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Affective Dynamics and Ethics of AI Use among Higher Education Students: A PLS-SEM Study Nabilah Auliah Rahman; Melda Auliyah Zakina; Aprilianti Nirmala S; Saipul Abbas
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

The use of artificial intelligence (AI) in higher education is increasing rapidly, raising questions about how emotional well-being, AI credibility, and AI interaction quality shape students’ affective engagement and ethical awareness. This study employs a quantitative cross-sectional design and analyzes data using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that emotional well-being (β = 0.549, p < 0.001) and AI interaction quality (β = 0.420, p < 0.001) significantly affect affective engagement, while AI credibility has no significant effect (β = –0.045, p = 0.342). Affective engagement significantly influences ethical awareness (β = 0.597, p < 0.001) and mediates the effects of emotional well-being and interaction quality. The model explains substantial variance in affective engagement (R² = 0.561) and moderate variance in ethical awareness (R² = 0.357). These findings highlight the importance of emotional and interactional factors in fostering ethical awareness and support the need for human-centered and ethically grounded AI integration in education.
The Effect Of Ai Literacy, Ethics, And Motivation On Student Learning Gains Shofiyah Rosyadah; Ahmad Siddiq Mappatunru; Aprilianti Nirmala S; M. Miftach Fakhri
Jurnal Pendidikan Terapan Vol 3, No 3 September (2025)
Publisher : Sakura Digital Nusantara

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Abstract

The increase in the use of artificial intelligence (AI) in higher education is happening faster than the readiness of literacy and ethical frameworks, thus creating a need to understand the factors that influence the effectiveness of AI utilization on student learning outcomes. This study aims to examine the influence of AI Literacy, AI Ethical Awareness, and Motivation to Learn with AI on Learning Gains and to identify the most dominant predictors. The study used a cross-sectional quantitative design with a sample of university students in Makassar selected through purposive sampling. The measurement of motivation adapted some items from the Academic Motivation Scale (AIMS) that had been psychometrically tested prior to structural analysis. The model was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed that the three independent variables had a positive and significant effect on Learning Gains, with coefficients β = 0.208 for AI Literacy, β = 0.236 for AI Ethical Awareness, and β = 0.358 for Motivation to Learn with AI. The R² value of 0.532 indicates the model's explanatory power in the moderate category. The f² effect size shows that motivation makes the largest contribution (0.329), while AI Literacy and AI Ethical Awareness have a small effect. Thus, motivation emerges as the strongest predictor, confirming that the successful integration of AI in learning depends not only on technical competence and ethical awareness, but also on the affective dimension of students. These findings contribute to the development of AIED studies and motivation theory, and emphasize the importance of educational strategies that balance literacy, ethics, and motivational support.