Fawad Khan, Ph.D.

Assistant Research Scientist
Anita Zucker Center for Excellence in Early Childhood Studies

khan.mu@coe.ufl.edu

Biography

Fawad Khan, Ph.D., is Assistant Research Scientist at the Anita Zucker Center for Excellence in Early Childhood Studies at the University of Florida. He specializes in human-centered artificial intelligence, with a focus on developing, deploying and evaluating AI-enhanced learning technologies. His research integrates large language models (LLMs), learning analytics and cognitive modeling to better understand and support student learning processes.

Dr. Khan’s work is distinguished by its practical application. At Utah State University, he developed and deployed a GPT-powered intelligent tutoring system used by more than 300 computer science students, which featured adaptive feedback through Retrieval-Augmented Generation (RAG) and fine-tuned models. His expertise includes keystroke analytics, cognitive load prediction and creating fairness-aware machine learning frameworks to ensure educational tools are equitable.

He has published his research in numerous peer-reviewed venues, including IEEE and EDM, and serves as a reviewer for major conferences such as AAAI, AIED and IEEE BigData. His contributions have been recognized with awards, including the Outstanding Graduate Teaching Assistant Award from Utah State University and a Graduate Research Fellowship. Dr. Khan’s primary professional interest is designing equitable, interpretable and impactful AI systems that enhance learning outcomes across diverse educational settings.

Dr. Khan’s research operates at the intersection of human-centered artificial intelligence and educational technology. He focuses on designing, developing and evaluating next-generation learning technologies powered by large language models. His work is dedicated to supporting a wide range of users, from early childhood educators such as teachers, caregivers and coaches to learners across the educational spectrum, including K-12 and higher education. His technical approach integrates advanced AI methods, such as Retrieval-Augmented Generation and fine-tuning, with multimodal learning analytics that capture keystroke, video, audio and interaction data. By analyzing these rich data streams, his research aims to model and enhance the learning process by delivering personalized, adaptive feedback. A deep commitment to responsible AI underpins his work, ensuring these systems are not only effective but also fair, interpretable and equitable for all users.

Selected Publications

Peer-Reviewed Conference Proceedings

Khan, M. F. A., Feri, L. E., Nguyen, H., & Karimi, H. (2025). Student-perceived cognitive load of LLM-generated programming exercises. In 2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–12). IEEE. https://doi.org/10.1109/DSAA65442.2025.11247983

Khan, M. F. A., Ramsdell, M., Falor, E., & Karimi, H. (2024a). Assessing the promise and pitfalls of ChatGPT for automated CS1-driven code generation. In Proceedings of the 17th International Conference on Educational Data Mining (EDM) (pp. 83–95). International Educational Data Mining Society. https://doi.org/10.48550/arXiv.2311.02640

Khan, M. F. A., Ramsdell, M., Nguyen, H., & Karimi, H. (2024b). Human evaluation of GPT for scalable Python programming exercise generation. In 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–10). IEEE. https://doi.org/10.1109/DSAA61799.2024.10722841

Farokhi, S., Yaramal, A., Huang, J., Khan, M. F. A., Qi, X., & Karimi, H. (2023). Enhancing the performance of automated grade prediction in MOOC using graph representation learning. In 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–10). IEEE. https://doi.org/10.1109/DSAA60987.2023.10302642

Khan, M. F. A., Edwards, J., Bodily, P., & Karimi, H. (2023). Deciphering student coding behavior: Interpretable keystroke features and ensemble strategies for grade prediction. In 2023 IEEE International Conference on Big Data (BigData) (pp. 5799–5808). IEEE. https://doi.org/10.1109/BigData59044.2023.10386085

Kheiri, K., Khan, M. F. A., Derr, T., & Karimi, H. (2023). An analysis of the dynamics of ties on Twitter. In 2023 IEEE International Conference on Big Data (BigData) (pp. 5809–5817). IEEE. https://doi.org/10.1109/BigData59044.2023.10386839

Karimi, H., Khan, M. F. A., Liu, H., Derr, T., & Liu, H. (2022). Enhancing individual fairness through propensity score matching. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–10). IEEE. https://doi.org/10.1109/DSAA54385.2022.10032333

Khan, M. F. A., & Karimi, H. (2022). A new framework to assess the individual fairness of probabilistic classifiers. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 876–881). IEEE. https://doi.org/10.1109/ICMLA55696.2022.00145

Dissertation

Khan, M. F. A. (2026). Large Language Models for Introductory Computer Science Education: Content Generation, Intelligent Tutoring, And Learner Modeling (Doctoral dissertation, Utah State University). https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1791&context=etd2023

Peer-Reviewed Journal Articles 

Elahi, F., Muhammad, K., Din, S. U., Khan, M. F. A., Bashir, S., & Hanif, M. (2022). Lithological mapping of Kohat Basin in Pakistan using multispectral remote sensing data: A comparison of support vector machine (SVM) and artificial neural network (ANN). Applied Sciences, 12(23), Article 12147. https://doi.org/10.3390/app122312147

Din, S. U., Muhammad, K., Khan, M. F. A., Bashir, S., Sajid, M., & Khan, A. (2021). A fusion of feature-oriented principal components of multispectral data to map granite exposures of Pakistan. Applied Sciences, 11(23), Article 11486. https://doi.org/10.3390/app112311486

Khan, M. F. A., Muhammad, K., Bashir, S., Ud Din, S., & Hanif, M. (2021). Mapping allochemical limestone formations in Hazara, Pakistan using Google Cloud architecture: Application of machine-learning algorithms on multispectral data. ISPRS International Journal of Geo-Information, 10(2), Article 58. https://doi.org/10.3390/ijgi10020058