An Exploratory Configurational Analysis of Learning-Process Engagement in AI-Supported Learning

This exploratory research examines learning-process engagement in artificial intelligence (AI)-supported courses, measured through students’ self-reported attention, active participation, and thinking during class. Necessary Condition Analysis and fuzzy-set qualitative comparative analysis were applied to questionnaire data from three cohorts at a Taiwanese university of technology, using 90 cases after screening 107 responses for invariant responding. Seventeen respondents selected the same option across all 30 items and were excluded from the main analysis. The study examines five measured learner characteristics and study practices: academic self-concept, learning desire, learning strategies, learning planning, and habitual regulation. In the screened sample, no condition meets the set-theoretic necessity criterion, while four show statistically significant NCA ceiling effects. High engagement is associated with one configuration combining learning desire, strategies, planning, and habitual regulation, with strategies and habitual regulation as core conditions. Low engagement is associated with the joint absence of all five conditions. Among all 107 respondents, no condition met the set-theoretic necessity criterion. Re-estimating the calibration anchors yielded five high-engagement configurations, while holding the original anchors fixed retained the single configuration. For instructors, these patterns offer questions for future study rather than validated rules for choosing support for individual students.