in Global North contexts and overlooks settings with economic and political limitations, including Palestine. Section 4.2.3 (Equity and Accessibility Concerns; page not provided) reports unequal access to AI tools and digital infrastructure. The paper does not identify Somalia or establish that political and economic limitations cause the reported infrastructure-access disparity. (LLM, conf=0.88)
Experimental — accuracy not yet measuredEven in high-resource settings, a digital divide persists as disadvantaged students lack reliable internet or financial means to access premium features.
“Even in high-resource settings, a “digital divide” persists as disadvantaged students lack reliable internet or financial means to access premium features (Khlaif et al., 2025; Peterson, 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Section 4.2.3, “Equity and Accessibility Concerns” (page not available), reports unequal access to AI tools and digital infrastructure, including students without reliable internet. It does not establish that this persists in high-resource settings or that students lack the financial means to access premium features; Section 1.1 instead locates the study in Palestine. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIntegrated oral defense (vivas) has high resistance to AI-generated shortcuts and ensures authentic understanding.
“High resistance to AI-generated shortcuts; ensures authentic understanding (Evangelista, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
The AI-Resistant Assessment section includes oral evaluations among authentic, process-driven tasks and says AI-resistant assessments reduce reliance on generative AI. It does not specifically evaluate integrated oral defenses or establish that they have high resistance to AI-generated shortcuts or ensure authentic understanding; those are stronger claims than the passage supports. No page number was provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredStudents in under-resourced contexts like Palestine and Somalia face connectivity limitations and political constraints that fundamentally impede access.
“Students in under-resourced contexts like Palestine and Somalia face connectivity limitations and political constraints that fundamentally impede access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
The Research Gap section describes economic and political limitations in Palestine, and §4.2.3 reports unequal access to AI tools and digital infrastructure, including unreliable Internet. The paper does not address Somalia, and it describes challenges to equitable assessment rather than establishing that these constraints fundamentally impede access. (LLM, conf=0.87)
Experimental — accuracy not yet measuredProject-based learning engaging real-world problems promotes critical thinking beyond AI's current capabilities.
“Project-based learning engaging real-world problems promotes critical thinking beyond AI’s current capabilities (Evangelista, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Many institutions lack clear guidelines on AI use in assessments, forcing educators to navigate AI-related challenges without structured policies.
“A significant concern is that many institutions lack clear guidelines on AI use in assessments, forcing educators to navigate AI-related challenges without structured policies [ 100 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [100]
Promoting critical engagement with AI-generated content fosters an educational environment where AI functions as a learning partner rather than a replacement for human cognition.
“This approach fosters an educational environment where AI functions as a learning partner rather than a replacement for human cognition [ 58, 100 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [100]
The AAAE framework from Palestine provides task-based categorization ranging from prohibited use in traditional exams to full human-AI partnership in creative problem-solving.
“The “Against, Avoid, Adopt, and Explore” (AAAE) framework from Palestine provides task-based categorization ranging from prohibited use in traditional exams to full human-AI partnership in creative problem-solving (Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Geographic diversity across Global North and South contexts strengthens external validity.
“Geographic diversity across Global North and South contexts strengthens external validity (Hanh and Duyen, 2025; Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Khlaif et al., 2025
Section 3.7 (Trustworthiness; page unavailable) links purposive sampling to transferability across similar contexts, but does not link geographic diversity to external validity. Searches for Global North/South contexts, geographic locations, and generalizability found no passage making the attributed point. (LLM, conf=0.75)
Experimental — accuracy not yet measuredCountries that invest in AI-driven medical education report higher student engagement, improved diagnostic capabilities, and shifts in career perspectives as AI competencies become increasingly essential in modern healthcare roles.
“In comparison, countries that invest in AI-driven medical education report higher student engagement, improved diagnostic capabilities, and shifts in career perspectives as AI competencies become increasingly essential in modern healthcare roles [16].”
From Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities (2025) · cites this paper as [16]
Medical students in South Korea and Germany report that AI enhances diagnostic efficiency, reduces physician workload, and improves patient outcomes without diminishing the human role in healthcare.
“For example, medical students in South Korea and Germany report that AI enhances diagnostic efficiency, reduces physician workload, and improves patient outcomes without diminishing the human role in healthcare [16].”
From Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities (2025) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Recorded interview time is shorter than the stated minimum
MinorThe study reports 25 interviews lasting at least 30 minutes each, which requires at least 12.5 hours of interviews. It reports only 11.65 hours of recorded interview data, although the interviews are described as audio-recorded.
“Individual semi-structured interviews were conducted with 25 participants.Each interview lasted 30 to 45 min and was audio-recorded for accuracy.”
“encompassing 11.65 h of recorded interview data and 3.75 h of recorded focus group discussions.”
The framework's third principle is called both Adapt and Adopt
MinorThe stated list of four principles names the third principle "adapt," while the framework's name and its dedicated section call it "Adopt."
“They identified four key principles, namely against, avoid, adapt, and explore, that participants reported during discussion sessions and interviews for redesigning assessments.”
“The Against, Avoid, Adopt, and Explore (AAAE) framework is accompanied by a comprehensive description of each principle and the types of assessments suitable for each category.”
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 3× · see citing works
“Future research could expand on these themes by investigating the long-term effects of AI-enhanced assessments on student learning and employability.”
Validity and generalization in future case study evaluations
Yin, R. K. (2013) · Evaluation · doi:10.1177/1356389013497081 · Cited by 1,435 · see citing works
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 1,409 · cited 2× · see citing works
“Future research could expand on these themes by investigating the long-term effects of AI-enhanced assessments on student learning and employability.”
Combining individual interviews and focus groups to enhance data richness
Lambert, S. D., & Loiselle, C. G. (2008) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2007.04559.x · Cited by 1,318 · see citing works
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181 · cited 1× · see citing works
“Gen AI technologies offer countless contributions to various elements of education, most notably in assessment (Zhang & Aslan, 2021; Chiu et al., 2023).”
Managing the Strategic Transformation of Higher Education through Artificial Intelligence
GEORGE, B. P., & Wooden, O. S. (2023) · Administrative Sciences · doi:10.3390/admsci13090196 · Cited by 718 · see citing works
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024) · Information · doi:10.3390/info15100596 · Cited by 620 · see citing works
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100348 · Cited by 452 · see citing works
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2024) · arXiv · doi:10.48550/arxiv.2405.11800 · Cited by 447 · see citing works
Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice
Farrelly, T., & Baker, N. (2023) · Education Sciences · doi:10.3390/educsci13111109 · Cited by 414 · cited 1× · see citing works
“Farrelly and Baker (2023) identified several factors enhancing graduate program instructors’ motivation to adopt AI platforms in education.”
Assessment in the age of artificial intelligence
Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100075 · Cited by 409 · cited 4× · see citing works
“Future research could expand on these themes by investigating the long-term effects of AI-enhanced assessments on student learning and employability.”
InfluentialDimitriadou, E., & Lanitis, A. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00231-3 · Cited by 370 · see citing works
Luo (Jess), J. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2309963 · Cited by 366 · cited 3× · see citing works
“Future research could expand on these themes by investigating the long-term effects of AI-enhanced assessments on student learning and employability.”
