The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index; none of the 8 sources its claims cite is checked for support yet.
Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education
Open accessAbstract
The sudden popularity and availability of generative AI tools, such as ChatGPT that can write compelling essays on any topic, code in various programming languages, and ace standardized tests across domains, raises questions about the sustainability of traditional assessment practices. To seize this opportunity for innovation in assessment practice, we conducted a survey to understand both the educators' and students' perspectives on the issue. We measure and compare attitudes of both stakeholders across various assessment scenarios, building on an established framework for examining the quality of online assessments along six dimensions. Responses from 389 students and 36 educators across two universities indicate moderate usage of generative AI, consensus for which types of assessments are most impacted, and concerns about academic integrity. Educators prefer adapted assessments that assume AI will be used and encourage critical thinking, but students' reaction is mixed, in part due to concerns about a loss of creativity. The findings show the importance of engaging educators and students in assessment reform efforts to focus on the process of learning over its outputs, higher-order thinking, and authentic applications.
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Claims the paper makes
24 Own result · 7 Sources not found · 2 Own method · 2 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It shows that educators have a strong preference for the adapted prompt while students are less convinced by the adaptation, especially for the essay prompt.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The assignment types rated to be least impacted are presentations and discussions that are either pre-recorded or live.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A similar pattern was observed for students' ratings of the assessment's authenticity.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, only one in four students used it weekly or daily for coursework (29% Australia; 24% US) and for fun (25% Australia; 14% US).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It shows that educators have a strong preference for the adapted prompt while students are less convinced by the adaptation, especially for the essay prompt.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, only one in four students used it weekly or daily for coursework (29% Australia; 24% US) and for fun (25% Australia; 14% US).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Educators were using it weekly or daily for professional purposes (35% Australia; 10% US), for research (15% Australia; 30% US), and for fun (31% Australia; 40% US).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Assessments that require product design or creative/artistic work are also rated as moderately affected.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proposed adaptation to the original prompt was generally thought to restore the level of academic integrity.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Educators were using it weekly or daily for professional purposes (35% Australia; 10% US), for research (15% Australia; 30% US), and for fun (31% Australia; 40% US).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 11
- Probably not a substantial claim. 12
- The quote only partly states this claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sample we have collected thus far consists of 389 students and 36 educators from two selective institutions of higher education, one in Australia (338 students; 26 educators) and one in the United States (51 students; 10 educators).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > METHODS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that almost every student and all educators in our sample have heard of ChatGPT.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides ChatGPT, students and educators use tools like Anthropic, Bard, BingChat, ClaudeAI, DALL-E, Midjourney, and Stable Diffusion.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An important exception is that students are more hesitant to endorse the adapted prompt than educators for reasons including a perceived loss in creativity.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 5 DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the coding assessment, students noted that they are already using various tools:”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one. The quote only partly states this claim.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students also had forward-looking ethical concerns:”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students also raised concerns about the adapted coding assessment:”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students also raised concerns about the adapted coding assessment:”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“that essays (incl.reports, literature review, case studies, research papers), computer code (incl.pseudo-code, mathematical proofs), short-answer and multiple-choice questions are very or at least moderately impacted.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Yet students also indicated that ChatGPT is useful to get them started”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- The authors state this as an assumption: In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
- Rests on an assumption: Pooling responses across the two institutions is valid for observing major trends.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This yields a clear within-participant comparison of how the adaptation affects their judgement of assessment quality.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > METHODS
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Researchers have identified several challenges educators face in designing and implementing new online assessments [4].”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 1 INTRODUCTION
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Unless educators and academic institutions adapt to this new reality, generative AI can undermine academic integrity in online assessment and the purpose of higher education to educate students, and ultimately reduce the value of a university degree [1].”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 1 INTRODUCTION
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Online assessment presents practical challenges for student authentication and academic integrity: identity verification is especially difficult in online essays, and remote invigilation is challenging for online examinations [2,4].”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 2 RELATED WORK
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Building on an extensive literature review, they identified six design criteria and four contextual factors at play (Figure 1).”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 2 RELATED WORK
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The survey format and questions were almost identical for educators and students since our goal is to compare their perspectives.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > METHODS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Generative AI tools can write human-like text in a conversational style, and ChatGPT has been applied in language translation, chat bots for conversations with humans, writing articles, stories, computer code, and other types of writing [1].”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 1 INTRODUCTION
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The survey instrument we developed allows us to evaluate the impact across various dimensions of online assessments [5] and evaluate similarities and differences between the student and educator perspectives.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > METHODS
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Not matched
The authors state this as an assumption.
