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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Education and Learning (EduLearn)
John Paul P. Miranda
Pampanga State University
Humanities Computer Science & IT Education Engineering Library & Information Science Social Sciences Other

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AI-induced fatigue among students in higher education: a latent profile analysis Dynah D. Soriano; Jordan L. Salenga; John Paul P. Miranda; Juvy C. Grume; Emerson Q. Fernando; Amado B. Martinez, Jr.; Raymond A. Cabrera; Jaymark A. Yambao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1963-1971

Abstract

The integration of artificial intelligence (AI) tools in education offers significant benefits but also introduces challenges, including AI-induced fatigue among students. This study aimed to classify students’ experiences with AI tools using latent profile analysis (LPA). A quantitative cross sectional design and referral approach were used to collect survey data from 388 college students who actively used AI tools for academic purposes from November to December 2024. The survey measured AI usage intensity, AI literacy, self-efficacy, perceived usefulness, cognitive load, technostress, sleep quality, general fatigue levels, and attitude toward AI. Descriptive results indicated moderate levels of AI usage intensity, AI literacy, perceived usefulness, cognitive load, sleep quality, and general fatigue, with technostress and attitude toward AI also at moderate levels. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and profile size adequacy, and expert review supported the retained six-profile structure. The LPA identified six interpretable user groups: competent but sleep-deprived users, overwhelmed and high-strain users, stable moderate users, strained moderate users, high intensity strained users, and low-strain selective users. The findings show differences in patterns of competence, strain, fatigue, and sleep outcomes associated with AI tool use, which supports the development of profile specific strategies to manage technostress, cognitive load, fatigue, and sleep disruption among higher education students.
A text mining analysis of preservice teachers’ reflective discourses in online teaching: basis for a policy brief Julius Ceazar G. Tolentino; John Paul P. Miranda
Journal of Education and Learning (EduLearn) Vol 20, No 1: February 2026
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v20i1.22344

Abstract

This study examined the reflections of 142 preservice teachers who taught online during the COVID-19 pandemic. This study identified common themes, emotions, and patterns in their experiences using sentiment analysis and hierarchical clustering. The most frequently used words, such as “learn,” “experience,” and “time,” highlight themes of learning and action. Sentiment analysis shows that most of their reflections are positive, using words like “well,” “good,” and “great.” Hierarchical clustering revealed three main themes in their reflections: i) professional growth and development; ii) passion for teaching and connection; and iii) adaptability and resilience. These themes show the complex nature of their experiences. While focusing on personal and professional growth, a strong commitment to teaching, and adaptability in challenging situations was evidenced. The findings of this study will help create a policy brief addressing these themes. Recommendations include strategies for professional growth in online teaching, encouraging a love for teaching through online platforms, and improving teacher training programs to build adaptability and resilience. Policymakers and educators can use these insights to develop effective policies and practices that support preservice teachers in online teaching even during health crises or similar disruptions.
Co-Authors Amado B. Martinez, Jr. Dynah D. Soriano Emerson Q. Fernando Jaymark A. Yambao Jordan L. Salenga Julius Ceazar G. Tolentino Juvy C. Grume Raymond A. Cabrera