When Does It Become Cheating? Exploring the Roles of Self-Efficacy and the Impostor Phenomenon in Computing Students' Perceptions of Academic Dishonesty

TEXT START: Academic dishonesty is an urgent problem in computer science (CS) education, compounded by the rise of artificial intelligence (AI) and the prevalence of online learning in a post-COVID world.

The Dissection

The paper measures how students classify ambiguous academic behaviors, then links those judgments to self-efficacy and impostor feelings. Its real function is narrower than its framing: it converts a structural crisis in assessment into a psychological profile of students navigating it.

The evidence is correlational and institutionally narrow: 443 undergraduates at one Southeastern U.S. university, surveyed asynchronously about 13 scenarios. It measures perceptions of cheating, not cheating itself, learning, competence, or post-graduation performance. The study therefore records moral boundary disputes without establishing what causes them or whether the boundaries remain economically meaningful.

The Core Fallacy

The core fallacy is treating AI-era academic integrity primarily as a problem of student psychology. Under the Discontinuity Thesis, AI is not merely another tool that students may misuse. It is the mechanism that destabilizes the old education-to-employment pipeline by automating cognitive work and degrading the reliability of human-only assessment.

The question “When does it become cheating?” is therefore not just about self-efficacy or impostor syndrome. It is evidence that institutions can no longer cleanly distinguish learning, assistance, substitution, and machine-produced output. The paper studies the smoke alarm while leaving the burning assessment regime largely untouched.

Its correlations cannot establish that impostor feelings cause stricter judgments, that stricter judgments reflect greater integrity, or that self-efficacy changes behavior. They may simply show that different students interpret an unstable rule system differently.

Hidden Assumptions

• That academic integrity remains a stable and coherent category despite AI making cognitive substitution cheap and ubiquitous.
• That the institution’s existing assessment model still measures individual competence rather than access to tools, surveillance, and procedural compliance.
• That psychological adjustment can repair a legitimacy problem created by technological displacement.
• That judging AI use as serious cheating is evidence of a meaningful moral distinction rather than fear of credential devaluation.
• That student perceptions are the critical variable, even though the decisive variable under P1–P3 is whether human cognition remains economically necessary.
• That a single university sample can illuminate computing education broadly.
• That correlation among self-efficacy, impostor phenomenon, demographics, course dedication, and TA experience can be interpreted without substantial confounding.

Social Function

This is a partial truth functioning as transition management and ideological anesthetic. It identifies a real symptom: students are navigating collapsing boundaries around AI use, and psychological insecurity shapes how they interpret those boundaries. But its framing individualizes the crisis. Institutions can discuss impostor feelings, clarify policies, and redesign conversations while postponing the more dangerous question: whether conventional coursework and credentials still certify capabilities that AI has made inexpensive to produce.

The paper also provides institutional self-exoneration. If integrity failures are framed as differences in self-efficacy or impostor experience, responsibility shifts toward student psychology and away from obsolete assessment systems, credential inflation, and the labor market’s changing demand for human cognitive work.

The Verdict

Useful as a narrow survey of perception; inadequate as an account of academic dishonesty in the AI transition. The paper documents disagreement at the boundary of a dying measurement system, then risks mistaking that disagreement for the disease.

Under the Discontinuity Thesis, AI does not merely create new cheating scenarios. It erodes the mass educational bargain that credentials reliably convert human effort into economically necessary capability. The study can help institutions manage the corpse, but it does not explain how to resurrect the system that produced it.