The framework for online assessment quality is likely to generalize across countries.
“We believe that the framework is likely to generalize across countries and we are interested in examining if it can accurately reflect students' perspectives on assessment as well.”
The authors state this as an assumption.
In today's context, generative AI tools such as ChatGPT are widely available and students have equal access to the tool.
“Now consider the same assessment prompt again but in today's context when generative AI tools, such as ChatGPT, are widely available (i.e., students have equal access to the tool).”
The paper relies on this without support.
ChatGPT can write compelling essays on any topic, code in various programming languages, and ace standardized tests across domains.
“The sudden popularity and availability of generative AI tools, such as ChatGPT that can write compelling essays on any topic, code in various programming languages, and ace standardized tests across domains, raises questions about the sustainability of traditional assessment practices.”
The paper relies on this without support.
Pooling responses across the two institutions is valid for observing major trends.
“To analyze the data we have collected so far, we compute percentages and means of participants' ratings to observe major trends pooling across the two institutions.”
The paper relies on this without support.
Students can potentially use generative AI tools like ChatGPT to cheat on online assessments by submitting essays that are not their own work.
“Students can potentially use generative AI tools like ChatGPT to cheat on online assessments by submitting essays that are not their own work.”
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?
The cited source doesn’t support the claim as written.
In the citing paper
“In a survey by Intelligent.comconducted in May 2023 among 3,017 high school and college students, it was found that nearly one-third of the students admitted to using ChatGPT to complete their homework [6].”
From Survey on Plagiarism Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic Integrity (2024) · cites this paper as [6]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the other, it may erode students' critical thinking and reflection if used uncritically, leading to what scholars have termed 'metacognitive laziness' (Fan et al., 2025;Smolansky et al., 2023).”
From Enhancing service-learning through Generative AI: a mixed-methods study on educational Game design in a finance course (2025) · cites this paper as Smolansky et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While much of the discourse around students' use of GenAI tools has tended to focus on academic integrity concerns, our understanding of the sociotechnical implications on an increasingly diverse student population is still in its infancy [33, 41, 101, 116].”
From "I'd Never Actually Realized How Big An Impact It Had Until Now": Perspectives of University Students with Disabilities on Generative Artificial Intelligence (2025) · cites this paper as [101]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“While educators favor adapted assessments integrating GenAI to maintain academic integrity and encourage critical thinking, students express mixed reactions, particularly concerning the loss of creativity [40].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [40]
The cited source says what the claim attributes to it.
In the citing paper
“While educators favor adapted assessments integrating GenAI to maintain academic integrity and encourage critical thinking, students express mixed reactions, particularly concerning the loss of creativity [40].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [40]
The cited source says what the claim attributes to it.
In the citing paper
“Although initial research (Smolansky et al., 2023) suggests that there may be gaps between students' and educators' opinions on how to achieve the best assessment approach, further research is required in this area.”
From The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment (2024) · cites this paper as Smolansky et al., 2023
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 4 PRELIMINARY FINDINGS
“Educators, especially in Australia (Fig. 1), rated the impact of generative AI higher than students for all types of assignments.”
§ Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education > 5 DISCUSSION
“Figure: 2 Figure 2 : Figure 2: Australian educators' and students' ratings of how impacted different types of assessment are by generative AI.Mean ratings with standard error bars are shown.”
Citations
Cites
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
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 1×
“However, AI also raises serious concerns about the validity of assessment practices, including issues of academic integrity, especially honesty and plagiarism [3].”
BackgroundMarkauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., et al. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100056 · Cited by 353
Mate, K. E., & Weidenhofer, J. (2021) · FASEB BioAdvances · doi:10.1096/fba.2021-00075 · Cited by 67
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
Self-citationOnline Assessment in Australian University Business Schools: A Snapshot of Usage and Challenges
Cram, A., Harris, L. M., Raduescu, C., Huber, E., Zeivots, S., Brodzeli, A., Wright, S., & White, A. W. (2022) · ASCILITE Publications · doi:10.14742/apubs.2022.181 · Cited by 7
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A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493
“Teachers should understand GenAI’s broader implications for cognition, social interaction, and values so that they can effectively train the students to use GenAI to solve problems (Smolansky et al., 2023).”
BackgroundInfluentialPerkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291
“Although initial research (Smolansky et al., 2023) suggests that there may be gaps between students’ and educators’ opinions on how to achieve the best assessment approach, further research is required in this area.”
BackgroundGenerative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda
Bittle, K., & El-Gayar, O. (2025) · Information · doi:10.3390/info16040296 · Cited by 305
“While educators favor adapted assessments integrating GenAI to maintain academic integrity and encourage critical thinking, students express mixed reactions, particularly concerning the loss of creativity [40].”
An, Y., Yu, J. H., & James, S. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00507-3 · Cited by 337
“The dominance of topics related to learning and assessment suggests that institutions are prioritizing GenAI tools to enhance pedagogical practices and outcomes, highlighting a proactive approach to educational innovation (Smolansky et al., 2023).”
Harnessing Generative AI (GenAI) for Automated Feedback in Higher Education: A Systematic Review
Lee, S. S., & Moore, R. L. (2024) · Online Learning · doi:10.24059/olj.v28i3.4593 · Cited by 181
“Some reviews attempt to understand the potential of using GenAI or large language models in educational contexts, although they do not specifically focus on feedback.”
BackgroundKhlaif, 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
“In contrast, students have expressed concerns about the potential lack of creativity [33].”
Schei, O. M., Møgelvang, A., & Ludvigsen, K. (2024) · Education Sciences · doi:10.3390/educsci14080922 · Cited by 166
“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI
Baek, C., Tate, T., & Warschauer, M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100294 · Cited by 306
Examining faculty and student perceptions of generative AI in university courses
Kim, J., Klopfer, M., Grohs, J. R., Eldardiry, H., Weichert, J., Cox, L. A., & Pike, D. (2025) · Innovative Higher Education · doi:10.1007/s10755-024-09774-w · Cited by 109
“Smolansky et al. (2023) expanded the scope of their study to include both students and faculty in an exploration of AI’s perceived effects on assessment practices; both groups were interested in including AI in assessment, but they were apprehen-sive about how ChatGPT could negatively affect…”
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 113
“Frameworks like those proposed by Smolansky et al. [22] and Khlaif et al. [26] target particular stake-holder groups, such as students and faculty, but do not offer fully integrated institutional strategies that encompass administrators, curriculum designers, and policymakers.”
InfluentialSaihi, A., Ben-Daya, M., Hariga, M., & As'ad, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100274 · Cited by 75
Oc, Y., Gonsalves, C., & Quamina, L. T. (2024) · Journal of Marketing Education · doi:10.1177/02734753241302459 · Cited by 98
Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Llorens Salvador, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09576-x · Cited by 70
“The dependency of students on such tools hampers their creativity and learning skills (Smolansky et al., 2023).”
Lubbe, A., Marais, E., & Kruger, D. J. D. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13476-x · Cited by 90
“In higher education, the growth of Gen AI applications has introduced teaching and learning but also raised concerns about how assessment looks within the higher education environment (Smolansky, Cram, Raduescu & Zeivots, 2023).”
Wang, P., Jing, Y., & Shen, S. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.100996 · Cited by 93
Automated Assessment and Feedback in Higher Education Using Generative AI
Naseer, F., Khalid, M. U., Ayub, N., Rasool, A., Abbas, T., & Afzal, M. W. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch021 · Cited by 53
Đerić, E., Frank, D., & Milković, M. (2025) · Information · doi:10.3390/info16070622 · Cited by 60
Reimagining Higher Education: Navigating the Challenges of Generative AI Adoption
Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., & Dwivedi, Y. K. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10582-6 · Cited by 99
Interplay of rationality and morality in using ChatGPT for academic misconduct
Zhang, L., Amos, C., & Pentina, I. (2025) · Behaviour & Information Technology · doi:10.1080/0144929x.2024.2325023 · Cited by 48
“Therefore, among all assessment types, the focus of this study is written essays as the assignment most impacted by ChatGPT (Smolansky et al. 2023).”
Revell, T., Yeadon, W., Cahilly-Bretzin, G., Clarke, I., Manning, G., Jones, J., Mulley, C., Pascual, R. J., Bradley, N., Thomas, D., et al. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00161-8 · Cited by 51
“The urgency for comprehensive insights into AI’s ramifications for both teaching and assessment has only intensified in an era where powerful generative AI tools are publicly-accessible (Zawacki-Richter et al. 2019).”
BackgroundCodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming
Hou, X., Wu, Z., Wang, X., & Ericson, B. J. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662032 · Cited by 74
Dotan, R., Parker, L. S., & Radzilowicz, J. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659023 · Cited by 71
Early Perceptions of Teaching and Learning Using Generative AI in Higher Education
Damiano, A. D., Lauría, E. J. M., Sarmiento, C., & Zhao, N. (2024) · Journal of Educational Technology Systems · doi:10.1177/00472395241233290 · Cited by 42
“This supports previous literature, which addresses the concerns students have about Generative AI (Smolansky et al., 2023).”
ResultA survey on students’ use of AI at a technical university
Balabdaoui, F., Dittmann-Domenichini, N., Grosse, H., Schlienger, C., & Kortemeyer, G. (2024) · Discover Education · doi:10.1007/s44217-024-00136-4 · Cited by 134
“…of learning outcomes need to be adjusted, it is imperative to better understand how students are already incorporating these tools into their learning journeys [13, 40, 44], and thus several studies have already been conducted within the short amount of time that these tools were available [43].”
Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization
Zhang, S., Xu, J., & Alvero, A. (2025) · Sociological Methods & Research · doi:10.1177/00491241251327130 · Cited by 63
Bikanga Ada, M. (2024) · Education Sciences · doi:10.3390/educsci14101106 · Cited by 40
“This aligns with research emphasising the importance of designing assessments that test cognitive processes beyond the capabilities of genAI tools [15,67–70].”
InfluentialKeith, M. J., Keiller, E., Windows-Yule, C., Kings, I., & Robbins, P. T. (2025) · Education for Chemical Engineers · doi:10.1016/j.ece.2025.01.002 · Cited by 45
Van Wyk, M. M. (2024) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2024.7.1.15 · Cited by 35
Enlarged Education – Exploring the Use of Generative AI to Support Lecturing in Higher Education
Hennekeuser, D., Vaziri, D. D., Golchinfar, D., Schreiber, D., & Stevens, G. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00424-y · Cited by 48
“Recently, Smolansky et al. ( 2023) published a concise report detailing the results of a survey where both students and lecturers expressed their concerns regarding the integration of LLMs into educational settings.”
Factors Influencing Educators’ Technostress While Using Generative AI: A Qualitative Study
Khlaif, Z. N., Hamamra, B. T., Bensalem, E., Mitwally, M. A. A., & Sanmugam, M. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09885-z · Cited by 37
“Despite these challenging conditions, the findings of this study are consistent with previous research (Alasadi & Baiz, 2023; Çelebi et al., 2023 ; Smolansky et al., 2023; Wong et al., 2023 ), which identified several elements contributing to technostress among educators using GenAI in higher…”
Lee, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13129-5 · Cited by 46
“Smolansky et al. (2023) investigated the perspectives of educators and students on the impact of GenAI on assessments in higher education.”
Pre-service Teacher Perceptions of ChatGPT for Lesson Plan Generation
Kalenda, P. J., Rath, L., Heidt, M. A., & Wright, A. (2024) · Journal of Educational Technology Systems · doi:10.1177/00472395241301388 · Cited by 54
“A study by Smolansky et al. (2023) revealed that some higher education students have reservations with using generative AI as it can be seen as sti fl ing creativity.”
Almazrou, S. H., Alanezi, F., Almutairi, S. A., AboAlsamh, H. M., Alsedrah, I. T., Arif, W. M., Alsadhan, A. A., Alsanad, D. S., Alqahtani, N. S., AlShammary, M. H., et al. (2024) · Nutrition and Health · doi:10.1177/02601060241273627 · Cited by 33
“Generative AI technologies like language models that produce human-like text responses are promising tools for teaching students critical thinking (Dai et al., 2023; Michel-Villarreal et al., 2023; Smolansky et al., 2023).”
Students’ Experience of a ChatGPT Enabled Final Exam in a Non-Majors Chemistry Course
Clark, M. J., Reynders, M., & Holme, T. A. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00161 · Cited by 36
AI chatbots in programming education: guiding success or encouraging plagiarism
Akçapınar, G., & Sidan, E. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00203-7 · Cited by 27
“In the literature, there are self-report-based studies that have reached the findings that GenAI affects students’ critical thinking by loss of creativity [28].”
