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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Education and Learning (EduLearn)
Yambao, Jaymark A.
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Humanities Computer Science & IT Education Engineering Library & Information Science Social Sciences Other

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Assessing novice programmers’ perception of ChatGPT: performance, risk, decision-making, and intentions Miranda, John Paul P.; Yambao, Jaymark A.
Journal of Education and Learning (EduLearn) Vol 19, No 4: November 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v19i4.22328

Abstract

This study explores the novice programmers’ intention to use chat generative pretrained transformer (ChatGPT) for programming tasks with emphasis on performance expectancy (PE), risk-reward appraisal (RRA), and decision-making (DM). Utilizing partial least squares structural equation modeling (PLS-SEM) and a sample of 413 novice programmers, the analysis demonstrates that higher PE of ChatGPT is positively correlated with improved DM in programming tasks. Novice programmers view ChatGPT as a tool that enhances their learning and skill development. Additionally, novice programmers that have a favorable RRA of ChatGPT tend to make more confident and effective decisions, acknowledging potential risks but recognizing that benefits such as quick problem-solving and learning new techniques outweigh these risks. Moreover, a positive perception of ChatGPT’s role in DM significantly increases the inclination to use the tool for programming tasks. These results highlight the critical roles of perceived capabilities, risk assessment, and positive DM experiences in promoting the adoption of artificial intelligence (AI) tools in programming education.
AI-induced fatigue among students in higher education: a latent profile analysis Soriano, Dynah D.; Salenga, Jordan L.; Miranda, John Paul P.; Grume, Juvy C.; Fernando, Emerson Q.; Martinez, Jr., Amado B.; Cabrera, Raymond A.; Yambao, Jaymark A.
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.
Co-Authors Cabrera, Raymond A. Fernando, Emerson Q. Grume, Juvy C. Martinez, Jr., Amado B. Miranda, John Paul P. Salenga, Jordan L. Soriano, Dynah D.