The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot

Abstract The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self‐regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self‐determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self‐directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations. Practitioner notes What is already known about this topic Artificial intelligence (AI) technologies can potentially support students' self‐regulated learning (SRL) of disciplinary knowledge through chatbots. Needs satisfaction in Self‐determination theory (SDT) can explain the directive process required for SRL. Technical and disciplinary knowledge would affect SRL with technologies. What this paper adds This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot. Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot. Autonomy and competence were mediators, but relatedness was not. Implications for practice and/or policy Teachers should use chatbots for knowledge consolidation rather than exploration. Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL. School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.