Human ratings of LLM response generation in pair-programming dialogue

We take first steps in exploring whether Large Language Models (LLMs) can be adapted to dialogic learning practices, specifically pair programming {---} LLMs have primarily been implemented as programming assistants, not fully exploiting their dialogic potential. We used new dialogue data from real pair-programming interactions between students, prompting state-of-the-art LLMs to assume the role of a student, when generating a response that continues the real dialogue. We asked human annotators to rate human and AI responses on the criteria through which we operationalise the LLMs' suitability for educational dialogue: Coherence, Collaborativeness, and whether they appeared human. Results show model differences, with Llama-generated responses being rated similarly to human answers on all three criteria. Thus, for at least one of the models we investigated, the LLM utterance-level response generation appears to be suitable for pair-programming dialogue.