Artificial Intelligence‐Supported Student Engagement Research: Text Mining and Systematic Analysis
Xieling Chen, Haoran Xie, S. Joe Qin, Fu Lee Wang, Yinan Hou
Abstract
ABSTRACT Artificial intelligence (AI) is increasingly exploited to promote student engagement. This study combined topic modelling, keyword analysis, trend test and systematic analysis methodologies to analyse AI‐supported student engagement (AIsE) studies regarding research keywords and topics, AI roles, AI systems and algorithms, methods and domains, samples and outcomes. Findings included the following: (1) frequent‐used and emerging keywords comprised ‘machine learning’, ‘artificial intelligence chatbot’ and ‘collaborative knowledge building’. (2) Frequently studied topics included ‘AI for MOOCs and self‐regulated learning’ and ‘affective computing and emotional engagement’. (3) Most studies adopted intelligent tutoring systems, traditional machine learning methods and natural language processing. (4) Emotional engagement regarding affective or psychological states among college students received the most attention. (5) Most studies adopted quantitative approaches and concerned computer science and language education. Accordingly, we highlighted AI's roles as tutors, advisors, partners, tutees and regulators for behavioural, cognitive and emotional engagement to inspire AI's effective integration into education.