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All Journal Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education
Ummul Khaeri Masna
Universitas Negeri Makassar
Computer Science & IT Education

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Student Perceptions of AI in Learning: The Role of Credibility and Emo-tional Well-Being in Supporting Critical Thinking Skills Ummul Khaeri Masna; Arum Putri Rahayu; Sakinah Mawaddah; Nurrahmah Agusnaya; Muh. Yusril Anam
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i1.3

Abstract

The growing use of artificial intelligence (AI) tools (e.g., ChatGPT, Grammarly) in higher education is often claimed to enhance students’ critical thinking, yet perceived benefits remain inconsistent and may depend more on trust and affective experience than on technical features alone. This study aimed to examine students’ perceptions of AI for supporting critical thinking by testing five predictors—perceived AI credibility, AI quality, cognitive absorption, emotional well-being, and satisfaction—and their effects on overall AI perception. A quantitative cross-sectional survey was administered to 90 Indonesian university students (purposive sampling; ages 18–25) using 26 closed-ended Likert items (5-point scale) and three open-ended questions; data were analyzed in Jamovi using descriptive statistics, Pearson correlations, and multiple linear regression. The results indicated generally moderate perceptions of AI (item means ≈2.2–2.8), significant positive correlations among all variables (p < .001), and strong explanatory power of the regression model (R² = 0.737; adjusted R² = 0.720). In the multivariate model, emotional well-being (β_std = 0.267, p = 0.016) and AI credibility (β_std = 0.196, p = 0.043) were the only significant predictors, whereas AI quality, cognitive absorption, and satisfaction showed positive but non-significant effects. These findings imply that AI-supported learning interventions should prioritize credible, trustworthy AI outputs and pedagogical designs that promote positive emotional experiences (e.g., comfort, reduced stress, motivation) to strengthen perceived critical-thinking benefits; overall, affective and trust-related factors appear to be central drivers of students’ positive AI perceptions, warranting validation in larger and longitudinal studies
The Concern Over Brain Rot from Generative AI Use Among Preservice Teachers: A UTAUT Approach Ummul Khaeri Masna; Udin Sidik Sidin; Mushaf; Stephen Amukune
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.347

Abstract

Purpose – The increasing use of generative AI on campus has raised concerns about a potential decline in students’ critical thinking skills. While the UTAUT theory is widely used to examine technology adoption, its relationship with the phenomenon of brain rot remains underexplored, particularly among preservice teachers. This study aims to analyze the factors associated with preservice teachers’ intention to use generative AI within the UTAUT framework, as well as to examine its association with tendencies toward brain rot.Method – A quantitative cross-sectional design was conducted with 243 preservice teachers from Universitas Negeri Makassar. Data were collected via a validated 30 item questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between technology adoption constructs and brain rot tendencies.Findings – Social influence was the only significant predictor of behavioral intention to use AI (β = 0.269, p = 0.002). Behavioral intention, in turn, showed a strong positive association with brain rot tendencies (β = 0.817, p < 0.001), explaining 66.7% of the variance (R² = 0.667). Other UTAUT constructs, including performance expectancy and effort expectancy, were not significant predictors. However, given the cross-sectional design, these findings reflect statistical associations rather than causal relationships.Research Implication : Socially driven AI adoption is strongly linked to cognitive passivity, highlighting the need to extend UTAUT with cognitive risk factors and rethink how technology use impacts higher-order thinking.Conclusion – This study indicates that the adoption of AI among preservice teachers is associated with perceptions of declining cognitive abilities. These findings highlight the importance of promoting critical AI literacy and developing assessment approaches that emphasize deep cognitive engagement. Future research is recommended to employ longitudinal designs or incorporate control variables such as digital self-efficacy.
Co-Authors Arum Putri Rahayu Muh. Yusril Anam Mushaf Nurrahmah Agusnaya Sakinah Mawaddah Stephen Amukune Udin Sidik Sidin