A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment

In the paper

“In this light, understanding the benefits that AI brings to HE curriculum, instruction, and assessment could help academics make full use of the technology to reduce workloads (Holmes et al., 2023;Pereira et al., 2023) and improve productivity.”

What the sources say

• Disputed

  The Teacher Perspective supports training academics to understand AI, but says there is currently little evidence that AI saves teachers’ time. The Conclusion calls claims that AI saves teachers’ time “questionable and unsubstantiated.” The paper therefore does not support the cited workload-reduction benefit; it does not establish improved productivity either. (LLM, conf=0.84)

  Checked by a reasoning model — may be wrong.

  cites Holmes et al., 2023 sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites Pereira et al., 2023 source

In the paper

“AI is found to accurately assess students' learning process and outcomes, and further determine teaching effect (Novais et al., 2023;Saad and Tounkara, 2023;Wang et al., 2025;Zhu et al., 2023).”

What the sources say

• Partly supported

  The Discussion (page unavailable) says the fuzzy assessment system adequately described students’ soft-skills profiles using self- and peer-assessments, and the Feedback Reports and FSSA Validation section says it generated reports for 19 students. This supports a narrower claim about assessing soft skills in active-learning sessions. The paper does not establish accurate assessment of learning processes and outcomes generally or show that the system determined teaching effectiveness; its validation was limited to expert analysis. (LLM, conf=0.86)

  cites Novais et al., 2023 sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites Saad and Tounkara, 2023 source
• ?Not checked — no full text of the sourcecites Wang et al., 2025 source

In the paper

“Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction.”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Al-Shanfari et al., 2023 sourceExperimental — accuracy not yet measured
• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Firat, 2023 sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites Kohnke et al., 2023 source

In the paper

“Additionally, Zhang et al. (2023) designed and experimented with an intelligent classroom for English language and literature courses in China, and found that this AI tool provided the experimental group with a good learning environment and enhanced students' language proficiency.”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Zhang et al. (2023) sourceExperimental — accuracy not yet measured

In the paper

“Using experiments with small-samples, Zhu et al. (2023) developed in China an AI tool to predict students' performance based on their classroom behavior and previous performance.They suggested that this tool could be used to adjust instructors' teaching strategies and improve teaching quality.”

What the sources say

• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Zhu et al. (2023) source

In the paper

“For instance, Wang (2023) stated that AI could make teaching content visualizable; that is, students could practice key communication competencies in a virtual community of practice, which improves teaching efficiency.”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Wang (2023) sourceExperimental — accuracy not yet measured

In the paper

“For instance, Archibald et al. (2023) showed that an AI-enabled discussion platform accurately calculated students' curiosity scores to present their engagement in discussion, further reducing teachers' assessment workload and facilitating their intervention based on the quality of posts written by students.”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Archibald et al. (2023) sourceExperimental — accuracy not yet measured

In the paper

“Both studies conducted focus group interviews, and revealed that AI, at curriculum levels, could provide instructors and students with new, rich, and personalized materials, contributing to curriculum design and development and facilitation of course preparation.”

What the sources say

• ?Not checked — no full text of the sourcecites Chen et al., 2023 source
• Partly supported

  The Literature Review says digital technology can customize learning modes, and the Results describe a course concept using digital educational technologies. But neither passage says AI provides new, rich, personalized materials or facilitates course preparation. The Methods describe an online conference and surveys, not focus-group interviews; the paper's development of a course concept does not establish that AI facilitated its design. (LLM, conf=0.79)

  cites Wang, 2023 sourceExperimental — accuracy not yet measured

In the paper

“AI could work as a virtual curriculum assistant that helps address students' time-consuming and repetitive questions about curriculum (e.g., content, time, deadline), reduce instructors' logistical workloads and give them more time to improve teaching quality and support students' development (Sajja et al., 2023).”