Literature Review on the Integration of Generative AI in Programming Education
Nathaniel, J., Oyelere, S. S., Suhonen, J. T., & Tedre, M. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00524-3 · Cited by 45
“The development of higher-order thinking skills is important in programming edu - cation, as it advances critical thinking (Smolansky et al., 2023), problem-solving (Kazemitabaar et al., 2023a, 2023b), and creativity (Kothari, 2023).”
Hwang, A. H., Siy, J. O., Shelby, R. M., & Lentz, A. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661555 · Cited by 34
“Besides building a mutual understanding of speech content, Smolansky et al. stressed the importance of forming a consensus on whether, when, and how genAI should be used in learning; they found this common ground played a crucial role in trust building between teachers and students [94].”
BackgroundWang, K. D., Wu, Z., Tufts, L., Wieman, C., Salehi, S., & Haber, N. (2025) · 2025 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016406 · Cited by 28
“For example, a survey for university instructors and students in the US and Australia revealed moderate usage of genAI tools for coursework and professional purposes [29].”
Atcheson, A., Khan, O., Siemann, B., Jain, A., & Karahalios, K. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714121 · Cited by 22
The Education Leadership Challenges for Universities in a Postdigital Age
Ellis, R. A. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00461-9 · Cited by 32
“These concerns will require existing degree programme and course designs to be rethought in increasingly inter-disciplinary ways, along with new programmes from disciplinary areas which are yet to exist but are emergent (Smolansky et al. 2023).”
UNESCO's AI Competency Framework
Mutawa, A. M., & Sruthi, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0884-4.ch004 · Cited by 24
Asking generative artificial intelligence the right questions improves writing performance
Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100374 · Cited by 21
Whether and When Could Generative AI Improve College Student Learning Engagement?
Guo, F., Zhang, L., Shi, T., & Coates, H. B. (2025) · Behavioral Sciences · doi:10.3390/bs15081011 · Cited by 21
“These tools deliver immediate feedback, adaptive explanations, and tailored learning pathways to meet diverse student needs, while also assisting with problem-solving tasks and automating routine academic processes (S. Guo et al., 2024; Smolansky et al., 2023).”
Dron, J. (2023) · Digital · doi:10.3390/digital3040020 · Cited by 22
One year in the classroom with ChatGPT: empirical insights and transformative impacts
Guo, F., Li, T., & Cunningham, C. J. L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1574477 · Cited by 40
Reimer, E. C. (2024) · Social Sciences · doi:10.3390/socsci13120648 · Cited by 18
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
Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First.
Crawford, J., Vallis, C., Yang, J., Fitzgerald, R., O'Dea, C., & Cowling, M. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.7.01 · Cited by 18
Asamoah, M. K., & Amarteifio, J. (2025) · Discover Education · doi:10.1007/s44217-025-00593-5 · Cited by 18
Reconsidering teacher assessment literacy in GenAI-enhanced environments: A scoping review
Cui, Y. H., Meng, Y., & Tang, L. (2025) · Teaching and Teacher Education · doi:10.1016/j.tate.2025.105163 · Cited by 24
Crafting Tomorrow’s Evaluations: Assessment Design Strategies in the Era of Generative AI
Kadel, R., Mishra, B. K., Shailendra, S., Abid, S., Rani, M., & Mahato, S. P. (2024) · International Symposium on Educational Technology · doi:10.1109/iset61814.2024.00012 · Cited by 18
“The authors in [9] have discussed innovative assessment strategies for addressing GenAI challenges in education, such as prioritising process over product, using oral assessments, employing AI detection tools, and promoting critical thinking.”
BackgroundEngineering Educators' Perspectives on the Impact of Generative AI in Higher Education
Dewan, U., Hingle, A., McDonald, N., & Johri, A. (2025) · 2025 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016518 · Cited by 16
“An important theme across these studies is the experimental nature of educator’s engagements with GenAI, frequently testing its capabilities by observing how it handles their assignments and assessments [3], [4], [14], [28].”
Hershkovitz, A., Tabach, M., Reich, Y., Lurie, L., & Cholcman, T. (2025) · Systems · doi:10.3390/systems13070518 · Cited by 26
Orhan, A., Yıldız, T. A., & Yağcı, Ş. Ç. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2437744 · Cited by 20
“Therefore, based on this theoretical framework, it can be said that the success of integrating these technologies into educational practices heavily depends on the willingness and readiness of both teachers and students to engage with GenAI (Smolansky et al., 2023).”
