AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Liu, M.; M'Hiri, F.

Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science Proceedings Article

In: SIGCSE - Proc. ACM Tech. Symp. Comput. Sci. Educ., pp. 743–749, Association for Computing Machinery, Inc, 2024, ISBN: 979-840070423-9 (ISBN).

Abstract | Links | BibTeX | Tags: Adaptive teaching, ChatGPT, Computational Linguistics, CS education, E-Learning, Education computing, Engineering education, GPT, Language Model, LLM, machine learning, Machine-learning, Novice programmer, novice programmers, Openai, Programming, Python, Students, Teaching, Virtual Reality

@inproceedings{liu_beyond_2024,

title = {Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science},

author = {M. Liu and F. M'Hiri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189289344&doi=10.1145%2f3626252.3630789&partnerID=40&md5=44ec79c8f005f4551c820c61f5b5d435},

doi = {10.1145/3626252.3630789},

isbn = {979-840070423-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {SIGCSE - Proc. ACM Tech. Symp. Comput. Sci. Educ.},

volume = {1},

pages = {743–749},

publisher = {Association for Computing Machinery, Inc},

abstract = {As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM teaching assistant (TA) or a virtual TA in computer science (CS) courses. The objective is to enhance the accessibility of CS education while maintaining academic integrity by refraining from providing direct solutions to current-semester assignments. Targeting Foundations of Programming (COMP202), an undergraduate course that introduces students to programming with Python, we have developed a virtual TA using the LangChain framework, known for integrating language models with diverse data sources and environments. The virtual TA assists students with their code and clarifies complex concepts. For homework questions, it is designed to guide students with hints rather than giving out direct solutions. We assessed its performance first through a qualitative evaluation, then a survey-based comparative analysis, using a mix of questions commonly asked on the COMP202 discussion board and questions created by the authors. Our preliminary results indicate that the virtual TA outperforms human TAs on clarity and engagement, matching them on accuracy when the question is non-assignment-specific, for which human TAs still proved more reliable. These findings suggest that while virtual TAs, leveraging the capabilities of LLMs, hold great promise towards making CS education experience more accessible and engaging, their optimal use necessitates human supervision. We conclude by identifying several directions that could be explored in future implementations. © 2024 ACM.},

keywords = {Adaptive teaching, ChatGPT, Computational Linguistics, CS education, E-Learning, Education computing, Engineering education, GPT, Language Model, LLM, machine learning, Machine-learning, Novice programmer, novice programmers, Openai, Programming, Python, Students, Teaching, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}