International Handbook of the Learning Sciences
Bodemer, D., Janssen, J. J. H. M., Schnaubert, L., Fischer, F., Hmelo‐Silver, C. E., Goldman, S. R., Reimann, P., van Gog, L., & Interaction, E. A. L. D. I. (2018) · Routledge eBooks · doi:10.4324/9781315617572 · Cited by 364 · see citing works
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., et al. (2023) · Zenodo · doi:10.5281/zenodo.7636568 · Cited by 350 · see citing works
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., et al. (2023) · Dublin City University Open Access Institutional Repository (Dublin City University) · doi:10.5281/zenodo.7636567 · Cited by 350 · see citing works
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023) · Sustainability · doi:10.3390/su151511524 · Cited by 340 · see citing works
Grassini, S. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1191628 · Cited by 330 · see citing works
An Overview of Qualitative Research and Focus Group Discussion
Akyıldız, S. T., & Ahmed, K. H. (2021) · International Journal of Academic Research in Education · doi:10.17985/ijare.866762 · Cited by 264 · see citing works
Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024) · System · doi:10.1016/j.system.2024.103399 · Cited by 228 · see citing works
Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023) · Societies · doi:10.3390/soc13050118 · Cited by 203 · see citing works
Importance of Focus Groups in Qualitative Research
Gundumogula, M. (2020) · The International Journal of Humanities & Social Studies · doi:10.24940/theijhss/2020/v8/i11/hs2011-082 · Cited by 197 · see citing works
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024) · Education Sciences · doi:10.3390/educsci14101090 · Cited by 177 · see citing works
Self-citationGenerative AI in Education: Pedagogical, Theoretical, and Methodological Perspectives
Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S. K. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.845 · Cited by 165 · see citing works
Shahriar, S., Lund, B. D., Mannuru, N. R., Arshad, M., Hayawi, K., Bevara, R. V. K., Mannuru, A., & Batool, L. (2024) · Applied Sciences · doi:10.3390/app14177782 · Cited by 160 · see citing works
Martínez-Comesaña, M., Rigueira, X., Larrañaga, A., Martínez, J. M., Ocarranza-Prado, I., & Kreibel, D. (2023) · Revista de Psicodidáctica (English ed ) · doi:10.1016/j.psicoe.2023.06.002 · Cited by 125 · see citing works
Al-Zahrani, A. M. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2271445 · Cited by 120 · see citing works
Using technology to facilitate effective assessment for learning and feedback in higher education
Deeley, S. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1356906 · Cited by 109 · see citing works
The assessment challenge of social and collaborative learning in higher education
Boud, D. J., & Bearman, M. (2022) · Educational Philosophy and Theory · doi:10.1080/00131857.2022.2114346 · Cited by 87 · see citing works
Lye, C. Y., & Lim, L. (2024) · Education Sciences · doi:10.3390/educsci14060569 · Cited by 83 · see citing works
INSTITUTIONAL POLICIES ON ARTIFICIAL INTELLIGENCE IN UNIVERSITY LEARNING, TEACHING AND RESEARCH
Spivakovsky, O. V., Omelchuk, S. A., Kobets, V. V., Valko, N. V., & Malchykova, D. S. (2023) · Information Technologies and Learning Tools · doi:10.33407/itlt.v97i5.5395 · Cited by 73 · see citing works
Almenara, J. C., Palacios‐Rodríguez, A., Aguirre, M. I. L., & de Rivas-Manzano, M. D. R. (2024) · Education Sciences · doi:10.3390/educsci14070740 · Cited by 70 · see citing works
AI-resistant assessments in higher education: Practical insights from faculty training workshops
Awadallah Alkouk, W., & Khlaif, Z. N. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1499495 · Cited by 68 · see citing works
Self-citationDeveloping valid assessments in the era of generative artificial intelligence
Kaldaras, L., Akaeze, H., & Reckase, M. D. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1399377 · Cited by 59 · see citing works
Using interview excerpts to facilitate focus group discussion
Geampana, A., & Perrotta, M. (2024) · Qualitative Research · doi:10.1177/14687941241234283 · Cited by 55 · see citing works
Challenges in using authentic assessment in 21st century ESL classrooms
