Document Type : Review Article
Authors
1
Nursing Department, Saveh University of Medical Sciences, Saveh, Iran.
2
Student Research Committee, Saveh University of Medical Sciences, Saveh, Iran.
10.22034/sumsj.2026.596002.1161
Abstract
Introduction: The rapid adoption of artificial intelligence (AI) tools in higher education has created new opportunities to enhance learning processes, provide educational feedback, and personalize instruction. However, the widespread and sustained use of these tools may also increase cognitive load, complicate decision-making, and contribute to digital fatigue. This scoping review aimed to map the available evidence on digital fatigue associated with AI use among higher education students, identify its associated factors, and explore its potential relationship with learning outcomes.
Methods: This scoping review was conducted using the framework developed by Arksey and O’Malley and in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted in PubMed, Scopus, Web of Science, ERIC, ScienceDirect, SID, Magiran, and IranDoc to identify studies published in English or Persian between January 2022 and June 2026. The search strategy included keywords related to artificial intelligence, digital fatigue, and higher education students, along with their corresponding terms and equivalents.
Results: Of the 91 studies identified, 11 met the eligibility criteria and were included in the review following duplicate removal and screening. Only four studies directly investigated digital fatigue. The findings indicated that no universally accepted conceptual definition of AI-related fatigue has yet been established. Rather, this phenomenon has primarily been examined in relation to constructs such as digital fatigue, cognitive load, technostress, and academic burnout. Furthermore, purposeful use of AI tools was associated with more favorable educational outcomes, whereas excessive or unfocused use was associated with increased cognitive load and digital fatigue. However, the limited body of direct evidence precludes definitive conclusions regarding the distinct nature of AI-related fatigue and its implications for learning.
Conclusion: The evidence concerning AI-related fatigue and its relationship with learning outcomes among higher education students remains limited and heterogeneous. Given the small number of studies directly investigating this phenomenon and the predominance of cross-sectional designs and self-report measures, the findings should be interpreted with caution. The available evidence is insufficient to establish causal relationships. Further longitudinal and experimental research using valid and context-specific measurement instruments is warranted to clarify the nature of AI-related fatigue and elucidate its potential relationship with learning outcomes.
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