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<p>The rapid advancement of artificial intelligence (AI) has presented transformative opportunities for education, particularly in addressing the limitations of traditional lecture-based teaching methods commonly employed in large-class settings. While these methods efficiently deliver content, they often fail to foster active engagement, critical thinking, and collaborative learning. Grounded in Constructivism Learning Theory, this study explores the potential of AI to enhance student-centered learning and overcome the inherent challenges of large-class instruction in higher education. Conducted across three universities in China and Malaysia from 2023 to 2024, this quasi-experimental research introduces a novel AI-empowered, student-centered teaching strategy (Active Learning, Situational Discussion, Inductive Teaching, Feedback). Designed to foster engagement, adaptability, and deeper learning in large-class settings, ASIF teaching strategy integrates AI to create dynamic, personalized, and interactive educational environments. Results demonstrate that the AI-empowered ASIF teaching strategy significantly enhances learning outcomes compared to traditional methods. This research demonstrates the potential of the AI-empowered ASIF teaching strategy to address the limitations of traditional teaching methods in large-class contexts, offering a scalable, student-centered solution that enhances educational outcomes. Additionally, it advances Constructivism Learning Theory by expanding its practical applications in modern education, establishing new theoretical and pedagogical frontiers.</p>

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A Novel AI-Empowered, Student-Centered Teaching Strategy for Large Classes in Higher Education

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Xin Meng,
  • Bo Yang,
  • Lianrong Yang,
  • Jian Zhang,
  • Ying Liu

摘要

The rapid advancement of artificial intelligence (AI) has presented transformative opportunities for education, particularly in addressing the limitations of traditional lecture-based teaching methods commonly employed in large-class settings. While these methods efficiently deliver content, they often fail to foster active engagement, critical thinking, and collaborative learning. Grounded in Constructivism Learning Theory, this study explores the potential of AI to enhance student-centered learning and overcome the inherent challenges of large-class instruction in higher education. Conducted across three universities in China and Malaysia from 2023 to 2024, this quasi-experimental research introduces a novel AI-empowered, student-centered teaching strategy (Active Learning, Situational Discussion, Inductive Teaching, Feedback). Designed to foster engagement, adaptability, and deeper learning in large-class settings, ASIF teaching strategy integrates AI to create dynamic, personalized, and interactive educational environments. Results demonstrate that the AI-empowered ASIF teaching strategy significantly enhances learning outcomes compared to traditional methods. This research demonstrates the potential of the AI-empowered ASIF teaching strategy to address the limitations of traditional teaching methods in large-class contexts, offering a scalable, student-centered solution that enhances educational outcomes. Additionally, it advances Constructivism Learning Theory by expanding its practical applications in modern education, establishing new theoretical and pedagogical frontiers.