Dr. Di Fan, a prominent researcher at Northeastern University, Shenyang, specializes in big data mining and recommendation systems, with a strong focus on artificial intelligence applications in education. Her research advances data-driven behavior modeling, interpretability analysis, and the construction of personalized learning environments through AI and large language models (LLMs). As a core member of several National Natural Science Foundation of China key projects, she has contributed to studies on learning portrait technology and interactive educational systems. Dr. Fan leads the 2025 Youth Artificial Intelligence Education Project, developing dynamic assessment methods for digital literacy using generative AI. She holds multiple invention patents in knowledge tracking and AI-driven recommendation methods and has played a significant role in formulating local standards for industrial software and cloud computing skills training. Her publications span reputed venues such as JCST, IEEE ISPA, APWeb, and Bench2024, reflecting expertise in explainable AI and multitask learning. Recognized through numerous national awards, including the CCF Outstanding Communicator and National Scholarship honors, Dr. Fan also serves on several executive committees of the China Computer Federation and international editorial boards, contributing actively to advancing AI education, data science, and public technology outreach.
Profile: Orcid
Featured Publications
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Fan, D., Zhang, T., Li, F., Wu, W., Yu, Y., & Yu, G. (2025). Research on multidimensional evaluation technology of teachers’ digital literacy for LLM as a judge. In Proceedings of Bench2024 (Book chapter). Springer. https://doi.org/10.1007/978-981-96-6310-1_7
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Fan, D., & Li, F. (2024). A survey of static and temporal explainable methods and their applications in knowledge tracing. Journal of Computer Science and Technology (JCST), CCF-B category.
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Fan, D., Zhang, T., & Yu, G. (2024). An LLM augmentation and multitask learning-based recommendation model for MOOCs. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), CCF-C category. IEEE.
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Fan, D., Wu, W., Yu, Y., & Zhang, T. (2024). ProteinMM: Adaptive multi-view and task-grouped evolutionary learning for prediction of protein structural features. In Proceedings of the Asia-Pacific Web Conference (APWeb), CCF-C category.