What the sources say

• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Sajja et al., 2023 source

In the paper

“For instance, Novais et al. ( 2023) designed an evaluation fuzzy expert system and employed it to build profiles of students' soft skills (e.g., communication and innovation skills, management skills, and social skills).”

What the sources say

• Supported

  The Introduction describes the authors’ Fuzzy Soft Skills Assessment expert system as a tool that produces individual student profiles. The Conclusion reports that the system described students’ soft-skills profiles, and the Introduction identifies management, communication, innovation, and social skills as soft skills. (LLM, conf=0.91)

  cites Novais et al. (2023) source

In the paper

“A new assessment method driven by AI tools (i.e., a backward propagation neural network) could automatically evaluate teaching, learning, and grading in an experiential online course in agriculture (Kumar et al., 2023).”

What the sources say

• Supported

  The Conclusions state that the study uses a backpropagation neural network to automate teaching, learning, and grading procedures for AI-assisted experiential agricultural education. The same section identifies the proposed model as an online practical agriculture course. (LLM, conf=0.94)

  cites Kumar et al., 2023 source

In the paper

“For example, Sajja et al. (2023) used the syllabus and other teaching materials to design a curriculum-oriented intelligent assistant and found that this virtualTA effectively provided accurate course information and improved students' course engagement.”

What the sources say

• Partly supported

  Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Sajja et al. (2023) sourceExperimental — accuracy not yet measured

In the paper

“PRISMA guidelines provide a structured framework for searching, identifying, and selecting articles, as well as extracting, analyzing, and synthesizing data to address specific research questions.”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Moher et al., 2009 sourceExperimental — accuracy not yet measured

In the paper

“For instance, while AI can provide opportunities for instructors to personalize learning and provide students with more immediate feedback (Fauzi et al., 2023), it can raise concerns about academic integrity and the propagation of biased or inaccurate information.”

What the sources say

• Partly supported

  The “Facilitate collaboration between students” section says students can use ChatGPT to share feedback and discuss subjects with lecturers. The “Providing useful information and resources” section says ChatGPT can answer students’ questions. Neither section says instructors can use AI to personalize learning or provide more immediate feedback; the feedback described is between students. (LLM, conf=0.79)

  cites Fauzi et al., 2023 sourceExperimental — accuracy not yet measured

In the paper

“Firat (2023) also mentioned overreliance on AI, data privacy, and unequal access to AI tools as challenges.”

What the sources say

• Partly supported

  The Abstract identifies privacy concerns as an issue requiring strategies, and the Discussion and conclusion calls for responsible and equitable AI implementation. But the passages do not identify overreliance on AI or unequal access to AI tools as instructional challenges; the call for equitable implementation alone does not establish the latter. (LLM, conf=0.86)

  cites Firat (2023) sourceExperimental — accuracy not yet measured

In the paper

“After removing duplications, 279 records were obtained for screening following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines (see Figure 1; Moher et al., 2009).”

What the sources say

• Supported

  The Introduction identifies this paper as the statement of the PRISMA guidelines, and “From QUOROM to PRISMA” describes the flow diagram’s record-screening phase. The citation identifies the guidelines used; the 279 records are the citing authors’ own count, not a number attributed to Moher et al. (LLM, conf=0.94)

  cites Moher et al., 2009 source

In the paper

“Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases (Firat, 2023).”

What the sources say

• Partly supported

  The Literature review and Discussion and conclusion (pages unavailable) identify misinformation generation and bias in ChatGPT as concerns in education. The paper calls for equitable implementation, but the passages do not identify accessibility as a specific ethical concern in instruction. (LLM, conf=0.85)

  cites Firat, 2023 sourceExperimental — accuracy not yet measured

In the paper

“They found that most participants considered ITS effective in providing customized instruction; however, the lack of support and guidance in using ITS brought the instructors substantial challenges.”

What the sources say

• Supported

  Section 4.4 says most instructors believed customized programming instruction helps students. The Discussion (Section 5) says instructors considered ITS effective because it supports one-on-one teaching, and the Conclusion (Section 6) reports that most participants recognized its effectiveness in supporting learning. No page numbers are available in our copy. (LLM, conf=0.90)

  cites Al-Shanfari et al. (2023) source

In the paper

“Although most faculties embraced AI technology and suggested establishing AI as a standalone subject, some stated that challenges, limitations, and uncertainty about AI in education should be thoroughly researched before incorporating it into the curriculum.”

What the sources say

• Supported

  Section 4.2 reports that some interviewees wanted the potential and implications of AI in university education thoroughly evaluated before its integration into communication faculties. Section 4.4 identifies limitations, challenges, and uncertainty as reasons some interviewees urged caution about adding a dedicated AI course. (LLM, conf=0.89)

  cites Lopezosa et al. (2023) source

In the paper

“Enthusiasm of technologists for using machines to replace the labor of humans (Brown, 2020) is clearly a threat to the human-in-the-loop.”

What the sources say

• Partly supported

  In “Scenario 5: The Likely Schooling Future?” (page unavailable), Brown describes arguments for ICT-based learning instead of human teachers and says replacing human-to-human schooling seems contraindicated. In “Schooling in COVID-19” (page unavailable), he mentions people enthusiastic about e-learning, but does not identify technologists as enthusiastic about replacing human labor generally. The citation supports the narrower concern about replacing teachers with technology, not the broader characterization. (LLM, conf=0.82)

  cites Brown, 2020 sourceExperimental — accuracy not yet measured

In the paper

“In terms of existing curricula, there is considerable debate as to what students need to be taught about or with AI and how it could be integrated (Lopezosa et al., 2023).”

What the sources say

• Supported

  Section 4.5 describes differing areas of AI knowledge and uses to teach journalism students. Section 5 reports no consensus among interviewed lecturers and researchers on how to integrate AI into communication curricula, identifying three positions. The paper supports the claim in the context of journalism and communication education. (LLM, conf=0.88)

  cites Lopezosa et al., 2023 source

In the paper

“Indeed, an important threat AI brings to education is the requirement that all teaching and learning has to happen in an ICT environment, which could be seen as antithetical to the human in the human experience of learning (Brown, 2020).”

What the sources say

• Partly supported

  In “Scenario 5: The Likely Schooling Future?” (page unavailable), Brown warns that reducing schooling to computer interaction limits opportunities to learn human skills and argues against replacing human-to-human schooling. But Brown describes an ICT-only future as a possibility, not a requirement imposed by AI; the paper mentions AI separately as a technology that cannot yet mimic humanity. (LLM, conf=0.88)

  cites Brown, 2020 sourceExperimental — accuracy not yet measured

In the paper

“Although most faculties embraced AI technology and suggested establishing AI as a standalone subject, some stated that challenges, limitations, and uncertainty about AI in education should be thoroughly researched before incorporating it into the curriculum.”

What the sources say

• Partly supported

  Section 4.2 reports strong agreement among interviewed lecturers and researchers about incorporating AI into communication education. Section 5 calls a standalone AI subject the most widely accepted position, but section 4.4 describes three competing views; the paper does not establish that a majority of interviewees favored a standalone subject or report a position held by faculties as institutions. Page numbers were not available in our copy. (LLM, conf=0.85)

  cites Lopezosa et al. (2023) sourceExperimental — accuracy not yet measured

In the paper

“They found that teachers tended to be critical about students' texts, underestimated students' performance, and doubted that some student texts had been finished by GPT.”

What the sources say

• Supported

  In ‘Participants’ assessment of student responses,’ the paper says teachers were more critical of student-written texts and often downgraded responses previously graded A or C. The Discussion likewise reports downgrading and says teachers perceived some student texts as chatbot-written, questioning student authorship. (LLM, conf=0.90)

  cites Farazouli et al. (2024) source

In the paper

“The fear and possibility of non-detectable academic dishonesty will require substantial efforts to ensure the integrity and social warrant (Brown, 2022) of course grades and academic qualifications.”