Generative artificial intelligence in fifth-generation education systems: A systematic review
Chakraborty, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114463 · Cited by 65
“This change from a model that is solely assessment-focused to one that emphasises interactive learning and continuous improvement contributes to the development of a more vibrant and encouraging learning environment [25].”
Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AIPreprint
Kadel, R., Mishra, B. K., Shailendra, S., Abid, S., Rani, M., & Mahato, S. P. (2024) · doi:10.36227/techrxiv.171502818.84241801/v1 · Cited by 25
Mathew, R., & Stefaniak, J. E. (2024) · TechTrends · doi:10.1007/s11528-024-00964-z · Cited by 15
“AI-powered systems can provide prompt and consistent evaluations, reducing the burden on faculty members while also offering students faster feedback (Baidoo-Anu & Ansah, 2023; Smolansky et al., 2023).”
Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work
Adnin, R., Pandkar, A., Yao, B., Wang, D., & Das, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713393 · Cited by 16
“These tools can negatively affect students’ learning gains when they submit AI-generated responses as their assignments without understanding the underlying concepts [51, 61, 87, 89] and provide an unfair advantage over peers [108].”
A tutorial for integrating generative AI in mixed methods data analysis
Combrinck, C. (2024) · Discover Education · doi:10.1007/s44217-024-00214-7 · Cited by 32
“Students are worried about the impact generative AI will have on their development, and the academy is concerned about academic integrity and the impact on assessment [46].”
Bernik, A., Radošević, D., & Čep, A. (2025) · Applied Sciences · doi:10.3390/app151810055 · Cited by 21
Assessing law students in a GenAI world to create knowledgeable future lawyers
Head, A., & Willis, S. (2024) · International Journal of the Legal Profession · doi:10.1080/09695958.2024.2379785 · Cited by 24
“Student views on using GenAI in higher education have received some attention (Chan 2023; Chan and Hu 2023; Farhi et al. 2023; Firat 2023; Huallpa et al. 2023; Shoufan 2023; Smolansky et al. 2023).”
Editorial: Harnessing the Power of Large Language Models in Agricultural Safety & Health
Shutske, J. M. (2023) · Journal of Agricultural Safety and Health · doi:10.13031/jash.15841 · Cited by 17
Cheung, L. H., & Dall’Asta, J. C. (2024) · Eídos · doi:10.29019/eidos.v17i23.1282 · Cited by 13
Labib, W. A., Abdelsattar, A., Abowardah, E. S., Abdelalim, M. F., & Mahmoud, H. (2025) · Sustainability · doi:10.3390/su172210029 · Cited by 18
Enhancing Critical Thinking with Gen AI: A Literature Review
Hikmawati, A., & Mohammad, N. K. (2025) · Buletin Edukasi Indonesia · doi:10.56741/bei.v4i01.764 · Cited by 20
“This aligns with the necessity of establishing robust AI literacy programs and regulatory frameworks to guide students and educators in using these tools responsibly, ensuring their integration aligns with academic integrity and societal values [3],[10].”
Generative AI Under Scrutiny: Assessing the Risks and Challenges in Diverse Domains
Nidhisree, C., Paul, A., Venunadh, A., & Bhowmick, R. S. (2024) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla63077.2024.10871350 · Cited by 10
“[11] Education 2.”
InfluentialKharrufa, A., Alghamdi, S. S., Aziz, A., & Bull, C. (2025) · ACM Transactions on Computing Education · doi:10.1145/3779296 · Cited by 26
“As with other research, their findings also reveal concerns about academic integrity and challenges to learning for novice students, but they also emphasize the rich pedagogical opportunities that these tools offer.”
Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Salvador, M. L. (2024) · arXiv · doi:10.48550/arxiv.2407.13105 · Cited by 8
“C Y ] 4 J un 2024 students on such tools leads to the loss of creativity and learning ability [7].”
BackgroundTHE ROLE OF GENERATIVE AI-POWERED PERSONAS IN DEVELOPING GRADUATE INTERVIEWING SKILLS
Sabbaghan, S., & Brown, B. (2024) · International journal on innovations in online education · doi:10.1615/intjinnovonlineedu.2024051770 · Cited by 9
Generative AI and Teachers - For Us or Against Us? A Case Study
Pettersson, J., Hult, E., Eriksson, T., & Adewumi, O. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208005 · Cited by 10
Exploring University Staff’s Perceptions of Using Generative Artificial Intelligence at University
Whitbread, M., Hayes, C., Prabhakar, S., & Upsher, R. J. (2025) · Education Sciences · doi:10.3390/educsci15030367 · Cited by 9
Chen, S., Metoyer, R., Le, K. H., Acunin, A., Molnar, I., Ambrose, A., Lang, J., Chawla, N. V., & Metoyer, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_22 · Cited by 10
Basil, M., Ahmed, W., Hajeomar, R., Strawbridge, J., Lynch, M. R., & Mukhalalati, B. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08527-3 · Cited by 14
“To address this need, suggested strategies include incorporating AI detection tools, prioritizing the learning process over outcomes, and expanding the use of oral examinations [185].”
