Recent research highlights the increasing role of large language models (LLMs) in computer science education, particularly through AI-assisted programming tools.
However, these workflows are prone to objective drift, where the educational goals may not be adequately met due to the autonomous nature of AI systems.
Implementing human-in-the-loop approaches could provide a necessary framework to ensure that educational objectives remain aligned with the outputs generated by LLMs.