Aziz, M. N. A., Yusoff, N. M., & Yaakob, M. F. M. (2020) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v9i3.20546 · Cited by 54 · see citing works
Cabellos, B., de Aldama, C., & Pozo, J. I. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1468900 · Cited by 53 · see citing works
Sembey, R., Hoda, R., & Grundy, J. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.111988 · Cited by 49 · see citing works
Towards a framework for designing and evaluating online assessments in business education
Huber, E., Harris, L. M., Wright, S., White, A. W., Raduescu, C., Zeivots, S., Cram, A., & Brodzeli, A. (2023) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2023.2183487 · Cited by 48 · see citing works
Analysis of students’ ideas and conceptual artifacts in knowledge‐building discourse
Oshima, J., Oshima, R., & Saruwatari, S. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12961 · Cited by 39 · see citing works
Dematerialized participation challenges: Methods and practices for online focus groups
Poliandri, D., Perazzolo, M., Pillera, G. C., & Giampietro, L. (2023) · Frontiers in Sociology · doi:10.3389/fsoc.2023.1145264 · Cited by 32 · see citing works
O Donnell, F., Porter, M., & Fitzgerald, D. S. (2024) · Irish Journal of Technology Enhanced Learning · doi:10.22554/szwjfy54 · Cited by 23 · see citing works
Abubakar, U., Falade, A. A., & Ibrahim, H. A. (2024) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2024.02.009 · Cited by 19 · see citing works
Adapting to AI: Reimagining the Role of Assessment Professionals
Miller, W. T. (2024) · Intersection A Journal at the Intersection of Assessment and Learning · doi:10.61669/001c.121439 · Cited by 18 · see citing works
The Importance Of Assessment In The Educational Process - Science Teachers’ Perspective
Stăncescu, I. (2017) · The European Proceedings of Social & Behavioural Sciences · doi:10.15405/epsbs.2017.07.03.89 · Cited by 15 · see citing works
AIAS: AI-ASsisted cybersecurity platform to defend against adversarial AI attacks
Petihakis, G., Farao, A., Bountakas, P., Sabazioti, A., Polley, J., & Xenakis, C. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3669920 · Cited by 13 · see citing works
Shivshankar, S., & Acharya, N. K. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7220-3.ch006 · Cited by 11 · see citing works
TRADITIONAL ASSESSMENT OF LEARNING VERSUS ONLINE ASSESSMENT
Cazan, A., & Indreica, E.-S. (2014) · eLearning and Software for Education · doi:10.12753/2066-026x-14-154 · Cited by 9 · see citing works
Adaptive AI-Driven Assessment for Competency-Based Learning Scenarios
Waladi, C., & Sefian, M. L. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3128-6.ch010 · Cited by 8 · see citing works
Nadeem, M., Farag, W., & Helal, M. (2024) · Mediterranean Smart Cities Conference (MSCC) · doi:10.1109/mscc62288.2024.10697084 · Cited by 8 · see citing works
Combining semi-structured interviews and document analysis in a study of private security expertise
Berndtsson, J. (2017) · Routledge eBooks · doi:10.4324/9781315669830-7 · Cited by 6 · see citing works
AI and Personalised Grading Criteria
Ahmed, S., Zaki, A., & Bentley, Y. N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2145-4.ch004 · Cited by 6 · see citing works
The False Sense of Achievement
Kostanek, E., & Li, F. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-6351-5.ch002 · Cited by 6 · see citing works
ChatGPT as an intelligent self-Continuous Professional Development tool for teachers
Fakhar, H., Mohammed, L., Echantoufi, N., Ouadrhiri, K., Khalid, E., & Ajana, L. (2024) · Statistics Optimization & Information Computing · doi:10.19139/soic-2310-5070-2145 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Baumberger, K. R. L., and George, T. M. (2020). The (in)visibility of AI in educational assessments: Examining the implications of datafication for educational practices. Digital transformations in education: The role of AI in educational innovation, Springer.
- Donaghy, K. (2024, October 07). Multimodality and multimodal literacy: What are they and why are they important in ELT?, Available online: https://teachingenglishwithoxford.oup.com/2023/11/16/multimodality-and-multimodal-literacy-elt/#:~:text=Multimodal%20literacy%20refers%20to%20the,curricula%20to%20include%20mult.
