Navigating the moral panic: encouraging appropriate use of GenAI in the classroom rather than condemning innovation as disruption

Jennifer M. Krebsbach & Victoria L. Cross (University of California, Davis) — Assessment & Evaluation in Higher Education (Taylor & Francis). Open Access, CC BY 4.0. doi:10.1080/02602938.2026.2686727.

📄 Full text (Taylor & Francis, OA) — open data on GitHub (jenniferkrebsbach/moralpanic)

Summary

A natural-experiment / design-based study tracking eight iterations of a lower-division Data Visualisation in the Social Sciences course (n = 921 across six years) to test whether — and how — GenAI changes student learning. The authors frame faculty anxiety about GenAI as the latest in a series of "moral panics" (calculators, word processors, search engines, e-learning) and argue the productive response is to teach and embed GenAI use, not ban it. They compare three instructional conditions on two quiz types (knowledge vs. applied):

• pre-GenAI (2019–2020, n = 3 cohorts)

• GenAI-available (2023–2024, n = 3) — GenAI present but no pedagogical adaptation; some students used it, often ineffectively/unethically

• GenAI-integrated (2025, n = 2) — explicit instruction + encouragement to use GenAI on the applied portion; GenAI banned on the knowledge portion (paper quiz)

Method (key design)

• Quizzes 3–6 analysed (first two dropped as orientation; Quiz 7 dropped as low-stakes). Item-level performance (% correct = difficulty; SD = variability) from the LMS.

• 3 × 4 mixed-design ANOVA: AI-availability (between, 3 levels) × quiz number (within, 4 levels). Small per-condition N (2–3 cohorts), so effect sizes (ω²) reported as the primary evidence.^[raw/papers/tandf-2026-moral-panic-genai.md]

Key Findings

Applied questions — "available" hurt, "integrated" recovered

• Main effect of GenAI availability: F(2,5) = 5.85, p = 0.049, ω² = 0.35 (GenAI availability accounts for 35% of variance in applied-question performance).

• In the GenAI-available condition, applied performance was significantly lower than baseline on Quizzes 4, 5, 6. Because applied questions could not be answered directly by GenAI, the drop indicates students were less prepared — either unable to use GenAI effectively or unable to critically evaluate its output.

• In the GenAI-integrated condition, applied performance returned to ~pre-GenAI levels (and exceeded baseline on one harder quiz). Teaching students to use GenAI for data summarising levelled the field.^[raw/papers/tandf-2026-moral-panic-genai.md]

Knowledge questions — availability masked cheating; paper quiz revealed a deficit

• Same main effect, ω² = 0.35. Knowledge performance stayed at baseline during GenAI-available, then dropped below baseline once delivered on paper in the integrated condition.

• The authors interpret the stable central tendency but elevated variability during GenAI-available as evidence that some students unethically used GenAI to boost knowledge scores (heterogeneous use masked underlying learning differences). Moving knowledge quizzes to paper removed that opportunity and exposed that integrated-cohort students were less prepared — plausibly from over-reliance on GenAI to summarise content.^[raw/papers/tandf-2026-moral-panic-genai.md]

Variability — the headline signal

• Applied-question variability: F(2,5) = 64.84, p < 0.001, ω² = 0.88 — GenAI availability accounted for 88% of variability. Variability spiked in the GenAI-available condition, returned to baseline under integration.

• Knowledge-question variability: availability × time interaction F(6,15) = 11.65, p < 0.001, ω² = 0.57; main effect ω² = 0.78. GenAI availability accounted for 78% of the increase. Paper delivery produced the lowest, most stable variability → read as greater equity in the classroom.^[raw/papers/tandf-2026-moral-panic-genai.md]

Student feedback (pilot, n = 28/149 responded)

Interpretation: design beats ban

Connections to the wiki

• A rare natural-experiment counterpart to the RCTs: it shows unequal, undirected GenAI access widens score variability (equity loss) and depresses applied performance, while taught, structured use restores parity — directly extending generative-ai-guardrails-harm-learning (design is decisive) and ai-generated-feedback-higher-ed (well-architected AI use matches/exceeds baseline).

• Reinforces over-reliance (the integrated cohort's below-baseline knowledge scores are attributed to over-reliance) and cognitive-offloading / ai-making-us-stupid (offloading summarisation → less preparation).

• Strong authentic-assessment and academic-integrity contribution: the split-format (paper knowledge + open applied) is a concrete, replicable design pattern.

• Ties to ai-literacy (teaching use is the intervention) and equity (variability as an equity proxy).

Related Pages

• generative-ai-guardrails-harm-learning — PNAS RCT: unguarded AI tutoring harms learning; guardrails neutralize it (design decisive)

• ai-generated-feedback-higher-ed — Well-architected AI feedback matched teacher feedback (same "coach not crutch" logic)

• over-reliance — Below-baseline knowledge scores attributed to GenAI over-reliance

• cognitive-offloading — Offloading summarisation reduces hands-on preparation

• ai-making-us-stupid — Cognitive offloading → skill decay framework (Cash et al. 2026)

• authentic-assessment — Split-format (paper knowledge + open applied) as an authenticity/integrity design

• academic-integrity — Cheating masked by GenAI availability; paper delivery restores integrity

• ai-literacy — Teaching students to use GenAI is the active intervention

• equity — Score variability as an equity signal; taught use reduced the gap

• higher-ed — Deployment context (university)

Citation