What the sources say

• Partly supported

  In “The past,” Brown describes invigilated examinations designed to ensure integrity and qualifications that carried public recognition, supporting the general concern about the social credibility of assessment. The cited paper does not address non-detectable dishonesty or say that it will require substantial future efforts to protect course grades and qualifications. (LLM, conf=0.80)

  cites Brown, 2022 sourceExperimental — accuracy not yet measured

In the paper

“Some individuals suggested a compromise idea of integrating AI into communication subjects as a preliminary step (Lopezosa et al., 2023).”

What the sources say

• Supported

  Section 4.4 reports that some interviewees proposed integrating AI content into existing journalism or advertising classes while the technology is still developing, with dedicated AI subjects possible later. This supports the proposed preliminary step. (LLM, conf=0.93)

  cites Lopezosa et al., 2023 source

In the paper

“Firat (2023) indicated that implementing AI may require educators to change their role from being instructors to guides or facilitators.”

What the sources say

• Supported

  The Discussion and conclusion says AI can relieve university staff of routine tasks so they can focus on higher-order skills and mentoring. That supports the hedged description of educators moving toward a guiding role, even though the paper does not use the words “instructors” and “facilitators.” (LLM, conf=0.84)

  cites Firat (2023) source

In the paper

“The operability of this system was further supported by assessing the teaching quality and effect of two classes, and the AI results showed that both classes' teaching quality scored almost 7 out of 10, suggesting a need to improve.”

In the paper

“Similarly, Tang et al. (2023) discussed how a designed intelligent evaluation system could better recognize voices, face, postures, and teaching skills in microteaching skill training, accurately assess preservice teachers' teaching performance, and provide accurate guidance.”

What the sources say

• ?Not checked — no full text of the sourcecites Tang et al. (2023) source

In the paper

“Pereira et al. (2023) described how an emerging recommender system generated equivalent questions for assignments and exams, to enhance the variation of assignments and support instructors in preparing individualized assignments and minimizing plagiarism.”

What the sources say

• ?Not checked — no full text of the sourcecites Pereira et al. (2023) source

In the paper

“2023) study, conducted in the Chinese context, showed that a newly designed speech recognition method, based on a recurrent neural network algorithm, had a better accuracy rate and faster convergence, and could replace the previous method and effectively address issues of the low speech recognition rate caused by noisy environments.”

In the paper

“They found that this model correctly assigned 85% of students to the correct clusters (i.e., at risk or leader), and assisted instructors in making correct decisions.”

In the paper

“AI-generated scores were compared with real scores, providing reliable feedback to instructors and students.”

In the paper

“By implementing embedded glasses in real classrooms, Li L. et al. (2023) showed that this device helped instructors recognize and process students' real-time images and emotions and keep abreast of their learning status, and this information further provided timely feedback to instructors to change their teaching strategies.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Li L. et al. (2023)

In the paper

“Similarly, a new piano teaching mode powered by a vocal music singing learning system has been demonstrated to be relatively successful: it not only made piano teaching more personalized and intelligent, increased teaching efficacy by 7.31% compared to the traditional teaching mode, but also motivated students to engage more in piano practice time and classroom participation (Li Q. et al., 2023).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Li Q. et al., 2023

In the paper

“Asia, predominantly China, accounted for 17 of the 33 studies.”

In the paper

“Wang et al. (2025) combined human-computer interaction and deep learning algorithm to design an intelligent evaluation system for innovation and entrepreneurship.”

What the sources say

• ?Not checked — no full text of the sourcecites Wang et al. (2025) source

In the paper

“The simulation results also suggested that AI-powered teaching mode had the potential to be implemented in real classrooms.”

In the paper

“The system could detect students' attitudes and behaviors and assess teachers' teaching preparation, language expression, content mastery, and teaching design.”

