Artificial Intelligence Use and Emotional Well-Being in  Higher Education: A Life-Course Perspective on  Technology Acceptance and Trust

Nailha Dinda Aprilia; Kartika Ratna Sari; Putri Nirmala; Rosidah; Shera Afidatunisa

doi:10.66053/aillce.v1i1.5

Authors

Nailha Dinda Aprilia Universitas Negeri Makassar
Kartika Ratna Sari Universitas Negeri Makassar
Putri Nirmala Universitas Negeri Makassar
Rosidah Universitas Negeri Makassar
Shera Afidatunisa Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.66053/aillce.v1i1.5

Keywords:

Technology Acceptance, Trust in AI, Artificial intelligence in education, Emotional well-being, Life-course education

Abstract

Purpose – The growing integration of artificial intelligence (AI) in higher education has reshaped students’ cognitive and emotional learning experiences. From a life-course education perspective, higher education represents a critical phase of early adulthood in which interactions with AI may influence emotional regulation and readiness for lifelong learning. However, empirical studies examining the affective consequences of AI use through technology acceptance and trust mechanisms remain limited. This study investigates how AI usage frequency, perceived usefulness, perceived ease of use, and trust in AI influence university students’ emotional well-being.
Design/methods/approach – A quantitative cross-sectional survey was administered to university students who actively used AI to support their learning activities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the direct effects of technology acceptance factors and trust in AI on emotional well-being.
Findings – The results indicate that AI usage frequency and trust in AI have significant positive effects on students’ emotional well-being. In contrast, perceived usefulness and perceived ease of use do not directly influence emotional well-being. These findings suggest that affective benefits of AI-supported learning are shaped more by familiarity and psychological trust than by technical efficiency alone.
Research implications/limitations – The cross-sectional design, reliance on self-reported measures, and single-institution sample limit causal interpretation and generalizability. Future studies are encouraged to adopt longitudinal or mixed-method approaches to capture emotional dynamics across educational stages.
Originality/value – This study extends the Technology Acceptance Model by positioning emotional well-being as a key outcome within a life-course framework, offering insights into how AI interaction during early adulthood may support psychological sustainability and lifelong learning readiness

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