Artificial Intellegence (AI) dan Dampaknya Dalam Distorsi Pendidikan Islam
Hakim, F., Fadlillah, A. N., & Rofiq, M. N. (2024) · Urwatul Wutsqo Jurnal Studi Kependidikan dan Keislaman · doi:10.54437/urwatulwutsqo.v13i1.1330 · Cited by 9
Papyshev, G. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2370716 · Cited by 15
“It is intriguing to note that, while students perceive the contribution of AI as largely positive for specific tasks, such as programming (Yilmaz & Karaoglan Yilmaz, 2023), they also express concerns about potential drawbacks, including a potential decrease in creativity (Smolansky et al., 2023).”
Ethical Uses of Generative AI in Assessment: Student Perceptions in UK Contexts
O’Dea, X., Bale, R., Chiu, Y.-L. T., Suleymenova, K., Tinker, A. J., & Stoker, R. (2025) · Evaluation Review · doi:10.1177/0193841x251399712 · Cited by 8
Memarian, B., & Doleck, T. (2025) · SN Computer Science · doi:10.1007/s42979-025-04569-y · Cited by 18
“Similarly, Smolansky et al. [ 107 ] measured and compared students’ and faculty’s perceptions.”
InfluentialChinese EFL learners’ engagement with ChatGPT feedback on academic writing: A case study in Malaysia
Kailin, Z., & Saeed, M. A. (2025) · Computers & composition/Computers and composition · doi:10.1016/j.compcom.2025.102976 · Cited by 19
Razi, A., Bouzoubaa, L. A., Pessianzadeh, A., Seberger, J. S., & Rezapour, R. (2025) · Research Square · doi:10.21203/rs.3.rs-5686084/v1 · Cited by 14
Kumar, S. S., Lones, M. A., Maarek, M., & Zantout, H. (2024) · International Workshop on Large Language Models for Code (LLM4Code) · doi:10.1145/3643795.3648380 · Cited by 7
Artificial Intelligence Policies in Higher Education: A Randomized Field Experiment
Greenspan, R. L. (2025) · Journal of Criminal Justice Education · doi:10.1080/10511253.2025.2449603 · Cited by 7
Developing students’ agency and voice by using generative AI in an online EAP module
Smirnova, L. Y. (2025) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2538781 · Cited by 7
Assessment Validity in the Age of Generative AI: A Natural Experiment
Brattli, H., Utne, A. S., & Lynch, M. (2026) · Informatics · doi:10.3390/informatics13040056 · Cited by 13
BoilerTAI: A Platform for Enhancing Instruction Using Generative AI in Educational Forums
Sinha, A., Goyal, S., Sy, Z., Kuperus, R., Dickey, E., & Bejarano, A. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893137 · Cited by 14
“…context and evidence in AI-generated responses, concerns about academic integrity, potential for hallucinations (incorrect or misleading results) [35]–[37], and concerns like the Junior-Year Wall [38] are addressed by having TAs add context and corroborate facts, effectively saving time and…”
(2025) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2025.8.1.20 · Cited by 7
Digital Assessment in Higher Education: Sustainable Trends and Emerging Frontiers in the AI Era
Lelescu, A., & Kabiraj, S. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-6136-4_2 · Cited by 9
Domain-Tailored Generative AI for Personalized Assistant
Jiang, N., Hasanzadeh, S., & Duffy, V. G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76809-5_17 · Cited by 6
Alhassan, M. D., Adam, I. O., Iddrisu, A., et al. (2025) · Digital Policy Regulation and Governance · doi:10.1108/dprg-01-2024-0003 · Cited by 6
Poornesh, M. (2025) · The Clearing House A Journal of Educational Strategies Issues and Ideas · doi:10.1080/00098655.2025.2493717 · Cited by 6
“Assessing the validity and reliability of Generative AI outputs is essential for upholding academic standards (Smolansky et al. 2023).”
