Mapping Instructors' Attitudes Towards Cheating with AI: An Exploratory Analysis Using Forum Analysis

Synthesis: Most accounts of AI cheating ask what students do. This one asks what instructors say to each other about it. Mohammadi and colleagues searched r/Professors for posts and comments on AI use, academic honesty and detection after ChatGPT's release — about 2.01 million posts and comments in all — drew a random sample of 2,000, and coded them thematically. Three motivations emerged: protecting students' learning, protecting instructors' own well-being, and upholding values of integrity and equity. Three strategy families followed: pedagogical and Assessment redesign, technological control, and communication with students. The contribution is the instructor-side taxonomy and the tension inside it — between strategies that make learning visible and strategies that make authorship verifiable — which the authors read through a learning-sciences lens as a question about what should count as evidence of learning in a course.

Key Findings

1. Instructors gave three reasons for acting. Protecting students' learning process, their own well-being and professional fulfillment, and their values of integrity and equity — motivations personal rather than merely policy-compliance.
2. Well-being is named as a motivation, not a side effect. Repeated encounters with AI-generated work produced frustration, lost personal connection with students and, in the authors' words, lower satisfaction with the job.
3. Equity creates an integrity dilemma. One instructor's account described tension between supporting inclusive, student-centered education and encountering what they saw as widespread misuse with little institutional appetite to address it.
4. Redesign was the most substantive strategy family. Handwritten and in-class work, process-oriented assessment with drafts and source trails, oral checks, and task tweaks that require local course knowledge.
5. Technological control was used with visible ambivalence. Proctoring, lockdown browsers, timed windows and identity checks, with detectors used as triage signals rather than as proof — one instructor noting the cheating was apparent "even without the detector".
6. Communication ranged from ethical dialogue to deterrence. Some instructors discussed responsible use and ran exercises on the ethics of AI-written text; others demonstrated that AI use can leave traces.
7. The authors frame the split as a design question. Surveillance-oriented strategies verify authorship, while redesign-oriented strategies re-center learning as participation, and the paper treats the tension as the productive object of study.

What the forum data can and cannot show

The corpus is large — about 2.01 million posts and comments matched by keyword — but the analyzed material is a random sample of 2,000 posts and comments, hand-coded by the authors with thematic analysis. That design captures how instructors narrate their reasoning in public, which is precisely the sense-making the paper is after: the why and how behind policy choices, in the practitioners' own words.

What it does not capture is behavior. A professor who writes that they moved to handwritten essays is reporting an intention or a practice, not documenting an effect on cheating or on learning. The paper is explicit that it is not evaluating the strategies normatively; it maps them, and its learning-sciences framing — learning as participation in disciplinary activity rather than production of decontextualized outputs — is the lens it uses to organize the map.

The distinction matters for anyone citing this work. The taxonomy is evidence about instructor reasoning and reported practice, not about which strategies reduce misconduct.

Where the two strategy families lead

The strategies diverge in what they treat as the problem. Redesign assumes the task was the weak point: if the assignment can be completed by a model, the assignment was asking for something a model can produce, so shift weight to process artifacts, in-class writing and oral checks. Technological control assumes verification is the weak point: if authorship cannot be established, add proctoring, lockdown browsers or detectors.

The paper's contribution to that argument is the observation that the second family narrows what counts as evidence of learning to what can be monitored. Several instructors interviewed or quoted in the forum data were already uneasy about that trade, and the equity thread makes it sharper: surveillance falls hardest on students whose circumstances make monitored, in-person work difficult. The authors recommend treating the tension as an object of inquiry rather than resolving it in advance.

What this means for practice

• Instructors: Diagnose the assignment before you add surveillance. If the task can be completed by a model, process artifacts, in-class writing and oral checks address the cause; monitoring addresses the symptom.
• Faculty developers: Put instructor well-being on the agenda. It appears here as a stated reason instructors act, not as an incidental cost, and it is rarely in AI-policy workshops.
• Administrators: Expect an equity argument, not only an integrity one. Instructors report the conflict between inclusive practice and enforcement, and policies that ignore it push the dilemma onto individual courses.
• Instructors: Use detectors as triage, not as verdicts. The instructors in this sample who used them described them as prompts for a closer look.

Limitations

• The sample is one subreddit, and r/Professors over-represents instructors who are motivated to discuss teaching problems publicly; the paper does not characterize non-participants.
• Of roughly 2.01 million matched posts and comments, 2,000 were analyzed, with no reported inter-coder agreement statistic for the thematic coding.
• Posts are self-reported accounts of practice, so the study documents what instructors say they do and why, not what they do or what effect it has.
• The data contain no student perspective, so student reasoning about AI use is inferred from instructors' descriptions of it.
• The analysis is exploratory and the authors present the themes as a map for further inquiry rather than as a tested account of instructor behavior.

Citation

Mohammadi, I., Keramati, A., & Rismanchian, S. (2026). Mapping Instructors' Attitudes Towards Cheating with AI: An Exploratory Analysis Using Forum Analysis. arXiv:2611.13366.