From enhancement to over-reliance: a mixed-method study of generative AI and sustainable learning performance

The rapid integration of generative artificial intelligence (AI) in education has raised important questions regarding its impact on student learning outcomes. While prior research has primarily focused on AI adoption, limited attention has been given to its influence on sustainable learning performance and the potential risks associated with its use. Addressing this gap, this study develops a comprehensive model to examine both the positive and negative effects of AI use on learning by integrating AI literacy, self-regulated learning, cognitive offloading, and individual differences. A mixed-method approach was employed, combining time-lagged survey data collected in three waves from 623 university students in China with qualitative interviews conducted with educators. The quantitative data were analyzed using PLS-SEM, while qualitative data were examined through thematic analysis to provide deeper insights into the observed relationships. The findings reveal that AI literacy significantly enhances critical AI evaluation, which, along with self-regulated learning, promotes effective AI use. Effective AI use positively influences sustainable learning performance but also increases AI over-reliance, which negatively affects learning outcomes. Furthermore, polychronicity moderates key relationships, indicating that multitasking tendencies shape both AI dependency and learning effectiveness. The qualitative findings support and explain these results, highlighting the behavioral mechanisms underlying AI-supported learning. Overall, the study demonstrates that the impact of AI on learning is dual in nature and depends on how students engage with the technology, offering important implications for theory and educational practice.