Wei-Lun Huang
Papers
1
Total Citations
6
H-Index
1
About
Dr. Wei-Lun Huang is a rising scholar at the intersection of artificial intelligence in education (AIED), technical education, and motivational psychology. Their most cited work, "Integrating Motivation Theory into the AIED Curriculum for Technical Education" (2025, 6 citations), pioneers a framework that embeds established motivation constructs—such as self-determination and expectancy-value theory—directly into AI-driven curricula. By demonstrating that computer self-efficacy significantly moderates the relationship between curriculum design and learning outcomes, Dr. Huang provides actionable insights for educators seeking to sustain student engagement in AI learning pathways. This contribution is particularly timely as technical education grapples with high dropout rates in AI-related courses. Though early in their career, Dr. Huang’s research already bridges a critical gap between pedagogical theory and technological implementation, offering evidence-based strategies for designing AI curricula that not only teach technical skills but also foster intrinsic motivation. Their work holds promise for shaping how future generations of engineers and technologists are trained, making Dr. Huang a notable voice in the evolving discourse on human-centered AI education.
Research Focus
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Top Papers
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