Motivational Disengagement, reported by 270 participants, captures reduced initiative and eroding confidence linked to AI reliance¶

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? 270 participants described reduced initiative and growing reliance on AI, reflected in the Initiative Loss and Competence Doubt 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 the grounded theory coding of student accounts. Representative statements include "It makes me feel lazy to do my schoolwork" (n = 74) and "I lost confidence because I rely on it too much" (n = 41). No effect sizes are printed.

"Motivational Disengagement was the second most reported dimension, with 270 participants describing reduced initiative and growing reliance on AI for academic tasks."

Discussion¶

Learner Variables¶

• Self-Efficacy — outcome: instruction changes it

Related Claims¶

• Students who used AI reported feeling less accomplished, with heavier reliance associated with greater loss of satisfaction — related
• Grounded theory analysis of 1,054 university students identifies five dimensions of AI fatigue, each with two indicators — a broader claim this one bears on
• Learners' task-specific self-efficacy beliefs predict whether they initiate, persist through, and recover from difficulty on academic tasks. — related
• Attentional Drift, reported by 125 participants, reflects pace impatience and restless switching after AI-supported work — related
• Moral Unease, reported by 190 participants, reflects authorship guilt and a gap between AI output quality and actual understanding — related
• Students with a better understanding of how AI systems work show higher academic self-efficacy — related
• AI use threatens students' computing identities as competent, hardworking, and ethical, with students identifying as cheaters, lazy, or incompetent even where AI is permitted — related
• Students attributed initial passive reliance on GenAI to prompting paralysis and an authority bias compounded by low academic self-efficacy — related
• Physical Strain, reported by 120 participants, comprises visual fatigue and session exhaustion from reading and verifying AI outputs — related
• Students in both conditions named awareness of their reliance on AI as the most valuable course outcome, and unrestricted-AI students described shallow engagement they could not stop — related