- Umar, 2024, Perceived entrepreneurial orientation and perceived academic entrepreneurial intention: A mediating role of knowledge creation, Policy Research Journal, 2, 953
- Williamson, 2020, Historical Threads, Digital Transitions, and the Implications of AI for Learning, Education, and the Field of the Learning Sciences, British Journal of Educational Technology, 51, 1117
- Zhang, 2021, AI technologies for education: Recent research & future directions, Computers and Education: Artificial Intelligence, 2, 100025
Cited by
Sorted by citations
AILA Applied Linguistics Series
(2013) · AILA applied linguistics series · doi:10.1075/aals · Cited by 115 · see citing works
A Framework for Generative AI-Driven Assessment in Higher Education
Ilieva, G., Yankova, T., Ruseva, M., & Kabaivanov, S. I. (2025) · Information · doi:10.3390/info16060472 · Cited by 106 · see citing works
Adamakis, M., & Rachiotis, T. (2025) · Encyclopedia · doi:10.3390/encyclopedia5040180 · Cited by 76 · see citing works
Alhur, A., Khlaif, Z. N., Hamamra, B. T., & Hussein, E. T. (2025) · JMIR Medical Education · doi:10.2196/74947 · Cited by 72 · see citing works
Self-citationAdvancing higher education with GenAI: factors influencing educator AI literacy
Ayyoub, A. M., Khlaif, Z. N., Shamali, M., Abu Eideh, B., Assali, A., Hattab, M. K., Barham, K. A., & Bsharat, T. R. K. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1530721 · Cited by 44 · see citing works
Self-citationSalama, N., Bsharat, R., Alwawi, A., & Khlaif, Z. N. (2025) · BMC Nursing · doi:10.1186/s12912-025-02913-4 · Cited by 42 · see citing works
Self-citationHamamra, B. T., Khlaif, Z. N., Mayaleh, A., & Baker, A. A. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2526435 · Cited by 43 · see citing works
Self-citationUsing Generative AI in nursing education: Students’ perceptions
Khlaif, Z. N., Salameh, N., Ajouz, M., Mousa, A., Itmazi, J. A., Alwawi, A. A., & Alkaissi, A. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07416-z · Cited by 48 · see citing works
Self-citationNcube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31 · see citing works
Nursing Students’ Attitudes Toward Artificial Intelligence: Palestinian Perspectives
Salameh, B. S., Qaddumi, J. A. S., Hammad, B. M., Eqtit, F., Ayed, A., Fashafsheh, I., ALBashtawy, M., Reshia, F. A. A., & Lukić, I. K. (2025) · SAGE Open Nursing · doi:10.1177/23779608251343297 · Cited by 31 · see citing works
Elshall, A. S., & Badir, A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1596462 · Cited by 28 · see citing works
Students’ Trust in AI and Their Verification Strategies: A Case Study at Camilo José Cela University
Martín-Moncunill, D., & Martínez, D. A. (2025) · Education Sciences · doi:10.3390/educsci15101307 · Cited by 23 · see citing works
Kangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22 · see citing works
Utilizing artificial intelligence for assessment in higher education
Mpolomoka, D. L. (2025) · Pedagogical Research · doi:10.29333/pr/16677 · Cited by 20 · see citing works
Barrot, J. S. (2025) · Assessing Writing · doi:10.1016/j.asw.2025.100990 · Cited by 28 · see citing works
Khlaif, Z. N., Alshakhshir, R., Hamamra, B. T., & Joma, A. I. (2025) · British Journal of Visual Impairment · doi:10.1177/02646196251382469 · Cited by 18 · see citing works
Self-citationLabib, W. A., Abdelsattar, A., Abowardah, E. S., Abdelalim, M. F., & Mahmoud, H. (2025) · Sustainability · doi:10.3390/su172210029 · Cited by 16 · see citing works
Farran, H. K., Khlaif, Z. N., Saifi, A.-G., Hijjawi, T. N., & Ismail, H. B. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2594885 · Cited by 14 · see citing works
Self-citationHenadirage, A., Chathuranga, B. T. K., & Gunarathne, N. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00578-w · Cited by 9 · see citing works
Shi, Y., Cui, H., Zhang, Y., Hui, X., Li, G., Ouyang, M., & Pan, L. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1735410 · Cited by 14 · see citing works
Abudalfa, S. I., & Barrot, J. S. (2026) · Assessing Writing · doi:10.1016/j.asw.2026.101041 · Cited by 12 · see citing works
