Prather, J., Denny, P., Leinonen, J., Becker, B., Albluwi, I., Caspersen, M. E., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., MacNeil, S., Petersen, A., Pettit, R., Reeves, B., & Savelka, J. (2023). Transformed by Transformers: Navigating the AI Coding Revolution for Computing Education: An ITiCSE Working Group Conducted by Humans. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2 (pp. 561–562). https://doi.org/10.1145/3587103.3594206
The recent advent of highly accurate and scalable large language models (LLMs) has taken the world by storm. From art to essays to computer code, LLMs are producing novel content that until recently was thought only humans could produce. Recent work in computing education has sought to understand the capabilities of LLMs for solving tasks such as writing code, explaining code, creating novel coding assignments, interpreting programming error messages, and more. However, these technologies continue to evolve at an astonishing rate leaving educators little time to adapt. This working group seeks to document the state-of-the-art for code generation LLMs, detail current opportunities and challenges related to their use, and present actionable approaches to integrating them into computing curricula.
Visual Computing and Human-Centered Technology: 40% Computer Engineering and Software-Intensive Systems: 50% Beyond TUW-research foci: 10%