The Role of AI-Based Technologies in Higher Education: A Comparative Study of Student Perceptions in India and Indonesia
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Abstract
Purpose: This study investigates the role of Artificial Intelligence (AI)-based technologies in higher education, comparing student perceptions and adoption patterns in India and Indonesia. It aims to evaluate how institutional, ethical, and technological factors influence learning, skill development, and the overall transformative potential of AI in educational contexts.
Design/Methodology/Approach: A quantitative, cross-sectional research design was adopted, using data from 120 higher education students across both countries. A structured questionnaire measured constructs such as Institutional Adoption, Support and Accessibility, Learning and Skill Development, Ethical and Equity Concerns, and Data Privacy. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to assess measurement reliability, convergent validity, and structural relationships among the constructs.
Findings: Results reveal that institutional adoption and personalization strongly enhance support and accessibility (β = 0.234, p < 0.01; β = 0.295, p < 0.001), which in turn positively influence learning and skill development (β = 0.479, p < 0.001). Ethical concerns (β = –0.202, p < 0.05) and perceived disruptions to academic roles (β = –0.230, p < 0.05) negatively affect students’ perceptions of AI’s general impact. In contrast, data privacy and future integration factors were not significant predictors. Students express optimism regarding AI’s role in personalized learning but remain cautious about ethical and pedagogical implications.
Practical Implications: Findings suggest that effective institutional adoption, coupled with transparent ethical frameworks, is key to maximizing AI’s educational benefits in emerging economies.
Originality/Value: By integrating descriptive and structural modeling, this study contributes comparative empirical evidence from India and Indonesia, highlighting both opportunities and ethical tensions in AI-driven higher education.
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References
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