To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies

Barbara Ericson; James M. Zumel Dumlao; Wells Lucas Santo; Xingjian Gu

REVIEW 1 major objections 4 minor 76 references

To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies

T0 review · 1 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Undergraduate CS instructors, the paper argues, have mainly tried to AI-proof assessments, and this choice adds policing burden and worsens instructor-student relationships while learning harms go unaddressed.

desk verdict A genuinely useful qualitative study of how CS instructors design and enforce GenAI policies, but the abstract's 'primarily' frequency claim overstates what 13 self-selected interviews can support. read the letter →

arxiv 2607.16475 v2 pith:ZTJKFON4 submitted 2026-07-17 cs.CY

Xingjian Gu , Wells Lucas Santo , James M. Zumel Dumlao , Barbara Ericson This is my paper · ORCID

classification cs.CY

keywords computerscienceeducationgenerativeAIpolicyassessmentintegrityinstructor-studentrelationshiplearningharmssemi-structuredinterviewshighercognitiveoffloading

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that undergraduate computer science instructors have responded to generative AI mainly by trying to make assessments AI-proof—through paper exams, exam-heavy grading, and detection—rather than by changing how students learn. Drawing on 13 semi-structured interviews with US instructors, it distinguishes two sets of problems: learning harms (students offloading thinking, overestimating their skills, becoming socially isolated) and assessment harms (instructors no longer able to tell who has actually mastered the material). The paper argues that assessment-focused policies impose second-order costs: policing burden on instructors, stress and shame for students, and worsening instructor-student relationships. It recommends learning-oriented alternatives—transparent discussion of AI, modeled good use, and low-stakes formative assessments—that could guide students without curriculum overhaul.

What carries the argument

The paper's analytic engine is the distinction between learning harms and assessment harms, with AI policies treated as reified social contracts between instructors and students. Learning harms are the ways AI undermines student development: offloading cognitive work, creating illusions of competence, and eroding social learning. Assessment harms are the ways AI undermines the instructor's ability to tell whether submitted work reflects learning. The argument is carried by showing that policies responding to assessment harms—paper exams, exam-heavy grading, AI-use detection—leave learning harms in place while shifting responsibility onto students, whereas policies responding to learning harm

What would settle it

A multi-section comparison would settle it: if courses with strict AI-proofing (paper exams, exam-weighted grades) produced equal or better measures of long-term coding skill, self-efficacy, help-seeking, and instructor-student trust than courses using transparent, formative, AI-guidance policies, then the paper's central claim that assessment-oriented policies leave learning harms unaddressed and strain relationships would be wrong.

Watch

Extended reading notes

Core claim

The central finding is that AI policies in undergraduate CS are primarily reactions to assessment harms, not learning harms. Instructors report that AI tools induce cognitive offloading, illusions of competence, and reduced human help-seeking, but the policies they most readily adopt—proctored paper exams, heavier exam weighting, and detection of AI signatures in code—target the instructor's ability to trust submitted work. These policies leave the learning harms in place and create new second-order burdens: labor-intensive detection that most instructors cannot sustain, punitive grading that students experience as adversarial, and a shift of responsibility onto students to self-regulate in

Load-bearing premise

The entire pattern rests on 13 instructors' self-reports accurately describing their own policies and what students do; the paper explicitly says in its limitations that no independent source of empirical data validates these accounts.

