Work

Exploring the relationships among perceived AI ability, academic self-efficacy and independent learning disposition in the tertiary contexts

Year: 2025

Type: article

Abstract: As generative AI (GenAI) becomes increasingly embedded in higher education, this study examines how students' perceived AI ability (AIA) relates to their independent learning disposition (INL) and academic self-efficacy (ASE). We administered a quantitative survey to 302 undergraduate students in Hong Kong. Results indicated significant positive correlations among AIA, INL, and ASE. Mediation analysis further showed that INL mediates the association between AIA and ASE: students who perceive themselves as more capable with AI also report stronger independent learning dispositions, which in turn are linked to higher academic self-efficacy. We discuss the potential of AI tools to scaffold self-directed learning and strengthen students’ academic confidence, outline pedagogical implications for embedding AI competency training in curricula, and propose directions for future research.

Source: Computers and Education Artificial Intelligence

DOI: 10.1016/j.caeai.2025.100516

Authors: Lok Ming Eric Cheung, On-Ting Lo, Huiwen Shi

Institutions: Hong Kong Polytechnic University, Lingnan University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,500