In the paper

“Regarding research methods, 15 of the studies used modeling or simulation methods to design, implement, and test the accuracy and effect of AI tools.”

In the paper

“A new assessment method driven by AI tools could help instructors prepare personalized assignments.”

In the paper

“Therefore, compared to the control group, the teaching effect of the experiment group increased by 9.44%, and students reported more satisfaction with teaching.”

In the paper

“These records were excluded because their titles and abstracts showed that (a) they did not investigate how AI affected HE curriculum, instruction, and assessment (n =135), (b) they lacked empirical evidence (n = 63), or (c) they did not focus on university contexts (n = 8).”

In the paper

“Additionally, AI seems to benefit assessments by generating personalized assignments (Pereira et al., 2023), effectively assessing and predicting students' academic achievement (Wang et al., 2025) and non-cognitive outcomes (e.g., soft skills, Novais et al., 2023), identifying disadvantaged students (Saad and Tounkara, 2023), and assessing teaching effectiveness (Wang et al., 2025).”

What the sources say

• ?Not checked — no full text of the sourcecites Pereira et al., 2023 source
• ?Not checked — no full text of the sourcecites Wang et al., 2025 source
• ?Not checked — no full text of the sourcecites Saad and Tounkara, 2023 source

In the paper

“The remaining 73 records were downloaded for full-text screening.”

In the paper

“Moreover, it is hard for humans or AI detectors to identify AIgenerated texts or assignments, which in turn challenges existing assessment practices and strategies.”

In the paper

“The titles and abstracts of these records were assessed using the agreed inclusion and exclusion criteria (see Table 1), resulting in the exclusion of 206 records.”

In the paper

“AI is found to bring many challenges to curriculum developers and existing curricula, especially in deciding what content is more valuable, how to integrate AI into the current curriculum, and how to prepare students with digital literacy.”

In the paper

“Of the 33 studies, taking into account all overlapping categories, 21 (64%) papers had something to do with instruction, about half had something to do with assessment (17, 52%), and about a quarter focused on curriculum (9, 27%).”

In the paper

“Similarly, Kohnke et al. (2023) found that AI challenged the current assessment system, as instructors were worried that AI tools are too convenient for students making it easy to cheat and not work independently.”

What the sources say

• ?Not checked — no full text of the sourcecites Kohnke et al. (2023) source

In the paper

“As Table 2 shows, the balance was distributed widely across the world.”

In the paper

“Specifically, studies that introduced AI or HE curriculum, instruction, and assessment but did not actually explore the relationship between them were excluded (n = 32).”

In the paper

“Three distinct foci of AI were examined.The most common focus in 16 studies was the technological dimensions of AI, such as designing and modeling an AI tool for HE curriculum, instruction, and assessment and testing the accuracy of this tool itself.”

In the paper

“As shown in Figure 2, 22 of the studies addressed just one of the three aspects, with most being in instruction and assessment.Just 11 studies attempted an integration between two or more of the three aspects.”

In the paper

“Thus, AI has been found to bring benefits to HE curriculum, instruction, and assessment, including generating new materials, alleviating faculty workloads, and automating or optimizing assessment, in alignment with progressive literature”

In the paper

“Chan (2023) stated that new concerns in HE assessment have emerged, as most students and teachers are worried that some students use AI tools to cheat and plagiarize, and teachers could not identify such dishonesty correctly.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Chan (2023)

In the paper

“This review first reveals that AI can create new courses and resources, promote curriculum development, address time-consuming workloads concerning curriculum (e.g., questions about syllabi, time, and deadline), and evaluate the material difficulty and quality”

In the paper

“Chan (2023) indicated that AI may cause overdependence on technology and weaken social connections between teachers and students.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Chan (2023)

In the paper

“Considerable enthusiasm exists for the integration of AI skills with other graduate attributes such as the 4C skills (i.e., communication, collaboration, critical thinking, and creativity).”

