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.

翻译：学生对学习推荐的投入程度，与其对推荐原因的理解以及基于该理解调整推荐方案的能力密不可分。在可解释性方法中，聊天机器人具有与学生进行对话互动的潜力，类似于与同伴或导师的讨论。然而，尽管生成式AI（GenAI）和大语言模型（LLM）已取得进展，聊天机器人的能力仍不足以完全取代人类导师。为此，我们提出一种方法：将聊天机器人作为对话中介和受控有限解释生成源，在发挥LLM潜力的同时降低其潜在风险。所提出的基于LLM的聊天机器人可帮助学生理解学习路径推荐。我们利用知识图谱（KG）作为人工筛选的信息源，通过定义提示上下文来调控LLM输出。开发了群聊机制，允许学生在需要时或超出聊天机器人预定任务范围时与人类导师连接。通过用户研究评估该聊天机器人，验证概念可行性并揭示在对话可解释性中应用聊天机器人的潜在需求与局限性。