Editorial extensions

If this is right

Assessment-oriented policies such as proctored paper exams and exam-heavy grading preserve short-term assessment integrity but do not fix the AI-driven learning harms instructors themselves describe.
Detection-based enforcement is so labor-intensive and unreliable that many instructors either stop enforcing, lower expectations, or rely on students to self-regulate, leaving vulnerable students without support.
The erosion of trust and rise of AI stigma reduce students' help-seeking from humans, compounding social isolation and making it harder for instructors to intervene.
Learning-oriented policies—clear AI-use guidance, instructor modeling, and low-stakes formative assessments—are reported by some instructors to be feasible without major curriculum change and deserving of broader adoption.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the self-report pattern generalizes, the institutional debate over 'cheating' with AI may be misframed: the harder problem is not detecting misuse but redesigning assessment so that earning a grade requires the learning the course wants to produce.
A natural next study would pair course AI policies with student learning analytics, assignment-replay logs, and relationship-quality surveys to test whether assessment-focused policies are as ineffective as these instructors report.
The paper's analogy to abstinence-only education points toward a testable extension: permissive, guidance-based AI policies that openly scaffold use may yield better long-term coding skill than restrictive policies once AI tools are part of everyday work.
Because instructors report nominally enforcing policies they cannot actually police, written AI policies may increasingly become symbolic documents, with actual norms negotiated informally in each classroom.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The thing worth knowing about this paper is that it's a solid, carefully done qualitative study of a real gap: how CS instructors actually design, implement, and enforce generative AI policies, and how those policies affect instructor-student relationships. The analytic split between learning harms (to students) and assessment harms (to instructors) is genuinely useful, and the finding that assessment-oriented policies like paper exams create policing burden and relationship strain is well supported by the interview data. The paper also does something rare: it gives concrete, low-cost learning-oriented alternatives, and it explicitly does not claim empirical proof for them. Credit where due: 13 interviews across nine institutions, rich quotes, reflexive thematic analysis done properly, and a limitations section that acknowledges self-report and non-generalizability. The authors know their method and stay within its interpretive frame most of the time.

The soft spots are real but not fatal. The biggest one is a mismatch between the abstract and the method. Section 3.3 says the findings 'are not meant to be representative of a broader population,' yet the abstract states that 'AI policies primarily seek to AI-proof assessments.' That's a population-level frequency claim, and 13 purposively and snowball-recruited instructors cannot support it. Same with 'surprisingly common, with 9 interviewees' — a sample count presented as if it tells us something about the field. Given self-selection into a study about AI policies, instructors with strong assessment concerns are plausibly over-recruited. The fix is easy: soften the abstract's 'primarily' and frame the pattern as emergent from this sample, not a prevalence finding. The self-report issue is secondary but worth noting; the paper already acknowledges it in 5.3, so I won't beat it. Minor editing slip: Section 5.3 has an unfinished sentence ending "such as," presumably a lost clause.

Bottom line: this is a well-executed qualitative study that deserves to be published and should get a serious referee. It advances the CS-education AI literature by shifting focus from tool effects to instructor-student relationships, and it generates testable recommendations. If I were reviewing, I'd ask for the abstract's language to be aligned with the non-representative design, but I would not request new data. Worth a reading group slot for anyone working on AI policy or qualitative methods in computing education.

Referee Report

1 major / 4 minor

Summary. This qualitative study reports a reflexive thematic analysis of 13 semi-structured interviews with U.S. undergraduate computer science instructors about their generative AI course policies. The authors distinguish two perceived types of harm: learning harms to students (cognitive offloading, illusion of competence, social isolation) and assessment harms to instructors (inability to verify student work). They argue that most instructors respond to assessment harms by AI-proofing assessments (e.g., switching to paper exams, increasing exam weight), which creates second-order costs: policing burden on instructors, strained instructor–student relationships, and a shift of responsibility for learning onto students. The paper then proposes learning-oriented alternatives, such as transparent AI instruction, modeling good AI use, formative assessments for metacognition, and motivation-focused course design. The manuscript is explicitly exploratory and disclaims representativeness (Section 3.3).

Significance. If the thematic claims hold, the study makes a useful contribution by shifting attention from AI tools themselves to the instructor–student relationship, a dimension often missing in tool-focused GenAI education research. The learning-harm vs. assessment-harm distinction is a clear conceptual contribution. The paper's method is transparent: it documents purposive/snowball sampling, interview protocol, reflexive thematic analysis, positionality, and extensive verbatim quotes that ground the analysis. The explicit limitations and the non-representativeness disclaimer (Section 3.3) are commendable. The recommendations in Section 5.2 are clearly framed as hypotheses requiring further empirical validation. Overall, this is a well-scoped exploratory study whose central claims are supported by the reported data, provided the language is carefully qualified.

major comments (1)

[Abstract and Section 4.2.2] The abstract's claim that AI policies "primarily seek to AI-proof assessments" is a frequency assertion that outruns the sample. Section 3.3 explicitly states the findings are "not meant to be representative of a broader population," and recruitment was purposive and snowball-based (Section 3.1). Section 4.2.2 reports that "9 interviewees" adopted paper exams and calls this "surprisingly common," which implies population-level prevalence. The qualitative analysis can support a claim about the interviewed instructors, but the current wording invites over-generalization. Please qualify the abstract and Section 4.2.2 (e.g., "among the instructors we interviewed") and soften the prevalence language in the recommendations.

minor comments (4)

[Section 5.3] The Limitations section is truncated mid-sentence: "skills like memorizing syntax might be less necessary for some students, such as". Complete the sentence or remove the dangling phrase.
[Section 3.1] Typo: "asksed" should be "asked".
[References] Reference [72] lists an author as "mark w uci" — likely a placeholder that should be corrected.
[Section 4.2.2] The phrase "surprisingly common" is ambiguous; as noted above, it is appropriate to describe the sample, but the paper should avoid implying a broader base rate given the non-representative sampling.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central themes are inductive summaries of interview data, and the two self-citations are background only.

full rationale

This is a qualitative interview study with no formal derivation, fitted parameter, prediction, or benchmark whose output is constructed from its inputs. The central finding—that AI policies primarily AI-proof assessments—is an inductive theme generated from 13 semi-structured interviews via reflexive thematic analysis (Section 3.3), and the manuscript explicitly presents it as an interpretation rather than a mathematical consequence. The paper's own limitation statement (Section 5.3: 'the answers to these questions are solely self-reported accounts by the instructors, without another source of empirical data to validate them') and its non-representativeness disclaimer (Section 3.3: 'the findings from the analysis are not meant to be representative of a broader population') are evidentiary weaknesses, not circular reasoning; even if the sample and self-reports were perfect, the claim would be a summary of those reports rather than a tautology. The self-citations ([31], [58]) appear only as background support for AI-literacy definitions and social-shaping framing and are not load-bearing for the paper's central contribution. No step equates a defined term with the target conclusion, no prediction is a re-labeled fit, and no uniqueness or ansatz is imported from the authors' prior work. Therefore the score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a qualitative interpretive study. There are no free parameters or invented entities. The central claim rests on the credibility of instructor self-reports, the chosen qualitative method, and imported prior literature assumptions.

assumptions (3)

domain assumption Instructor self-reports in semi-structured interviews are treated as reliable evidence of actual course policies, enforcement, and student behavior.
The entire analysis rests on 13 interviews; Section 5.3 acknowledges there is no independent empirical validation.
domain assumption Reflexive thematic analysis is a valid method for producing the study's patterns from the transcripts.
Section 3.3 applies Braun & Clarke's reflexive thematic analysis and explicitly rejects inter-rater reliability as a yardstick, so themes are researcher-constructed rather than objectively measured.
domain assumption Imported premises from prior literature: cognitive offloading harms learning, social learning is important, and harm reduction is an appropriate pedagogical frame.
The interpretation in Sections 2 and 5 relies on these cited premises (e.g., [29], [56], [74]) without new empirical support in this study.

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Cite this review

Pith. "Pith review of To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies." pith.science (2026). https://pith.science/paper/ZTJKFON4

@misc{pith2026260716475,
  author       = {Pith},
  title        = {Pith review of: To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZTJKFON4}},
  note         = {Machine review of arXiv:2607.16475}
}

read the original abstract

While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about their roles and responsibilities to students through their course AI policies. Based on 13 semi-structured interviews with CS instructors in the US, we found that while instructors recognize that AI tools could harm student learning, AI policies primarily seek to AI-proof assessments without directly addressing student learning. Although policies such as switching to paper exams can preserve assessment integrity in the short term, instructors report extra burden of policing student AI use behaviors and worsening relationships with students. Based on the experiences of several interviewees, we make recommendations on AI policies that are more learning-oriented and could guide students toward healthier AI usage instead.

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

76 extracted references · 8 canonical work pages

[1]

Rudaiba Adnin, Atharva Pandkar, Bingsheng Yao, Dakuo Wang, and Maitraye Das. 2025. Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, 1–17. https://doi.org/10.1145/3...

arXiv 2025
[2]

Ibrahim Albluwi. 2019. Plagiarism in Programming Assessments: A Systematic Review.ACM Trans. Comput. Educ.20, 1 (Dec. 2019), 6:1–6:28. https://doi.org/10.1145/3371156

doi:10.1145/3371156 2019
[3]

Areej Ali, Aayushi Hingle Collier, Umama Dewan, Nora McDonald, and Aditya Johri. 2025. Analysis of Generative AI Policies in Computing Course Syllabi. InProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSETS 2025). Association for Computing Machinery, New York, NY, USA, 18–24. https://doi.org/10.1145/3641554.3701823

arXiv 2025
[4]

Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, and Mohammad Amin Alipour
[5]

Yunjo An, Ji Hyun Yu, and Shadarra James. 2025. Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration.International Journal of Educational Technology in Higher Education22, 1 (Feb. 2025), 10. https://doi.org/10.1186/s41239-025-00507-3

doi:10.1186/s41239-025-00507-3 2025
[6]

Sarah Baldeo. 2026. Generative artificial intelligence reliance and executive function attenuation: Behavioral evidence of cognitive offload in high-use adults.Technology, Mind, and Behavior(2026). https://doi.org/10.1037/tmb0000191 Place: US Publisher: American Psychological Association

work page doi:10.1037/tmb0000191 2026
[7]

1977.Social learning theory

Albert Bandura and Albert Bandura. 1977.Social learning theory. Prentice Hall, Englewood Cliffs, N.J

1977
[8]

Aorigele Bao and Yi Zeng. 2025. AI disclosure, moral shame, and the punishment of honesty.Accountability in Research0, 0 (Aug. 2025), 1–14. https://doi.org/10.1080/08989621.2025.2542197 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/08989621.2025.2542197

arXiv 2025

Show all 76 references

[9]

Bourgeois, Ellen Zegura, Rodrigo Borela, and Ben Rydal Shapiro

Grace Barkhuff, Ian Pruitt, Vyshnavi Namani, William Gregory Johnson, Anu G. Bourgeois, Ellen Zegura, Rodrigo Borela, and Ben Rydal Shapiro
[10]

Nancy K. Baym. 2010.Personal connections in the digital age. Polity, Cambridge ; Malden, MA :

2010
[11]

Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. InProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). Association for C...

2021
[12]

Samuel Boguslawski, Rowan Deer, and Mark G. Dawson. 2024. Programming education and learner motivation in the age of generative AI: student and educator perspectives.Information and Learning Sciences126, 1-2 (July 2024), 91–109. https://doi.org/10.1108/ILS-10-2023-0163

2024 doi
[13]

Virginia Braun, , and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis.Qualitative Research in Sport, Exercise and Health11, 4 (Aug. 2019), 589–597. https://doi.org/10.1080/2159676X.2019.1628806 Publisher: Routledge _eprint: https://doi.org/10.1080/2159676X.201...

2019
[14]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology.Qualitative Research in Psychology3, 2 (Jan. 2006), 77–101. https://doi.org/10.1191/1478088706qp063oa Publisher: Routledge _eprint: https://www.tandfonline.com/doi/pdf/10.1191/1478088706qp063oa

2006 doi
[15]

Virginia Braun and Victoria Clarke. 2023. Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher.International Journal of Transgender Health24, 1 (Jan. 2023), 1–6. https://doi.org/10.1080/26895269.2022.2129597 Publisher: Taylor ...

2023
[16]

Ngoc Thanh Bui and Aijuan Dong. 2026. Adoption of Generative AI Policies in Computing Education: A Longitudinal Syllabus Analysis. In2026 International Conference on Semantic Computing (ICSC). 336–342. https://doi.org/10.1109/ICSC67292.2026.00055 ISSN: 2472-9671

2026
[17]

Darren Cambridge, Etienne Wenger-Trayner, Per Hammer, Phil Reid, and Lab Wilson. 2024. Theoretical and Practical Principles for Generative AI in Communities of Practice and Social Learning. InFraming Futures in Postdigital Education: Critical Concepts for Data-driven Practices...

2024 doi
[18]

Thomas Corbin, Phillip Dawson, Kelli Nicola-Richmond, and Helen Partridge. 2025. ‘Where’s the line? It’s an absurd line’: towards a framework for acceptable uses of AI in assessment.Assessment & Evaluation in Higher Education50, 5 (July 2025), 705–717. https://doi.org/10.1080/...

2025
[19]

Debby R. E. Cotton, Peter A. Cotton, and J. Reuben Shipway. 2024. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International61, 2 (March 2024), 228–239. https://doi.org/10.1080/14703297.2023.2190148 Publisher: ...

