Beyond Task Completion: A Theoretical Integration and Framework for Guiding Students’ ChatGPT Use for Learning

Abstract

ChatGPT’s intuitive interface and instant feedback can either support learning or enable superficial task completion, depending on how it is integrated into academic work. While generative AI has rapidly entered higher education, much existing guidance focuses on policy and academic integrity rather than explaining how AI use interacts with established mechanisms of learning. This conceptual paper contributes to this discussion by developing an explanatory framework for students’ ChatGPT use grounded in foundational learning theories. Drawing on cognitive load theory and goal orientation theory and incorporating self-efficacy and task relevance as mediating constructs, the study undertakes a theory-driven synthesis of four perspectives relevant to how students engage with complex academic tasks. The framework brings these perspectives together to clarify how cognitive demands, motivational orientations, perceived competence, and perceived task value may shape students’ decisions about when and how to rely on generative AI tools during academic work. The resulting framework illustrates how these mechanisms may influence whether generative AI is used as a scaffold supporting thinking and knowledge construction or as a shortcut that bypasses essential cognitive processes. The article concludes by illustrating how the framework may inform instructional design that aligns AI use with meaningful engagement in disciplinary tasks in higher education.