Attentional Drift, reported by 125 participants, reflects pace impatience and restless switching after AI-supported work¶

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? 125 participants reported difficulty sustaining focus during and after AI-supported academic work, captured by the Pace Impatience and Restless Switching 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. Representative statements include "I get impatient when I need to read my textbook after using AI" (n = 19) and "I keep switching tabs because I want faster answers" (n = 38). No effect sizes are printed.

"Attentional Drift was the fifth dimension identified in this study, with 125 participants reporting difficulty sustaining focus during and after AI -supported academic work."

Discussion¶

Learner Variables¶

• Attention — outcome: instruction changes it

Related Claims¶

• 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
• Motivational Disengagement, reported by 270 participants, captures reduced initiative and eroding confidence linked to AI reliance — related
• LLM participant agents clarify stage goals, keep discussions on topic, and prompt deeper ethical reflection — related
• Physical Strain, reported by 120 participants, comprises visual fatigue and session exhaustion from reading and verifying AI outputs — related