2024
[20]

Dawson, Rowan Deer, and Samuel Boguslawski

Mark G. Dawson, Rowan Deer, and Samuel Boguslawski. 2025. Cognitive dissonance in programming education: A qualitative exploration of the impact of generative AI on application-directed learning.Computers in Human Behavior Reports19 (Aug. 2025), 100724. https://doi.org/10.1016...

2025
[22]

Umama Dewan, Ashish Hingle, Nora McDonald, and Aditya Johri. 2025. Engineering Educators’ Perspectives on the Impact of Generative AI in Higher Education. In2025 IEEE Global Engineering Education Conference (EDUCON). 1–10. https://doi.org/10.1109/EDUCON62633.2025.11016518 ISSN...

2025
[24]

Andrew J. Elliot. 1999. Approach and avoidance motivation and achievement goals.Educational Psychologist34, 3 (June 1999), 169–189. https: //doi.org/10.1207/s15326985ep3403_3 Publisher: Routledge _eprint: https://doi.org/10.1207/s15326985ep3403_3

1999 doi
[25]

Yizhou Fan, Luzhen Tang, Huixiao Le, Kejie Shen, Shufang Tan, Yueying Zhao, Yuan Shen, Xinyu Li, and Dragan Gašević. 2024. Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance.British Journal of Educ...

2024 doi
[26]

Becker, Andrew Luxton-Reilly, and James Prather

James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. InProceedings of the 24th Australasian Computing Education Conference (ACE ’22). Asso...

2022
[27]

James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly, Eddie Antonio Santos, James Prather, and Brett A. Becker. 2023. My AI Wants to Know if This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises. InProceedings of the 25th Australasian Computing Educati...

2023
[28]

Gene Flenady and Robert Sparrow. 2026. Cut the bullshit: why GenAI systems are neither collaborators nor tutors.Teaching in Higher Education31, 1 (Jan. 2026), 163–172. https://doi.org/10.1080/13562517.2025.2497263 Publisher: Routledge _eprint: https://doi.org/10.1080/13562517....

2026
[29]

Michael Gerlich. 2025. AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking.Societies15, 1 (Jan. 2025), 6. https://doi.org/10.3390/soc15010006 Publisher: Multidisciplinary Digital Publishing Institute

2025 doi
[31]

Xingjian (Lance) Gu and Barbara J. Ericson. 2025. AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review. InProceedings of the 2025 ACM Conference on International Computing Education Research V.1 (ICER ’25). Association for Computing Mach...

2025
[32]

Brian Harrington, Irina Zlotnikova, Gayathri Nadarajan, and Samuel Ekundayo. 2025. Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education.ACM Trans. Comput. Educ.(Nov. 2025). https://doi.org/10.1145/3776558 ...

2025 doi
[33]

Irene Hou, Owen Man, Kate Hamilton, Srishty Muthusekaran, Jeffin Johnykutty, Leili Zadeh, and Stephen MacNeil. 2025. ’All Roads Lead to ChatGPT’: How Generative AI is Eroding Social Interactions and Student Learning Communities. InProceedings of the 30th ACM Conference on Inno...

2025
[36]

Irene Hou, Hannah Vy Nguyen, Owen Man, and Stephen MacNeil. 2025. The Evolving Usage of GenAI by Computing Students. InProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSETS 2025). Association for Computing Machinery, New York, NY, USA, 14...

2025
[37]

Xinying Hou, Zihan Wu, Xu Wang, and Barbara J. Ericson. 2024. CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming. InProceedings of the Eleventh ACM Conference on Learning @ Scale (L@S ’24). Association for Computing Machinery, ...

2024
[38]

Binny Jose, Jaya Cherian, Alie Molly Verghis, Sony Mary Varghise, Mumthas S, and Sibichan Joseph. 2025. The cognitive paradox of AI in education: between enhancement and erosion.Frontiers in Psychology16 (April 2025). https://doi.org/10.3389/fpsyg.2025.1550621 Publisher: Frontiers

2025
[40]

Peter Kahn, Mark Carrigan, Paul Smith, Lisa Murtagh, Ruirui Liu, and Fangtong Song. 2025. Teacher agency and generative artificial intelligence: teaching in higher education as a responsive, cultural activity.Learning, Media and Technology0, 0 (Oct. 2025), 1–12. https://doi.or...

