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TRUVACE RECORD VERSION
record: TRV-2026-2225
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-23T23:59:59.172042Z
status: published
lens: p_space
sector: labor
headline: BEYOND TECHNOSTRESS AI Fatigue as a Cumulative Adaptation Burden in Education and Work
dek: Artificial intelligence has become a persistent layer of work and learning rather than an occasional tool. Users must repeatedly decide when to delegate, how to prompt, whether to trust, how to verify, and how responsibility should be distributed. This integrative review defines AI fatigue as a cumulative reduction in the cognitive, affective, or motivational capacity for adaptive AI engagement when interaction, evaluation, correction, learning, and role-adjustment demands accumulate faster than recovery and sup…
gain_title: (none)
problem_title: Constant demands to delegate, prompt, verify and adjust roles around AI can accumulate faster than recovery and support, reducing people's capacity for adaptive AI engagement in work and learning.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Constant demands to delegate, prompt, verify and adjust roles around AI can accumulate faster than recovery and support, reducing people's capacity for adaptive AI engagement in work and learning.
problem_evidence: a cumulative reduction in the cognitive, affective, or motivational capacity for adaptive AI engagement
quick_read: This integrative review argues that AI now forms a persistent layer of work and learning, so users must repeatedly decide when to delegate, how to prompt, whether to trust and how to verify. It defines AI fatigue as a cumulative loss of capacity for adaptive engagement with AI.

The framing matters because gains in output may come at a cost to the people and institutions using AI, and it treats fatigue as a system-level adaptation problem rather than individual weakness. The text offers a conceptual framework and research agenda, so the size and prevalence of the problem remain to be established.
limitation: The article is a conceptual integrative review that proposes frameworks and a research agenda, so it does not itself measure how common or severe AI fatigue is.
tag: Evidence-backed problem
key_points: AI fatigue is defined as reduced cognitive, affective or motivational capacity when AI-related demands accumulate faster than recovery and support. | The review links technostress, automation vigilance, cognitive offloading, trust calibration, digital fatigue and the Job Demands–Resources perspective. | It proposes an Exposure–Adaptation–Depletion–Response framework, a five-domain taxonomy and an AI Fatigue Load model. | The load model separates use volume from uncertainty, task stakes, correction burden, interruption, role disruption and imposed adoption velocity. | Proposed interventions span interface design, workflow, curriculum, organizational policy and governance.
rundown: The review draws on several established literatures and positions AI fatigue as distinct from generic screen tiredness and from burnout, locating it in the coupled human–AI–institution system.

It incorporates a 2026 validated 15-item AI Fatigue Scale, and presents its load model as a complement that specifies socio-technical antecedents and exposure conditions rather than the outcome itself.

The article also identifies recurring inferential errors, sets out a measurement and longitudinal research agenda, and offers an intervention hierarchy for education and organizations.
sources:
- peer_reviewed | Emirati Journal of Applied Psychology | https://doi.org/10.54878/bxxewh81 | 2026-10-23
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