Recent News & Events
Recent Events & News
(January 22, 2025): Our paper on identifying gaps in students' code explanations using LLMs is now available: https://arxiv.org/abs/2501.10365
(May 25, 2024): Check my GIFTSym 12 paper: LLMs for Conversational Tutors .
(November 6, 2023): Check our new paper on the behavior of LLMs for code explanations: https://arxiv.org/abs/2311.01490
(October 19, 2023): Dr. Vasile Rus is co-PI on a new ~$1.1million NSF grant entitled MRI: Track 2 Acquisition of a HPC Cluster for Fostering Interdisciplinary Collaboration on AI-driven and Data-intensive Research and Education in West Tennessee. This GPU cluster will enable the development of a novel LLM-based iCODE architecture. More details can be found on the NSF site: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318210&HistoricalAwards=false
Rus, V., Sinatra, A.M., Lintean, M. (Eds.). (in press). On The Role of Generative AI in Conversational Intelligent Tutoring Systems, In Design Recommendations for Intelligent Tutoring Systems: Volume 12 – Generative AI for Intelligent Tutoring Systems, Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center.
Chapagain, J., Sajib, M.I., Prodan, R., Rus, V. (2024). A Study of LLM Generated Line-by-Line Explanations in the Context of Conversational Program Comprehension Tutoring Systems, In European Conference on Technology Enhanced Learning, 2024.
Lekshmi-Narayanan, A.B., Oli, P., Chapagain, J., Hassany, M., Banjade, R., Brusilovsky, P., Rus, V. (2024). Explaining Code Examples in Introductory Programming Courses: LLM vs Humans, In Proceedings of the AI for Education Workshop, AAAI 2024, Vancouver, Canada.
Oli, P., Banjade, R., Chapagain, J., Rus, V. (2024). Automated Assessment of Students' Code Comprehension using LLMs, Proceedings of the AI for Education Workshop, AAAI 2024, Vancouver, Canada
Oli, P., Banjade, R., Lekshmi-Narayanan, A.B., Brusilovsky, P., Rus, V. (2024). Exploring The Effectiveness of Reading vs. Tutoring For, Enhancing Code Comprehension For Novices. In Proceedings of ACM SAC Conference (SAC'24). ACM,Avila, Spain
Banjade, R. & Oli, P. & Sajib, M.H, Rus, V. (2024). Identifying Gaps in Students’ Explanations of Code Using LLMs. Proceedings of The 25th International Conference on Artificial Intelligence in Education; AIED 2024. 8-12 July; Recife, Brazil, 10.1007/978-3-031-64299-9_21.
Magar, A.T., Fancsali, S.E., Rus, V., Murphy, A., Ritter, S., Venugopal, D. (2024). Learning Representations for Math Strategies using BERT, ACM Learning @ Scale 2024.
Rus, V. (2024). Large Language Models and Their Implications for Conversational Tutors and GIFT. In Proceedings of the 12th Annual GIFT Users Symposium. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0-9977258-6-5.
Sinatra, A.M., Graesser, A.C., Hu, X., Townsend, L.N. and Rus, V. (Eds.). (2023). Design Recommendations for Intelligent Tutoring Systems: Volume 11 - Professional Career Education. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0-9977258-3-4.
Rus, V., Tamang, L, Oli, P., Banjade, R., Brusilovsky, P. (submitted) DeepCode: An Annotated Instructional CodeSet Based on Code Comprehension and Learning Theories To Foster The Development of Code Comprehension Adaptive Instructional Systems, ACM Transactions on Learning Engineering.
Rus, V., Olney, A. M., & Graesser, A. C. (2023). Deeper learning through interactions with students in natural language. In B. du Boulay, A. Mitrovic, & K. Yacef (Eds.), Handbook of Artificial Intelligence in Education (pp. 250–272). Edward Elgar Publishing. https://doi.org/10.4337/9781800375413.00021.