Xu, X., & Thien, L. M. (2026) · European Journal of Education · doi:10.1111/ejed.70520 · Cited by 10 · see citing works
Yoon, Y. S., Baek, W., Jo, H., Hong, C., & Ji, Y. (2026) · Nurse Education in Practice · doi:10.1016/j.nepr.2026.104775 · Cited by 5 · see citing works
AI Paradox in Higher Education: Understanding Over-Reliance, Its Impact, and Sustainable Integration
Khlaif, Z. N., Hamamra, B., & Hussein, E. T. (2025) · doi:10.21203/rs.3.rs-6127885/v1 · Cited by 5 · see citing works
Self-citationAi-RACE as a Framework for Writing Assignment Design in Higher Education
El-Soussi, A., & Yousef, D. (2026) · Education Sciences · doi:10.3390/educsci16010119 · Cited by 4 · see citing works
Lai, C. Y. (2026) · Journal of Hospitality Leisure Sport & Tourism Education · doi:10.1016/j.jhlste.2026.100602 · Cited by 3 · see citing works
Cournoyea, M., & Seeley, S. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2656286 · Cited by 2 · see citing works
Digital empowerment of undergraduate students with vision disability
Shokhedim, S. S. A., Khlaif, Z. N., Hattab, M. K., & Itmazi, J. A. (2025) · British Journal of Visual Impairment · doi:10.1177/02646196251330161 · Cited by 2 · see citing works
Self-citationAI-Augmented Legacy Modernization: Transforming Enterprise Systems with Smart Automation
Annela, L. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.5.16 · Cited by 1 · see citing works
Medical Education Learning Specialists in the Age of Artificial Intelligence
Hu, H. H., Wallace, D. D., & Boateng, B. A. (2025) · Cureus · doi:10.7759/cureus.88439 · Cited by 1 · see citing works
Assessment in Higher Education in the Era of Generative AI
Eren, S. U. N. A., & Özer, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7579-3.ch008 · Cited by 1 · see citing works
A Framework for Responsible AI in Higher Education
Bilgin, H. (2025) · Higher Education Governance and Policy · doi:10.55993/hegp.1788343 · Cited by 2 · see citing works
Putra, S., Rohman, A., & Almanda, R. L. (2025) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss66954.2025.11389727 · Cited by 1 · see citing works
Ugon, J., Pierce, C., Cain, A., & Laird, B. (2026) · IFAC Symposium on Advances in Control Education · doi:10.1145/3786228.3786244 · Cited by 1 · see citing works
Nyagoga, L. M., Malik, L., Lukoko, H. O., & Liu, M. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0029 · Cited by 1 · see citing works
TRANSFORMATION OF LEARNING TASKS USING GENERATIVE AI
Kovalenko, I. (2026) · OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY · doi:10.28925/2414-0325.2026.204 · Cited by 2 · see citing works
Isaeva, R., CANER, H. N., Caner, M., Giray, L., & Karadağ, E. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100606 · Cited by 6 · see citing works
Elshall, A. S., Badir, A. M., & Gebremedhin, M. A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1834090 · Cited by 1 · see citing works
Gonsalves, C., Vuren, S. V., & Gill-Simmen, L. (2026) · Journal of Marketing Education · doi:10.1177/02734753261454375 · Cited by 1 · see citing works
Grading in an age of assessment reform: the elephant in the room
Nieminen, J. H., Bearman, M., Boud, D. J., Corbin, T., Dawson, P., Tai, J., & Walton, J. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2694451 · Cited by 1 · see citing works
Tayebipour, F., Siddique, M., & Hamid, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1864395 · Cited by 1 · see citing works
Beyond AI Detection: A Decision-Making Guide for Strategic Assessment Design in Nursing Education
McCusker, K., Kennedy, B. B., & Parish, A. L. (2026) · Nurse Educator · doi:10.1097/nne.0000000000002281 · Cited by 1 · see citing works
Cobblah, M.-A., Tachie‐Donkor, G., Atuase, D., Danso, T. K. O., Sarfo, J. O., & Somuah, B. A. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103597 · Cited by 1 · see citing works
Abdellatif, M. S., Alshehri, M. A., Lamouchi, A., Rahmath, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1809174
“Process-oriented assessment shifts focus from final products to iterative drafts, prompt engineering logs, and reflective exercises documenting cognitive journeys (Khlaif et al., 2025; Talandis and Muller, 2025; Wang, 2024).”
