Exploring the Key Affordances of AI Tutors and Their Influence on Learning Outcomes: Using Structural Equation Modeling and Fuzzy-Set QCA

Abstract

This research investigates the evolving landscape of artificial intelligence (AI) in education, focusing on AI tutors and Intelligent Tutoring Systems (ITS). AI’s rapid advancement, particularly in generative AI and natural language processing (NLP), is reshaping educational methodologies, offering personalized, adaptive learning experiences tailored to individual needs. The study aims to understand the primary affordances of AI tutors and their impact on student performance and learning outcomes. Employing the Delphi method, insights from experts across technology, education, and business domains were amalgamated to identify key affordances intrinsic to AI tutors. The study integrates the Stimulus-Organization-Response (S-O-R) model and affordance process modeling to explore the influence of these affordances on learning outcomes, using structural equation modeling (PLS-SEM) for empirical validation. Additionally, fsQCA methodology was employed to examine the interplay between diverse affordances and student engagements, such as presence, motivation, and their contribution to learner performance. The research findings provide a comprehensive understanding of the role of AI tutors in educational environments. The identified affordances and their relationship with learner outcomes highlight the potential of AI tutors in enhancing educational experiences. This study contributes to the dialogue on AI tutors’ effectiveness, offering insights into their long-term impacts and opportunities and presenting recommendations for integrating AI advancements in education. The collective insights from the studies are anticipated to guide the future development of AI tutors, enhancing their applicability and effectiveness in learning environments.