Physical Strain, reported by 120 participants, comprises visual fatigue and session exhaustion from reading and verifying AI outputs¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? 120 participants reported bodily discomfort from reading and verifying AI-generated outputs, captured by the Visual Fatigue and Session Exhaustion 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 · r3

Frequency counts from grounded theory coding. Representative statements include "My eyes hurt after reading many AI explanations" (n = 47) and "I get physically drained after long sessions with AI" (n = 24). No effect sizes are printed.

"Physical Strain was the fourth dimension identified in this study, with 120 participants reporting bodily discomfort arising from reading and verifying AI -generated outputs during academic work."

Discussion¶

Learner Variables¶

Physical Well-Being — outcome: instruction changes it

Cognitive Overload is the most frequently reported dimension of AI fatigue, driven by output volume and repeated verification — 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
Students used AI mainly for retrieving pharmacological mechanisms, generating PPT outlines, and clarifying concepts under rules requiring verification and student responsibility for AI outputs — related
Students used visualizations to verify their own reasoning rather than as primary explanation tools, valuing control over their cognitive effort (agency) — related
Moral Unease, reported by 190 participants, reflects authorship guilt and a gap between AI output quality and actual understanding — related
First-year students report coexisting high approval of AI-enabled courses and high self-reported psychological strain, with strain indicators endorsed by more than two-thirds of the sample — related
Motivational Disengagement, reported by 270 participants, captures reduced initiative and eroding confidence linked to AI reliance — related
Attentional Drift, reported by 125 participants, reflects pace impatience and restless switching after AI-supported work — related
Low comment rewriting does not imply a smooth process: students describe the main effort as verifying generated code rather than revising prompts — related

Page metadata

Field	Value
`type`	`claim`
`id`	`physical-strain-visual-fatigue-session-exhaustion`
`title`	`Physical Strain, reported by 120 participants, comprises visual fatigue and session exhaustion from reading and verifying AI outputs`
`description`	`Physical Strain, reported by 120 participants, comprises visual fatigue and session exhaustion from reading and verifying AI outputs`
`status`	`review`
`evidence_strength`	`moderate`
`newest_evidence`	`2026`
`generated`	by `process:wiki-ingest` on `2026-10-10`
`trust`	unverified — no one has confirmed this page's content
`sources`	1 — `miranda-2026`

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