Scholarlink

<p>This study empirically validates the Human–AI Co-Intelligence Learning (HAIL) framework, which explains how university students convert generative AI support into autonomous, ethical, and effective learning. Using a mixed-methods design, data were collected from 320 undergraduates in Taiwan who had integrated ChatGPT or similar tools into their coursework. Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that AI Support significantly enhanced Trust Calibration, which in turn strengthened Self-Regulated Learning (SRL) and promoted Learning Agency and Outcomes. SRL partially mediated the trust–agency relationship, while Ethical Awareness and Cultural Orientation moderated these effects in opposite directions. Thematic interviews corroborated the quantitative findings, showing that students developed reflective reliance on AI—balancing efficiency with integrity—while hierarchical cultural norms constrained autonomy. The validated HAIL model extends self-regulation and trust theories into AI-mediated education, positioning trust calibration as the cognitive bridge between algorithmic support and human autonomy. The findings provide theoretical and practical guidance for designing human-centered, ethically sustainable, and culturally responsive AI learning environments in higher education.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Human–AI Co-intelligence in higher education: reframing student agency and learning strategies among Taiwanese undergraduates in the ChatGPT era

  • 期刊论文

  • 发表日期：

    2026年4月25日

查看全文
  • Kuo-Ming Chu,
  • Hui-Chun Chan

摘要

This study empirically validates the Human–AI Co-Intelligence Learning (HAIL) framework, which explains how university students convert generative AI support into autonomous, ethical, and effective learning. Using a mixed-methods design, data were collected from 320 undergraduates in Taiwan who had integrated ChatGPT or similar tools into their coursework. Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that AI Support significantly enhanced Trust Calibration, which in turn strengthened Self-Regulated Learning (SRL) and promoted Learning Agency and Outcomes. SRL partially mediated the trust–agency relationship, while Ethical Awareness and Cultural Orientation moderated these effects in opposite directions. Thematic interviews corroborated the quantitative findings, showing that students developed reflective reliance on AI—balancing efficiency with integrity—while hierarchical cultural norms constrained autonomy. The validated HAIL model extends self-regulation and trust theories into AI-mediated education, positioning trust calibration as the cognitive bridge between algorithmic support and human autonomy. The findings provide theoretical and practical guidance for designing human-centered, ethically sustainable, and culturally responsive AI learning environments in higher education.