Lilian Gong
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Mail code: 4303Campus: Tempe
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Jiachen (Lilian) Gong is a learning analytics and AI researcher at the Learning Engineering Institute, where she leads data-intensive research and AI-driven innovation projects that advance personalized learning at scale. She holds a Master’s degree in Educational Technology and Applied Learning Sciences from Carnegie Mellon University, where she specialized in educational data mining, learning analytics, and learner-centered design.
Her research focuses on modeling student learning behaviors, building data-driven learner models, and developing applied AI systems that improve educational outcomes. She has conducted multiple quantitative studies using advanced statistical modeling to examine how behavioral, linguistic, and contextual factors influence student performance, producing insights that inform institutional decision-making and contribute to the learning analytics research community. Her recent work includes the development and evaluation of a large-scale privacy-preserving student discussion board entry dataset for open science initiatives, as well as a university-level analysis of early alert systems and their impact on academic success.
Gong also leads projects at the intersection of AI and education technology, including the design and development of an AI-powered language tutoring system. Her applied work spans prompt engineering, retrieval-augmented generation, supervised fine-tuning, and the evaluation of such AI systems utilizing synthetic interaction datasets.
Prior to ASU, she served as a Data Science Engineer at ExploreLearning, where she implemented large-scale data ETL pipelines, developed Bayesian Knowledge Tracing models, and delivered analytics that shaped product strategy.
Her publications appear in top venues such as the ACM Learning at Scale Conference and the Journal of Applied Research in Memory and Cognition. Across her portfolio, Gong’s work aims to bridge rigorous learning science with cutting-edge AI methodologies to enable equitable, personalized, and scalable learning experiences.
- M.S., Educational Technology and Applied Learning Sciences, Carnegie Mellon University, 2019–2020
- B.A., Sociology, Peking University, 2014–2018
Learning Analytics; AI in Education; Student-centered Learning Design; Learning Sciences
Gong, J., Goldshtein, M., Xu, X., Arner, T., Roscoe, R. D., & McNamara, D. L2 English and culture as factors in college math achievement. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25).
ACM. https://doi.org/10.1145/3698205.3733957
Imundo, M. N., Goldshtein, M., Watanabe, M., Gong, J., Crosby, D. N., Roscoe, R. D., Arner, T., & McNamara, D. S. (2025). Awareness to Action: Student Knowledge of and Responses to an Early Alert System. Applied Sciences, 15(11), 6316. https://doi.org/10.3390/app15116316
Imundo, M. N., Li, S., Gong, J., Potter, A., Arner, T., & McNamara, D. S. (2025). Applying Self-Determination Theory to the Effective Implementation of Personalized Learning in Online Higher Education. Handbook of Personalized Learning, 334–351. https://doi.org/10.4324/9781032719467-26
Imundo, M. N., Watanabe, M., Potter, A. H., Gong, J., Arner, T., & McNamara, D. S. (2024). Expert thinking with generative chatbots. Journal of Applied Research in Memory and Cognition, 13(4), 465–484. https://doi.org/10.1037/mac0000199
Christhilf, K., Gong, J., & McNamara, D.S., (2024). Context-embedded knowledge tracing and latent concept detection in a reading game. In D. Spikol, O. Viberg, A. Martínez-Monés, & P. Guo (Eds.), L@S '24: Proceedings of the eleventh ACM conference on learning @ scale. Association for Computing Machinery. https://doi.org/10.1145/3657604.3664674