I am a learning designer in the Faculty of Science & Technology at AU. I work closely with faculty and course teams to design high-quality online courses, explore effective digital pedagogies, and deliver professional development. My work integrates learning sciences, educational technology, and instructional design to foster meaningful and flexible learning in online and distance education.
Building on this design work, my research explores innovative technologies such as personalized and adaptive learning, artificial intelligence in education, and educational neuroscience, with a particular interest in human–AI collaborative learning models for self‑paced environments.
Educational credentials
PhD, Computer Science, University of Eastern Finland (Joensuu, Finland)
Master of Arts, Educational Technology, Concordia University (Montreal, Canada)
Master of Engineering, Xi'an University of Science and Technology (Xi'an, China)
Bachelor of Engineering, Shandong University of Technology (Zibo, China)
Yan, H. (2025). Human–AI Integrated Adaptive Practicing to Foster Self-regulated Learning in Online STEM Education (Doctoral dissertation). University of Eastern Finland. Presents a comprehensive model for AI-supported adaptive practice to enhance learner autonomy.
Yan, H., Lin, F., & Kinshuk. (2024). Adaptive Practicing Design to Facilitate Self-Regulated Learning. Canadian Journal of Learning and Technology, 50(3). Introduces the ZPD-KT model and demonstrates its effectiveness in supporting self-regulated learning in STEM.
Yan, H., Lin, F., & Kinshuk. (2022). Removing Learning Barriers in Self-Paced Online STEM Education. Canadian Journal of Learning and Technology, 48(4). Explores the design of intelligent tools to mitigate challenges inherent in self-paced STEM learning.
Yan, H., Lin, F., & Kinshuk. (2021). Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design. International Journal of Artificial Intelligence in Education, 31(4), 878–895. Applies learning analytics to enhance instructional design for self-paced online learning environments.
Yan, H. (2020). Using Learning Analytics and Adaptive Formative Assessment to Support At-risk Students in Self-paced Online Learning. IEEE International Conference on Advanced Learning Technologies. Examines the use of analytics-driven adaptive assessment for supporting vulnerable learner groups.