InfluentialThe Role of AI in Fostering Youth-Adult Collaboration for Digital Literacy
Arshad, M. M., Ismail, I. A., Abdullah, H. B., Nawi, N. R. C., Krauss, S. E., Aziz, M. F. A., D’Silva, J. L., Dahalan, D., & Shaffril, H. A. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2170-7.ch012
Javed, S., Javed, T., Draissi, Z., Rehman, N., Maqbool, S., & Sarwar, U. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4217-7.ch007
Kituku, B., Araka, E., & Maina, E. M. (2025) · International Conference on Information and Communication Technology for Development for Africa (ICT4DA) · doi:10.1109/ict4da67218.2025.11282535
Redefining the Role of Educators in GenAI-Supported Classrooms
MERZİFONLUOĞLU, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1195-1.ch010 · Cited by 1 · see citing works
Transforming Teaching and Learning in Business Education
Berraies, S., Rejeb, W. B., & Abidine, S. Z. E. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4377-8.ch008
Jayatilake, S., Rosunally, Y. Z., & Ky, N. T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5403-3.ch002
AI and the Reconfiguration of Professional Identity
Kızıl, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7416-1.ch002
Student Perceptions of AI-Assisted Institutional Emails
Huynh, M. K., Tran-Pham, T. A., & Nguyễn, H. K. (2026) · Business and Professional Communication Quarterly · doi:10.1177/23294906261418213
More than words: a multimodal analysis portfolio for the digital era
El-Soussi, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1778081
Rethinking Student Assessment in the Age of AI
Elsawah, W. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0328-2.ch008
Zhang, J., Yao, Z., Kang, D., Rasiah, R., & Gao, N. (2026) · BMC Psychology · doi:10.1186/s40359-026-04246-6 · Cited by 1 · see citing works
From Reluctance to Engagement: Aligning Institutional Policy with “Human-in-the-Loop” Pedagogy
Makarevitch, I., Kostihová, M., Hilk, C. L., & Gumiela, J. (2026) · Trends in Higher Education · doi:10.3390/higheredu5020030
Lo, D., Chon, L., & Chen, J. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3638-1.ch011
Balancing Innovation and Integrity
Uden, L., Ching, G. S., & Francis, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407452
Minutes, not months: A practical guide for faculty to leverage GenAI in business education
Geoghegan, W., & Gonzalez-Mule, E. N. (2026) · Business Horizons · doi:10.1016/j.bushor.2026.04.006
Academic Integrity in the Age of Artificial Intelligence
Salendab, F. A., Baraguir, R. S., Cabato, C. J., & Lumogdang, J. C. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7097-2.ch012
Yang, Z., Chen, G. P., Chotia, V., Malik, P., Quacquarelli, B., & Cillo, V. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-03-2026-0471
Integrating GenAI into HCI Pedagogy: Supporting Students’ Learning of Evaluation Methods
Aziz, A., Kharrufa, A., & Johnson, I. G. (2026) · Annual Symposium on HCI Education (EduCHI) · doi:10.1145/3803869.3803872
Maurat, J. I. C., Labanan, R. M., & Isip, E. V. (2025) · International Conference on Education and E-Learning (ICEEL) · doi:10.1145/3789859.3789870
Prompt Engineering: Fine-Tuning Strategies to Elevate GenAI Interaction
Sinnappan, G. S., Qomariyah, N. N., Yookwan, W., & Pugoy, R. A. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-13119-5_68
Khlaif, Z. N., Hamamra, B., & Farran, H. (2026) · Research Square · doi:10.21203/rs.3.rs-9192785/v1
Self-citationStudents' conceptions about AI in science classrooms
Conradty, C., Sotiriou, S. A., Koulentianos, D., & Bogner, F. X. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1810140
An Integrated OCR-LLM-Graph Framework for Intelligent Educational Question Generation
Sangeetha Archana, J., Jayashrinidhi, V., Fazil, S., & Arunachalam, T. (2026) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei68323.2026.11542204
Tashmeer, M., Davies, M. J., & Stephens, J. M. (2026) · British Educational Research Journal · doi:10.1002/berj.70208
Kic-Drgas, J., Rahmani, A., & Kılıçkaya, F. (2026) · SAGE Open · doi:10.1177/21582440261454817
Safeguarding students' academic integrity in the age of generative AI: challenges and solutions
Alyafei, R. A. S. M., Al-kfairy, M., Alfandi, O., & Alrabaee, S. (2026) · Kybernetes · doi:10.1108/k-04-2025-0878 · Cited by 1 · see citing works
Matobobo, C., Dzvapatsva, G. P., Ncube, P. D. N., & Chinhamo, E. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574202
