Prof. DAI Yun

B.A.(PKU), M.Phil.(PKU), Ph.D.(UCSB)

Assistant Professor
Prof. DAI Yun

Prof. DAI Yun

Assistant Professor

HTB Rm. 405
3943 6719


Introduction
Dr. Dai is an Assistant Professor of Curriculum and Instruction at The Chinese University of Hong Kong (CUHK). Before joining CUHK, she served as a Postdoctoral Fellow in engineering education at the Viterbi School of Engineering, University of Southern California. Her work explores the evolving intersection of technology, education, and human development. She is particularly interested in developing innovative theoretical and methodological frameworks to address the challenges posed by technological advancements. Her current research focuses on (1) AI literacy and ethics, (2) human agency in AI-augmented environments, and (3) AI as an epistemic tool in engineering design. Her work on AI literacy earned recognition in 2023 as the Most Cited Article and Most Downloaded Article in the Journal of Engineering Education—the flagship publication of the American Society for Engineering Education.

Beyond her academic work, Dr. Dai is committed to community outreach. Since 2022, she has led an AI education initiative, AI Literacy for All (AILFA), which provides accessible and inclusive AI education to underprivileged students in Hong Kong and Shenzhen. She is also an expert reviewer for AI curriculum development in cities such as Beijing and Shenzhen.
Research Areas
AI literacy, ethics, engineering education, learning technology
Courses
PEDU 6126: Special Topics in STEAM Education and Research
PEDU 6052: Curriculum Evaluation: Theory & Practice
PEDU 6004: Ethnographic Study in Education Research
Grants and Funding
  • PI. Early Career Scheme, Research Grant Council. Fostering quality online adaptive help-seeking through an analytics-enhanced course forum in large college classes. 2022-2024.
  • Co-I. Knowledge Transfer Project Fund, Chinese University of Hong Kong. Empowering Youth from Underprivileged Community: Enhancing Programming Skills and Mathematical Knowledge for STEM Teaching and Learning. 2024-2025.
  • PI. Direct Grant at the Chinese University of Hong Kong. Investigating upper primary students’ misconceptions and learning trajectories in an introductory artificial intelligence course. 2023.
  • Co-I. Quality Education Fund, HK Education Burea. The curriculum development of artificial intelligence and education in Hong Kong schools. 2021-2022.
  • PI. Direct Grant at the Chinese University of Hong Kong. Design-based research on student-facing learning analytics and pedagogical supports for self-regulated learning in online education. 2019-2021.
  • PI. Women in Engineering Visiting Funding Scheme, University of New South Wales, Australia, 2019.
Selected Publications
Please refer to my Google Scholar profile for a complete and up-to-date list of my publications.

AI Literacy and Pedagogy
  1. Dai, Y. (2024). Integrating unplugged and plugged activities for holistic AI education: An embodied constructionist pedagogical approach. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13043-w [SSCI Q1]
  2. Dai, Y. (2024). Dual-contrast pedagogy for AI literacy in upper elementary schools. Learning and Instruction, 91 (2024), 101899. https://doi.org/10.1016/j.learninstruc.2024.101899 [SSCI Q1]
  3. Wen, X., Ye, H., Dai, Y., & Ng, O. (2024). Integrating artificial intelligence and computational thinking in educational contexts: A systematic review of instructional design and student learning outcomes. Journal of Educational Computing Research, 62(2), 1640-1670. https://doi.org/10.1177/07356331241248686 [SSCI Q1]
  4. Dai, Y., Lin, Z., Liu, A., Dai, D., & Wang, W. (2023). Effect of an analogy-based approach of artificial intelligence pedagogy in upper primary schools. Journal of Educational Computing Research, 61(8), 159-186. https://doi.org/10.1177/07356331231201342 [SSCI Q1]
  5. Dai, Y., Lin, Z., Liu, A., & Wang, W. (2023). An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study. British Journal of Education Technology, 55, 417-434. https://doi.org/10.1111/bjet.13371 [SSCI Q1]
  6. Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M. S. Y., Chai, C.S., & Lin, Z. (2023). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, 112 (1), 23-42. https://doi.org/10.1002/jee.20503 [SSCI Q1]
  7. Dai, Y. (2023). Negotiation of epistemological understandings and teaching practices between primary teachers and scientists about artificial intelligence in professional development. Research in Science Education, 53(3), 577-591. https://doi.org/10.1007/s11165-022-10072-8 [SSCI Q1]
  8. Xia, Q., Chiu T. K. F., Lee, M., Temitayo I., Dai, Y., & Chai, C.S. (2022). A Self-determination theory design approach for inclusive and diverse Artificial Intelligence (AI) K-12 education. Computers & Education, 189. https://doi.org/10.1016/j.compedu.2022.104582 [SSCI Q1]