Revolutionizing Creative Education
Akbar, K., Nyika, F. D., & Mbonye, V. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2418-9.ch012 · Cited by 6
From Assistance to Misconduct: Unpacking the Complex Role of Generative AI in Student Learning
Axelsson, A., Wallgren, D. T., Verma, U., Cajander, Å., Daniels, M., Eckerdal, A., & McDermott, R. (2024) · 2024 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893133 · Cited by 5
“In a survey involving students and educators, the majority were concerned about academic integrity and favored adapted assessments that assume GenAI usage while encouraging critical thinking [4].”
Unlocking Insights: Investigating Student AI Tutor Interactions in a Large Introductory STEM Course
Russell, J. M., Smith, A. M., George, S., Pratt, J., Fodale, B., Monk, C., & Brummett, A. E. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706524 · Cited by 7
Kusam, V. A., Shrestha, S., Kattan, K. A., Maxim, B. R., & Song, Z. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893118 · Cited by 5
“This is particularly crucial in online classes where students may not receive much one-on-one attention [20], [28], [29].”
AI Across Borders: Exploring Perceptions and Interactions in Higher Education
Gerard, J., Singh, S. P., Macleod, M., McKay, M., Rivoire, A., Chakraborty, T., & Singh, M. (2025) · Education Sciences · doi:10.3390/educsci15081039 · Cited by 8
“We also observed the usefulness of optional text comments in better assessing the perception, for example, by enlisting the use cases as well as potential risks to better map their awareness and perception around GenAI.”
The early wave of ChatGPT research: A review and future agenda
Busch, P. A., Hausvik, G. I., & Nielsen, J. A. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100213 · Cited by 9
Applying the AI assessment scale in writing and translation for EFL
Roe, J., Perkins, M., & Furze, L. (2025) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0034 · Cited by 21
“This is important given the gap between learners and teachers’ opinions on how to use AI (Smolansky et al., 2023).”
Employing generative AI applications in learning
Nadif, B., Zemrani, M. Z., Hammou, B. A., Mansour, S.-E., Fakhrou, A., & Bouih, A. (2025) · American Journal of STEM Education · doi:10.32674/vt55r634 · Cited by 6
Xu, X., & Thien, L. M. (2026) · European Journal of Education · doi:10.1111/ejed.70520 · Cited by 10
Japanese University EFL Student Insights on the Emerging ChatGPT Phenomenon
Price, G. (2024) · International Journal of Education Learning and Development · doi:10.37745/ijeld.2013/vol12n583100 · Cited by 5
“…2023; Chan, 2023; Chan & Hu, 2023; Cotton et al., 2023; Fitria, 2023; Fyfe, 2023; Kanabar, 2023; Mohammadkarimi, 2023; Price & Sakellarios, 2023; Smolansky et al., 2023; Van Wyk, 2024) have been published concerning ethics and assessing the use of AI-generated writing in student assignments.”
BackgroundComparative Performance of GPT-4, RAG-Augmented GPT-4, and Students in MOOCs
Miladi, F., Psyché, V., & Daniel Lemire, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-65996-6_7 · Cited by 5
“Instructors should adopt a more sustainable assessment approach that priori-tizes the cultivation of higher-order cognitive abilities, including critical thinking [18], problem-solving, and creativity [19].”
Background
Metadata
- Authors
- Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F.
- Affiliations
- Cornell University (US)
- The University of Sydney (AU)
- Cornell University, Ithaca, NY, USA
- Venue
- Proceedings of the Tenth ACM Conference on Learning @ Scale
- Publisher
- ACM
- Published
- 2023-07-20
- Pages
- 378-382
- OpenAlex
- W4384662087
- Semantic Scholar
- f6943500f55c8b67b2396238ea71d2b18f574323
- Cited by
- 275
- References
- 6
- Citations by year
- 2026: 76 · 2025: 116 · 2024: 79 · 2023: 4
- FWCI
- 63.59
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Learning @ Scale (L@S) (publishedVersion)
- Funders
- USyd-Cornell Partnership Collaboration Awards (PCA) (1178246719)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- generative AI, ChatGPT, higher education assessment, academic integrity, student perceptions, educator perspectives, assessment reform, critical thinking, creativity, higher-order thinking skills, artificial intelligence in education
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.
A language model drew the claims from the paper on 2026-10-23, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-23 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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