Wilson, J., Fennell, K. L., & Prior, S. J. (2026) · Trends in Higher Education · doi:10.3390/higheredu5030058
Masot, O., Alzuria‐Alós, R. M., Roca, J., Torné‐Ruiz, A., Bonet, A., Selva‐Pareja, L., Gimeno, E. B., & Pueyo, E. P. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107263
Governing AI in Education: A Cross-Organization Analysis of International Policy, Law, and Standards
Chumachenko, D., Fitz-Gerald, A., Аrtyukhov, А., & Lyeonov, S. (2026) · International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI) · doi:10.55056/ceur-ws.org/vol-4164/paper26.pdf
Teachers’ Perceptions of AI-Based Evaluation and a Suggested Questionnaire
Thabet, E. A., Hamdan, R., & Daher, W. M. (2026) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v20i13.61147
Teacher Agency in the Age of AI
Monib, W. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7749-0.ch002
Huot, S., Kun, S., Shah, K., Hok, C., Loch, S., Phin, K., & Chheang, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7675-2.ch004
Rethinking assessment in the age of AI: Towards authentic and AI-resilient practices
Vaghjee, G., & Vaghjee, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36775-5.00008-4
A theoretical model for the ethical use of generative AI tools in higher education assessments
Baig, M. I., & Yadegaridehkordi, E. (2026) · International Journal of Ethics and Systems · doi:10.1108/ijoes-09-2025-0538 · Cited by 1 · see citing works
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.ni
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.si
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.lof
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.lot
Charting the future of inclusive language learning
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c07
Technology-enhanced learning and immersive virtual reality environments
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c03
Staggini, G. (2026) · AILA applied linguistics series · doi:10.1075/aals.23.intro
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.prelim
Understanding dyslexia in the context of higher education
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c01
The potential of VR environments for students with SpLDs’ language learning process
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c04
A guide to inclusive and accessible language e-learning environments
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c05
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.toc
Virtual Reality in Inclusive Language Learning
Staggini, G. (2026) · AILA applied linguistics series · doi:10.1075/aals.23
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.conclusion
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.refs
Designing immersive virtual reality environments for inclusive language teaching
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.c06
(2026) · AILA applied linguistics series · doi:10.1075/aals.23.ded
GenEdu: Personalized Learning with Retrieval-Augmented Generative AI and Dual-Layer Evaluation
Frigui, R., & Rezgui, K. (2026) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit70676.2026.11631071
Beyond Replacement: How AI Reconfigures Academic Roles
Ghorbani, A., & Blankesteijn, M. (2026) · Foresight and STI Governance · doi:10.17323/fstig.2026.34197
Dumbrăveanu, R., Grosseck, G., Maliţa, L., & BALMUȘ, O. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631500
- Authors
- Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B.
- Affiliations
- An-Najah National University (PS)
- Birzeit University (PS)
- Arab American University (PS)
- Faculty of Humanities and Educational Sciences, An Najah National University, Nablus P4110257, Palestine
- Corresponding author
- Khlaif, Zuheir N.
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2025-02-02
- Volume
- 15
- Issue
- 2
- Pages
- 174
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4407106354
- Semantic Scholar
- dd46d4f6b9f3798bc9db77b4e89714fba2a4eee7
- Cited by
- 127
- References
- 54
- Citations by year
- 2026: 98 · 2025: 28 · 2013: 1
- FWCI
- 130.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI in education, assessment redesign, student assessment, faculty motivation, academic integrity, professional development for faculty, equity and accessibility, institutional policy, career preparation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
