How generative artificial intelligence tools support self-directed learning: Implications for students’ learning motivation and design thinking

Abstract

Generative artificial intelligence (GenAI) tools have introduced promising opportunities for enhancing learning and automated assessment. Self-directed learning (SDL) and a design thinking mindset (DTM) have also been recognized as promising pedagogical approaches that support and contribute to advancing the Sustainable Development Goals. Although prior research has suggested that components of SDL may influence motivation and that motivation may be associated with DTM ability, there remains a noticeable gap in empirical studies examining the interrelationships among these variables. Grounded in the principles of SDL, the present study investigates the use of GenAI tools in assessing students’ DTM. A quantitative study was conducted with a sample of 379 university students, and the data collected through validated measurement instruments were analyzed using structural equation modeling to test and verify the proposed structural model. Empirical findings revealed that the use of GenAI tools for assessment significantly influenced all components of SDL, ultimately leading to enhanced learning motivation. Furthermore, awareness, learning strategies, learning activities, and interpersonal skills were found to mediate the relationship between the use of GenAI tools in assessment and students’ learning motivation. Interestingly, students’ self-assessment did not significantly affect learning motivation and did not play a mediating role between GenAI tools-based assessment and motivation. A particularly noteworthy finding is the absence of a significant relationship between motivation and DTM. This study highlights the role of GenAI tools in relation to students’ learning motivation and the development of their DTM, offering important implications for policies governing virtual learning environments and for instructional practices.