In the paper

“The literature reported here makes clear substantial challenges to curriculum, instruction, and assessment.”

In the paper

“Other articles were removed because they (a) did not have empirical evidence (n = 4), (b) were in a non-HE context (n = 1), (c) were not available as full text (n = 2), and (d) were not in English (n = 1).”

In the paper

“Related to this is the lack of crossdisciplinary collaborative research between computer scientists and social scientists.”

In the paper

“Meanwhile, AI, as virtual teachers, could take up logistical workloads (e.g., reinforce students' mastery of key concepts) and provide instructors time and energy to conduct personalized instruction and satisfy students' distinct needs”

In the paper

“Chen et al. (2023) interviewed 16 faculty members in data science and revealed that inconsistent definitions of data science, inadequate team support, and lack of collaboration platforms were major challenges.”

What the sources say

• ?Not checked — no full text of the sourcecites Chen et al. (2023) source

In the paper

“There are some concerns about using AI in HE instruction, including challenging teacher's AI teaching competencies, ethical considerations, and lack of teaching support.”

In the paper

“There are 16 countries around the world contributing to this field.”

In the paper

“Twenty-five initial descriptive codes (e.g., improve teaching effectiveness, challenge the role of educators, assess teaching effect) were captured.”

In the paper

“Articles could be arranged into more than one theme because of the presence of multiple themes.”

In the paper

“A total of 2,810 articles were identified.”

In the paper

“The most frequently mentioned challenge is that AI has been proven to pass many examinations and assignments.Consequently, some students may use it to cheat or plagiarize.”

In the paper

“To establish trustworthiness, the research team made agreements on search terms and initial inclusion and exclusion criteria before the first author identified the literature.”

In the paper

“Not only would this tool ensure the alignment of assessment reading materials with course reading materials, but also provide valid evidence for the assessment materials.”

In the paper

“Filtering only to include peer-reviewed journal articles helped ensure the quality of literature in the search phases.”

In the paper

“Thus, curriculum, instruction, and assessment comprise the essential triad of all educational practices (Pellegrino, 2006).”

What the sources say

• ?Not checked — no full text of the sourcecites Pellegrino, 2006

In the paper

“The threat of AI applies also to instructors' role and their teaching abilities.”

In the paper

“These opportunities point to the development of interdisciplinary courses with the help of AI, especially in terms of course content and assessment design.”

In the paper

“The presence of AI seems to create opportunities for academics in terms of revisions to existing courses and freeing up time to focus on improving existing curriculum, instruction, and assessment quality.”

In the paper

“Thus, few have thought constructively about how to integrate AI into their teaching.”

In the paper

“During the screening stage, either author was unsure if a specific article should be included, and then the content of this article was discussed against the research question and focus of this review.These discussions resulted in refining the inclusion and exclusion criteria and a consensus on included articles.”

In the paper

“Application within humanities may be much more feasible with the current capacities of GenAI, but still academics have to learn how AI can be an adjunct to teaching rather than potentially a substitute for the instructor's knowledge and skill.”

In the paper

“The other authors critically read and reviewed the coding results, final synthesis, and interpretation of the themes.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Patton, 2003

In the paper

“This early research, however, is potentially problematic because of its narrowness.”

In the paper

“AI creates the possibility that skill with large language models (e.g., to analyze data, to compose communication) is what students might need in the future.”

In the paper

“The most important challenge centers around assessment and evaluation of learning.”

In the paper

“It is clear generative AIs will force academics to rethink the purpose of assessment (e.g., studentcentered or knowledge-based learning), the content and format of what is assessed, the design of assessments (e.g., process evaluation, outcome evaluation, or value-added evaluation), and the formative use of assessed performances.”

In the paper

“It is also likely that the challenges associated with meaningful integration of AI into curriculum, instruction, and assessment will remain difficult for years to come.”

In the paper

“While the future remains uncertain, several of the trends found in the study are likely to continue for some time to come.”