  Student commitment towards a learning recommendation is not separable from
their understanding of the reasons it was recommended to them; and their
ability to modify it based on that understanding. Among explainability
approaches, chatbots offer the potential to engage the student in a
conversation, similar to a discussion with a peer or a mentor. The capabilities
of chatbots, however, are still not sufficient to replace a human mentor,
despite the advancements of generative AI (GenAI) and large language models
(LLM). Therefore, we propose an approach to utilize chatbots as mediators of
the conversation and sources of limited and controlled generation of
explanations, to harvest the potential of LLMs while reducing their potential
risks at the same time. The proposed LLM-based chatbot supports students in
understanding learning-paths recommendations. We use a knowledge graph (KG) as
a human-curated source of information, to regulate the LLM's output through
defining its prompt's context. A group chat approach is developed to connect
students with human mentors, either on demand or in cases that exceed the
chatbot's pre-defined tasks. We evaluate the chatbot with a user study, to
provide a proof-of-concept and highlight the potential requirements and
limitations of utilizing chatbots in conversational explainability.