Learning Science and Technology
  1. Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2024). University policies on generative AI in Asia: Promising practices, gaps, and future directions. Journal of Asian Public Policy. https://doi.org/10.1080/17516234.2024.2379070 [SSCI Q1]
  2. Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2023). ChatGPT and its impact on research supervision: Insights from Australian postgraduate research students. Australasian Journal of Educational Technology, 39(4), 74–88. https://doi.org/10.14742/ajet.8843 [SSCI Q1]
  3. Dai, Y., Liu, A., & Lim, C.P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Procedia CIRP, 119, 84-90. https://doi.org/10.1016/j.procir.2023.05.002 [Scopus]
  4. Dai, Y. (2019). Situating videoconferencing in a connected class toward intercultural knowledge development: A comparative reflection approach. The Internet and Higher Education, 41, 1-10. https://doi.org/10.1016/j.iheduc.2018.11.001 [SSCI Q1]
  5. Dai, Y. & Liu, A. (2019). Understanding student variances in learning outcomes and task interpretations from multimedia presentations. British Journal of Education Technology, 50(5), 2685-2702. https://doi.org/10.1111/bjet.12715 [SSCI Q1]
  6. Dai, Y., Lu, S., & Liu, A. (2019). Student pathways to understanding the global virtual teams: An ethnographic study. Interactive Learning Environments, 27(1), 3-14. https://doi.org/10.1080/10494820.2018.1448286 [SSCI Q1]

Engineering Design and Education
  1. Dai, Y., Lin, Z.Y., & Liu, A. (2024). Facilitating Students’ Help-seeking and Peer Interactions in Undergraduate Design Education through an Analytics-enhanced forum. Procedia CIRP. https://doi.org/10.1016/j.procir.2024.06.024
  2. Xin, H., Liu, A., & Dai, Y. (2024). Combining ChatGPT and knowledge graph for explainable machine learning-driven design: a case study. Journal of Engineering Design, 0(0) 1–31. https://doi.org/10.1080/09544828.2024.2355758 [SCI Q1]
  3. Tian, Y., Liu, A., Dai, Y., Nagato, K., & Nakao, M. (2024). Systematic synthesis of design prompts for large language models in conceptual design. CIRP Annals. https://doi.org/10.1016/j.cirp.2024.04.062 [SCI Q2]
  4. Hu, X., Liu, A., Li, X., Dai, Y., & Nakao, M. (2023). Explainable AI for customer segmentation in product development. CIRP Annals. https://doi.org/10.1016/j.cirp.2023.03.004 [SCI Q2]
  5. Wang, X.Z., Anwer N., Dai, Y. & Liu, A. (2023). ChatGPT for design, manufacturing, and education. Procedia CIRP, 119, 7-14. https://doi.org/10.1016/j.procir.2023.04.001
  6. Dai, Y. (2021). Blended Learning for Intercultural Competence: A Case Study in Engineering Education. In: Lim, C.P., Graham, C.R. (eds) Blended Learning for Inclusive and Quality Higher Education in Asia. Springer, Singapore. https://doi.org/10.1007/978-981-33-4106-7_11
  7. Dai, Y., Liu, A., Morrison, J., & Lu, S. (2016). Systemic design of interactive learning environment for global engineering courses. International Journal of Engineering Education, 32(6), 2597-2611.