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<p>This study moves beyond asking whether generative AI (GenAI) improves learning to examine who engages, how they engage, and what kinds of questions they ask. We investigated how graduate students used a GenAI chatbot designed based on Retrieval-Augmented Generation (RAG) architecture to support statistics learning and how engagement patterns associated with learner characteristics, motivation, and academic growth. Drawing on 2,819 chatbot queries across a semester, motivational measures, and performance data from 97 students, we modeled engagement using a zero-inflated negative binomial (ZINB) framework and identified performance gain profiles through <i>k</i>-means clustering. More than one-quarter of students did not use the chatbot at all, highlighting meaningful non-participation. Engagement frequency was shaped by learner differences: female students, those with stronger autonomous motivation, lower prior knowledge, and higher course performance interacted more frequently, while higher-performing students were less likely to abstain from use. Importantly, engagement was not merely a matter of frequency but of inquiry quality and diversity. Growing Achievers (students who began with weaker achievement but demonstrated substantial performance gains) posed more diverse and conceptually oriented questions. In contrast, Declining Performers engaged minimally and focused on narrower, procedural inquiries despite stronger initial knowledge. These findings suggest that GenAI tools function differently across learner subpopulations and can either amplify growth or remain underutilized depending on motivational and achievement trajectories. The observed variability in student engagement profiles indicates that the efficacy of AI integration depends on moving beyond uniform technological access toward strategies that prioritize autonomous motivation and scaffold concept-focused AI support tailored to individual learner trajectories. Such supports are essential to ensure shared human-AI agency so that AI integration expands learning opportunities and promotes upward academic mobility instead of concentrating benefits among already advantaged learners.</p>

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Differential engagement with generative artificial intelligence in higher education: Gender, motivation, and achievement trajectories

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Yuan-Hsuan Lee,
  • Jiun-Yu Wu

摘要

This study moves beyond asking whether generative AI (GenAI) improves learning to examine who engages, how they engage, and what kinds of questions they ask. We investigated how graduate students used a GenAI chatbot designed based on Retrieval-Augmented Generation (RAG) architecture to support statistics learning and how engagement patterns associated with learner characteristics, motivation, and academic growth. Drawing on 2,819 chatbot queries across a semester, motivational measures, and performance data from 97 students, we modeled engagement using a zero-inflated negative binomial (ZINB) framework and identified performance gain profiles through k-means clustering. More than one-quarter of students did not use the chatbot at all, highlighting meaningful non-participation. Engagement frequency was shaped by learner differences: female students, those with stronger autonomous motivation, lower prior knowledge, and higher course performance interacted more frequently, while higher-performing students were less likely to abstain from use. Importantly, engagement was not merely a matter of frequency but of inquiry quality and diversity. Growing Achievers (students who began with weaker achievement but demonstrated substantial performance gains) posed more diverse and conceptually oriented questions. In contrast, Declining Performers engaged minimally and focused on narrower, procedural inquiries despite stronger initial knowledge. These findings suggest that GenAI tools function differently across learner subpopulations and can either amplify growth or remain underutilized depending on motivational and achievement trajectories. The observed variability in student engagement profiles indicates that the efficacy of AI integration depends on moving beyond uniform technological access toward strategies that prioritize autonomous motivation and scaffold concept-focused AI support tailored to individual learner trajectories. Such supports are essential to ensure shared human-AI agency so that AI integration expands learning opportunities and promotes upward academic mobility instead of concentrating benefits among already advantaged learners.