Abstract
Digital fatigue is pervasive among university instructors, yet rigorous evidence on whether generative AI improves well-being is scarce. We conducted an eight-week staggered multiple-baseline AB–AB reversal with eight lecturers at a private Peruvian university. In intervention phases, participants replaced full readings with a daily ≤200-word ChatGPT summary plus three discussion questions (“content-curation sprint”). Outcomes were self-reported digital fatigue (FDU-24) and automatically logged screen time; analyses were carried out using trend-corrected Tau-U and paired-phase Cohen’s d. Across two intervention cycles, screen exposure fell by about 122 min per day (~29% of baseline) and digital fatigue scores decreased by ~22%. Effects were large and replicated (aggregate Tau-U = −0.79; d = −1.5 to −2.2). Treatment fidelity averaged 96%, and post-study technology acceptance was high. These findings provide preliminary experimental evidence that a brief, low-friction ChatGPT workflow can simultaneously reduce screen time and alleviate digital fatigue in higher-education faculty, suggesting a dual productivity-and-well-being dividend and positioning generative AI as a Job Demands–Resources “resource” rather than a stressor. Multi-site randomized trials with active controls, longer follow-up, and cost-effectiveness analyses are warranted. Practical implications for faculty development are immediate.
| Original language | American English |
|---|---|
| Article number | 1223 |
| Journal | Education Sciences |
| Volume | 15 |
| Issue number | 9 |
| DOIs | |
| State | Indexed - Sep 2025 |
Bibliographical note
Publisher Copyright:© 2025 by the authors.
Keywords
- ChatGPT
- Job Demands–Resources (JD-R) theory
- digital fatigue
- faculty development
- generative artificial intelligence
- higher education
- prompt engineering
- screen time
- single-case experimental design
- technostress
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