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<p>The advancement of artificial intelligence (AI) necessitates fostering students’ self-regulated learning (SRL) skills to sustain autonomy in human-AI collaborative (HAC) decision-making. This study examines the role of AI teammates in influencing SRL behaviors and academic performance in a simulated business decision-making environment. Using a sample of 176 undergraduate and graduate students, the research analyzes temporal dynamics and interaction patterns of SRL behaviors across different performance groups. Key findings reveal that AI teammates significantly enhance academic performance with distinct SRL behavior patterns. High-performing students exhibit higher levels of collaboration with AI, utilizing it for strategic planning and optimization, while low-performing students demonstrate resistance and reliance on manual processes. These insights underscore the importance of tailoring AI-assisted educational designs to different learner needs, emphasizing the role of SRL in cultivating digital literacy. Practical implications include recommendations for task complexity, AI integration, and instructional strategies to optimize human-AI collaboration in education.</p>

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Effects of AI teammates on learning behavior in Human-AI collaboration environments: a perspective on self-regulated learning

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Fangcong Zhang,
  • Juanqiong Gou,
  • Kathy Ning SHEN,
  • Luis M. Camarinha-Matos,
  • Zhe Wang

摘要

The advancement of artificial intelligence (AI) necessitates fostering students’ self-regulated learning (SRL) skills to sustain autonomy in human-AI collaborative (HAC) decision-making. This study examines the role of AI teammates in influencing SRL behaviors and academic performance in a simulated business decision-making environment. Using a sample of 176 undergraduate and graduate students, the research analyzes temporal dynamics and interaction patterns of SRL behaviors across different performance groups. Key findings reveal that AI teammates significantly enhance academic performance with distinct SRL behavior patterns. High-performing students exhibit higher levels of collaboration with AI, utilizing it for strategic planning and optimization, while low-performing students demonstrate resistance and reliance on manual processes. These insights underscore the importance of tailoring AI-assisted educational designs to different learner needs, emphasizing the role of SRL in cultivating digital literacy. Practical implications include recommendations for task complexity, AI integration, and instructional strategies to optimize human-AI collaboration in education.