We tackle the task of mention detection for pair-programming dialogue, a setting which adds several challenges to the task due to the characteristics of natural dialogue, the dynamic environment of the dialogue task, and the domain-specific vocabulary and structures. We compare recent variants of the Llama and GPT families and explore different prompt and context engineering approaches. While aspects like hesitations and references to read-out code and variable names made the task challenging, GPT 4.1 approximated human performance when we provided few-shot examples similar to the inference text and corrected formatting errors.

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.