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All Journal Daengku: Journal of Humanities and Social Sciences Innovation
Sahabuddin Sahabuddin
Universitas Negeri Makassar, Makassar, 90222, Indonesia
Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other

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Trust-Mediated AI Continuance Intention among Pre-Service Teachers: Integrating UTAUT and the Extended S-O-R-S Framework with AI Brain-Rot Exposure M. Miftach Fakhri; Andika Isma; Sahabuddin Sahabuddin; Pramudya Asoka Syukur; Rosidah Rosidah
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4849

Abstract

The rapid use of artificial intelligence (AI) in teacher education raises important concerns about whether pre-service teachers will continue using AI despite emerging risks such as perceived AI brain-rot exposure. Therefore, this study examines how UTAUT-related stimuli, institutional support, and perceived AI brain-rot exposure influence the intention to continue using AI through trust in AI. This study employed a quantitative cross-sectional survey design involving 247 pre-service teachers enrolled in teacher education programmes in Indonesia, all of whom had prior experience using AI for academic or teaching-related purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling. The results showed that performance expectancy and social influence significantly increased trust in AI, whereas effort expectancy and institutional support did not significantly influence trust. Perceived AI brain-rot exposure also significantly influenced trust in AI, but the relationship was positive, suggesting that awareness of AI-related cognitive risks may coexist with selective or calibrated trust. Trust in AI strongly influenced continuance intention and mediated the effects of performance expectancy, social influence, and perceived AI brain-rot exposure on the continuance intention. The model explained 72.1% of the variance in trust in AI, and 62.6% of the variance in continuance intention. This study contributes to the literature by extending the UTAUT and S–O–R with a stressor perspective and by introducing perceived AI brain-rot exposure as an emerging construct in AI-in-education adoption research. These findings suggest that teacher education programmes should prioritize demonstrating AI's concrete pedagogical benefits and fostering reflective AI literacy to build trust, rather than relying solely on institutional policy or ease-of-use considerations.
From Growth Mindset and Social Media Influence to Learning Outcomes: A PLS-SEM Study in Indonesian Higher Education Andika Isma; Andi Naila Quin Azsisah Alisyahbana; Sahabuddin Sahabuddin; Akhmad Khairul Shiddiq; Della Fadhilatunisa
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4850

Abstract

This study examines how growth mindsets and social media influence shape learning outcomes through student engagement and digital literacy in digitally mediated higher education. Survey data were collected from 478 undergraduate students enrolled at higher education institutions in Indonesia and analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. The structural model explained 67.3% of the variance in learning outcomes, 60.5% in student engagement, and 40.7% in digital literacy, indicating a substantial explanatory power. Growth mindset positively predicted learning outcomes, student engagement, and digital literacy, with the strongest substantive effect observed on student engagement. Social media influence positively predicted student engagement and digital literacy but did not have a significant direct effect on the learning outcomes. Student engagement and digital literacy both positively predicted learning outcomes, and indirect-effect analysis confirmed several mediating pathways linking growth mindset and social media influence to learning outcomes. These findings indicate that the academic benefits of digital higher education are not produced by mindset or social media exposure alone. Learning outcomes improve when psychological dispositions and social-digital interactions are translated into active engagement and effective digital literacy.
Co-Authors Akhmad Khairul Shiddiq Andi Naila Quin Azsisah Alisyahbana Andika Isma Andika Isma Della Fadhilatunisa M. Miftach Fakhri Pramudya Asoka Syukur Rosidah Rosidah