2025
[41]

Viggo Kann. 2025. Students’ Attitudes Towards Cheating Before and After ChatGPT. InProceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1. ACM, Nijmegen Netherlands, 291–297. https://doi.org/10.1145/3724363.3729108

2025
[42]

Kizilcec, Elaine Huber, Elena C

René F. Kizilcec, Elaine Huber, Elena C. Papanastasiou, Andrew Cram, Christos A. Makridis, Adele Smolansky, Sandris Zeivots, and Corina Raduescu
[43]

Sam Lau, Kianoosh Boroojeni, Harry Keeling, and Jenn Marroquin. 2026. Barriers that Programming Instructors Face While Performing Emergency Pedagogical Design to Shape Student-AI Interactions with Generative AI Tools. (2026). https://doi.org/10.1145/3772318.3790682

2026
[44]

Ban It Till We Understand It

Sam Lau and Philip Guo. 2023. From "Ban It Till We Understand It" to "Resistance is Futile": How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot. InProceedings of the 2023 ACM Con...

2023
[45]

Elena Hayoung Lee, Yidan Yin, Nan Jia, and Cheryl J. Wakslak. 2026. Relying on AI at work reduces self-efficacy, ownership, and meaning while active collaboration mitigates the effects.Scientific Reports16, 1 (March 2026), 13583. https://doi.org/10.1038/s41598-026-42312-6 Publ...

2026 doi
[46]

Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa, Seth Bernstein, Joanne Kim, Andrew Tran, and Arto Hellas. 2023. Comparing Code Explanations Created by Students and Large Language Models(ITiCSE 2023). Association for Computing Machinery, New York, NY, USA, 124–130. http...

2023
[47]

Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, and Brett A. Becker. 2023. Using Large Language Models to Enhance Programming Error Messages. InProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). As...

2023
[48]

Rongxin Liu, Carter Zenke, Charlie Liu, Andrew Holmes, Patrick Thornton, and David J. Malan. 2024. Teaching CS50 with AI: Leveraging Generative Artificial Intelligence in Computer Science Education. InProceedings of the 55th ACM Technical Symposium on Computer Science Educatio...

2024
[49]

Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–16. https://doi.org/10.1145/3313831.3376727

2020
[50]

originality

Jiahui Luo (Jess). 2024. A critical review of GenAI policies in higher education assessment: a call to reconsider the “originality” of students’ work. Assessment & Evaluation in Higher Education49, 5 (July 2024), 651–664. https://doi.org/10.1080/02602938.2024.2309963 Publisher...

2024
[51]

Jiahui Luo (Jess). 2025. How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences. Teaching in Higher Education30, 4 (May 2025), 991–1006. https://doi.org/10.1080/13562517.2024.2341005 Publisher: Routledge _eprint: https://do...

2025
[52]

Wenhan Lyu, Shuang Zhang, Tingting Chung, Yifan Sun, and Yixuan Zhang. 2025. Understanding the practices, perceptions, and (dis)trust of generative AI among instructors: A mixed-methods study in the U.S. higher education.Computers and Education: Artificial Intelligence8 (June ...

2025
[53]

Qianou Ma, Hua Shen, Kenneth Koedinger, and Sherry Tongshuang Wu. 2024. How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging. InArtificial Intelligence in Education, Andrew M. Olney, Irene-Angelica Chounta, Zitao Liu, Olga C. Santos, and Ig Ibe...

2024 doi
[54]

Stephen Macneil, Paul Denny, Andrew Tran, Juho Leinonen, Seth Bernstein, Arto Hellas, Sami Sarsa, and Joanne Kim. 2024. Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models. InProceedings of the 26th Australasian Computing Education...

2024
[55]

Manley, Timothy Urness, Andrei Migunov, and Md

Eric D. Manley, Timothy Urness, Andrei Migunov, and Md. Alimoor Reza. 2024. Examining Student Use of AI in CS1 and CS2.J. Comput. Sci. Coll. 39, 6 (April 2024), 41–51

2024
[56]

Alan Marlatt

G. Alan Marlatt. 1996. Harm reduction: Come as you are.Addictive Behaviors21, 6 (Nov. 1996), 779–788. https://doi.org/10.1016/0306-4603(96)00042-1

1996 doi
[57]

Shakked Noy and Whitney Zhang. 2023. Experimental evidence on the productivity effects of generative artificial intelligence.Science381, 6654 (July 2023), 187–192. https://doi.org/10.1126/science.adh2586 Publisher: American Association for the Advancement of Science

2023 doi
[58]

Aadarsh Padiyath, Xinying Hou, Amy Pang, Diego Viramontes Vargas, Xingjian Gu, Tamara Nelson-Fromm, Zihan Wu, Mark Guzdial, and Barbara Ericson. 2024. Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course. In Pro...

