TY - JOUR
T1 - Can ChatGPT Ease Digital Fatigue? Short-Cycle Content Curation for University Instructors
AU - Cajas Bravo, Verónica Tomasa
AU - Huanca Rojas, Lupe Marilu
AU - Arias Lizares, Andrés
AU - Ramírez Cajamarca, Juan Cielo
AU - Vasquez Perdomo, Fernando
AU - De la Cruz Cruz, Miguel Angel
AU - Romero Girón, Hilario
AU - Guerrero Millones, Ana María
AU - Dávila-Morán, Roberto Carlos
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/9
Y1 - 2025/9
N2 - 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.
AB - 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.
KW - ChatGPT
KW - digital fatigue
KW - faculty development
KW - generative artificial intelligence
KW - higher education
KW - Job Demands–Resources (JD-R) theory
KW - prompt engineering
KW - screen time
KW - single-case experimental design
KW - technostress
UR - https://www.scopus.com/pages/publications/105017810834
U2 - 10.3390/educsci15091223
DO - 10.3390/educsci15091223
M3 - Original Article
AN - SCOPUS:105017810834
SN - 2227-7102
VL - 15
JO - Education Sciences
JF - Education Sciences
IS - 9
M1 - 1223
ER -