A Configurational Approach to Investigating the Use of Artificial Intelligence in Self‐Regulated Learning From Needs Satisfaction, Learning Environments and Perceived Affordances

ABSTRACT Background Integrating artificial intelligence (AI) tools into self‐regulated learning (SRL) is complex and multifaceted. Configurational approaches are based on the concept that combinations (or configurations) of causally relevant factors should be connected to the desired outcome. Objectives Self‐determination theory (SDT), the unified theory of acceptance and use of technology (UTAUT) and perceived affordances focus on basic needs, technology adoptions and technological designs, respectively. Taking a configurational approach, this study investigates how the combinations of SDT, UTAUT and perceived affordances affect SRL with AI. Methods We employed fuzzy‐set qualitative comparative analysis (fsQCA) to uncover the complex interplay of factors that facilitate effective SRL. Data from 248 students reveal that a combination of autonomy satisfaction, relatedness satisfaction and performance expectancy is the most influential factor affecting the use of AI in SRL. Results and Conclusions Our results suggest that both innate psychological needs and external learning environments should be considered together for understanding the adoption of AI in SRL. They also further highlight the critical role of perceived affordances as mediators and moderators, enhancing the relationships between innate psychological needs and external learning environments. This integrative approach provides valuable insights into the design and implementation of AI‐supported SRL environments. We recommend designing the environments to foster autonomy and relatedness while also ensuring ease of use for learners.