2024
[59]

Pallant, Janneke Blijlevens, Alexander Campbell, and Ryan Jopp

Jessica L. Pallant, Janneke Blijlevens, Alexander Campbell, and Ryan Jopp. 2026. Mastering knowledge: the impact of generative AI on student learning outcomes.Studies in Higher Education51, 4 (April 2026), 714–735. https://doi.org/10.1080/03075079.2025.2487570 Publisher: Routl...

2026
[60]

Siddhartha Prasad, Ben Greenman, Tim Nelson, and Shriram Krishnamurthi. 2023. Generating Programs Trivially: Student Use of Large Language Models. InProceedings of the ACM Conference on Global Computing Education Vol 1 (CompEd 2023). Association for Computing Machinery, New Yo...

2023
[61]

Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, Stephen MacNeil, Andrew Petersen, Raymond Pettit, Brent N

James Prather, Paul Denny, Juho Leinonen, Brett A. Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, Stephen MacNeil, Andrew Petersen, Raymond Pettit, Brent N. Reeves, and Jaromir Savelka. 2023. The Robots Are Here: Nav...

2023
[62]

Reeves, Jaromir Savelka, David H

James Prather, Juho Leinonen, Natalie Kiesler, Jamie Gorson Benario, Sam Lau, Stephen MacNeil, Narges Norouzi, Simone Opel, Vee Pettit, Leo Porter, Brent N. Reeves, Jaromir Savelka, David H. Smith, Sven Strickroth, and Daniel Zingaro. 2025. Beyond the Hype: A Comprehensive Rev...

2025
[63]

Becker, Bailey Kimmel, Jared Wright, and Ben Briggs

James Prather, Brent N Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S Randrianasolo, Brett A. Becker, Bailey Kimmel, Jared Wright, and Ben Briggs. 2024. The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers. InProceedings of the 2024 ACM Conference...

2024
[64]

Victor Qiu, Liam Parker, Kaitlin Riegel, Nasser Giacaman, Paul Denny, Stephen MacNeil, and James Prather. 2026. One Line at a Time: Scaffolding Reflective Code Evaluation through Structured AI Assistance. InProceedings of the 28th Australasian Computing Education Conference. A...

2026
[65]

Md Mostafizer Rahman and Yutaka Watanobe. 2023. ChatGPT for Education and Research: Opportunities, Threats, and Strategies.Applied Sciences 13, 9 (Jan. 2023), 5783. https://doi.org/10.3390/app13095783 Number: 9 Publisher: Multidisciplinary Digital Publishing Institute

2023 doi
[66]

William Rebelsky. 2026. Talking to Our Students About Generative AI. InProceedings of the 57th ACM Technical Symposium on Computer Science Education V.1. Vol. 1. Association for Computing Machinery, New York, NY, USA, 915–921. https://dl.acm.org/doi/10.1145/3770762.3772642

2026
[67]

Judy Sheard, Paul Denny, Arto Hellas, Juho Leinonen, Lauri Malmi, and Simon. 2024. Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). As...

2024
[68]

Judy Hanwen Shen and Alex Tamkin. 2026. How AI Impacts Skill Formation. https://doi.org/10.48550/arXiv.2601.20245 arXiv:2601.20245 [cs]

2026 doi
[69]

Stanger-Hall and David W

Kathrin F. Stanger-Hall and David W. Hall. 2011. Abstinence-Only Education and Teen Pregnancy Rates: Why We Need Comprehensive Sex Education in the U.S.PLOS ONE6, 10 (Oct. 2011), e24658. https://doi.org/10.1371/journal.pone.0024658 Publisher: Public Library of Science

2011 doi
[70]

Marielle Justine Sumilong. 2025. Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms.Frontiers in Education10 (Sept. 2025). https://doi.org/10.3389/feduc.2025.1626802 Publisher: Frontiers

2025
[71]

Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, and Sean Rintel. 2024. The Metacognitive Demands and Opportunities of Generative AI. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24). A...

