Determinants of Continuance Intention Toward Generative AI for Learning and Skill Development: The Mediating Roles of Perceived Learning Effectiveness and Trust in AI

Abstract

This paper will analyze the complex issues which govern the continued desire of users to engage in generative artificial intelligence (GenAI) into their learning process and skill acquisition, including those of performance and ethical implications. The research is based on a comprehensive framework examining the immediate impacts of the quality of AI support, AI self-efficacy, transparency, perceptions of algorithmic prejudice, and privacy issues, and the dual mediating influences of perceived learning efficiency and trust in institutions. The study took the quantitative approach to research and the evidence gathered was empirical based on a structured online questionnaire (230/230 valid). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for the statistical analysis, carried out in the SmartPLS (version 3.2.8) software environment. The structural model estimations findings reveal that the quality of the AI assistance, as well as the technological self-efficacy, have significant, positive, and statistically significant effects on the long-term continuance intentions, and both effects are significantly mediated by the perceived learning effectiveness. Conversely, the findings indicate a transparency paradox: Transparency regarding AI can assist in establishing user trust, but paradoxically, under specific circumstances, it will prompt users to reduce their likelihood of keeping using AI systems. Furthermore, the total privacy issue impact had little to do with constancy usage, and that bias perception was unexpectedly positive with continuance intention, showing that users had proactively adjusted to the identified restrictions of the algorithm. Conclusively, this research has offered a distinctive addition to the existing body of knowledge by integrating functional utility and ethical responsibility into one model with practical and strategic implications to software developers, educators and policy makers in the higher education sector who aim to ensure the responsible and sustainable use of AI in education.