Publications

2025

Chapagain, J., Narayanan, A. B. L., Akhuseyinoglu, K., Brusilovsky, P., & Rus, V. (2025). SelfCode 2.0: An Annotated Corpus of Student and Expert Line-by-Line Explanations of Code for Automated Assessment.

Neupane, S., Chapagain, J., Niraula, N. B., & Koirala, D. (2025). Generative AI for Named Entity Recognition in Low-Resource Language Nepali. arXiv preprint arXiv:2503.09822.

2024

Khanal, K., & Chapagain, J. (2024). Curating LLM Tuning Data from the FineWeb Dataset for High-fidelity Domain Adaptation. AGU24.

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 (pp. 64-74). Springer.

2023

Brusilovsky, P., Lekshmi-Narayanan, A. B., Oli, P., Chapagain, J., Hassany, M., Banjade, R., & Rus, V. (2023). Explaining code examples in introductory programming courses: LLM vs humans. arXiv preprint arXiv:2403.05538.

Oli, P., Banjade, R., Chapagain, J., & Rus, V. (2023). Automated Assessment of Students’ Code Comprehension using LLMs. arXiv preprint arXiv:2401.05399.

Oli, P., Banjade, R., Chapagain, J., & Rus, V. (2023). The Behavior of Large Language Models When Prompted to Generate Code Explanations. In Proceedings of the workshop on Generative AI for Education (GAIED) at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023). arXiv.

Oli, P., Banjade, R., Lekshmi Narayanan, A. B., Chapagain, J., Tamang, L. J., Brusilovsky, P., & Rus, V. (2023). Improving Code Comprehension Through Scaffolded Self-explanations. In International Conference on Artificial Intelligence in Education (pp. 478-483). Springer.

Niraula, N., & Chapagain, J. (2023). DanfeNER - Named Entity Recognition in Nepali Tweets. The International FLAIRS Conference Proceedings, 36(1). DOI: 10.32473/flairs.36.133384

Chapagain, J., Risha, Z., Banjade, R., Oli, P., Tamang, L., Brusilovsky, P., & Rus, V. (2023). SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension. The International FLAIRS Conference Proceedings, 36(1).

2022

Niraula, N., & Chapagain, J. (2022). Named Entity Recognition for Nepali: Data Sets and Algorithms. In The International FLAIRS Conference Proceedings (Vol. 35).

Chapagain, J., Tamang, L., Banjade, R., Oli, P., & Rus, V. (2022). Automated Assessment of Student Self-explanation During Source Code Comprehension. In The International FLAIRS Conference Proceedings (Vol. 35).

Tamang, L. J., Banjade, R., Chapagain, J., & Rus, V. (2022). Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension. In Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I (pp. 743-748). Springer.

2021

Banjade, R. (2021). Domain model discovery from textbooks for computer programming intelligent tutors. In The International FLAIRS Conference Proceedings (Vol. 34).

Rus, V., Akhuseyinoglu, K., Chapagain, J., Tamang, L., & Brusilovsky, P. (2021). Prompting for Free Self-Explanations Promotes Better Code Comprehension. In Proceedings of The 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop.