Moral Unease, reported by 190 participants, reflects authorship guilt and a gap between AI output quality and actual understanding¶

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? 190 participants reported ethical and epistemic discomfort during AI use, captured by the Authorship Guilt and Understanding Gap indicators. → Miranda 2026

Evidence¶

Miranda 2026¶

Miranda, J. P. P., Parreño, E. B., & Rivera, J. G. (2026). Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory. International Journal of Learning, Teaching and Educational Research, 25(5), 91–107. https://doi.org/10.26803/ijlter.25.5.5

q2 · i? · qualitative · r2

Frequency counts from grounded theory coding of student accounts. Representative statements include "I sometimes feel guilty because I rely on AI" (n = 68) and "It bothers me that my output looks better than my actual understanding" (n = 21). No effect sizes are printed.

"Moral Unease was the third dimension identified in this study, with 190 participants reporting tension surrounding authorship and a perceived gap between AI-generated output quality and their actual understanding."

Discussion¶

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