2024
[72]

Tamara P Tate, Daniel Ritchie, and mark w uci. 2026. Generative AI as a Mediational Agent: Rethinking Learning in Sociocultural Theory. https://osf.io/preprints/edarxiv/wcpj5_v1/

2026
[73]

Stephanie Tom Tong, Ashley DeTone, Austin Frederick, and Stephen Odebiyi. 2025. What are we telling our students about AI? An exploratory analysis of university instructors’ generative AI syllabi policies.Communication Education74, 3 (July 2025), 261–282. https://doi.org/10.10...

2025
[74]

L. S. Vygotsky and Michael Cole. 1978.Mind in Society: Development of Higher Psychological Processes. Harvard University Press. Google-Books-ID: RxjjUefze_oC

1978
[75]

Wiggins and Jay McTighe

Grant P. Wiggins and Jay McTighe. 2005.Understanding by Design. ASCD. Google-Books-ID: N2EfKlyUN4QC

2005
[76]

Lesley Wilton, Stephen Ip, Meera Sharma, and Frank Fan. 2022. Where Is the AI? AI Literacy for Educators. InArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium,...

2022 doi
[77]

Ramazan Yilmaz and Fatma Gizem Karaoglan Yilmaz. 2023. Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning.Computers in Human Behavior: Artificial Humans1, 2 (Aug. 2023), 100005. https://doi.org/10.1016/j.chbah...

2023
[78]

Zamfirescu-Pereira, Richmond Y

J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, Hamburg Germany, 1–21. https:...

2023
[79]

Nevenka Popović Šević, Aleksandar Šević, Milica Slijepčević, and Jelena Krstić. 2025. AI adoption in higher education: Exploring attitudes and perceived benefits between users and non-users.Online Journal of Communication and Media Technologies15, 4 (Oct. 2025), e202528. https...

2025 doi
[2023]

https://arxiv.org/abs/2310.04631v2

Trust in Generative AI among students: An Exploratory Study. https://arxiv.org/abs/2310.04631v2

arXiv
[2024]

Computers and Education: Artificial Intelligence7 (Dec

Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States. Computers and Education: Artificial Intelligence7 (Dec. 2024), 100269. https://doi.org/10.1016/j.caeai.2024.100269

2024
[2026]

InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26)

Situated Imaginaries: Designing AI Futures with Computer Science Teaching Assistants. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3772318.3791874

2026

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

Rudaiba Adnin, Atharva Pandkar, Bingsheng Yao, Dakuo Wang, and Maitraye Das. 2025. Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, 1–17. https://doi.org/10.1145/3...

arXiv 2025

[2] [2]

Ibrahim Albluwi. 2019. Plagiarism in Programming Assessments: A Systematic Review.ACM Trans. Comput. Educ.20, 1 (Dec. 2019), 6:1–6:28. https://doi.org/10.1145/3371156

doi:10.1145/3371156 2019

[3] [3]

Areej Ali, Aayushi Hingle Collier, Umama Dewan, Nora McDonald, and Aditya Johri. 2025. Analysis of Generative AI Policies in Computing Course Syllabi. InProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSETS 2025). Association for Computing Machinery, New York, NY, USA, 18–24. https://doi.org/10.1145/3641554.3701823

arXiv 2025

[4] [4]

Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, and Mohammad Amin Alipour

[5] [5]

Yunjo An, Ji Hyun Yu, and Shadarra James. 2025. Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration.International Journal of Educational Technology in Higher Education22, 1 (Feb. 2025), 10. https://doi.org/10.1186/s41239-025-00507-3

doi:10.1186/s41239-025-00507-3 2025

[6] [6]

Sarah Baldeo. 2026. Generative artificial intelligence reliance and executive function attenuation: Behavioral evidence of cognitive offload in high-use adults.Technology, Mind, and Behavior(2026). https://doi.org/10.1037/tmb0000191 Place: US Publisher: American Psychological Association

work page doi:10.1037/tmb0000191 2026

[7] [7]

1977.Social learning theory

Albert Bandura and Albert Bandura. 1977.Social learning theory. Prentice Hall, Englewood Cliffs, N.J

1977

[8] [8]

Aorigele Bao and Yi Zeng. 2025. AI disclosure, moral shame, and the punishment of honesty.Accountability in Research0, 0 (Aug. 2025), 1–14. https://doi.org/10.1080/08989621.2025.2542197 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/08989621.2025.2542197

arXiv 2025