支持学生对学习推荐的决策：一款基于 LLM、具备知识图谱情境化能力以实现对话式可解释性与指导的聊天机器人
Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring
- University of Siegen（锡根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于 LLM 的学习推荐解释聊天机器人，利用知识图谱约束生成，并通过群聊接入人类导师，用户研究验证了其概念可行性与局限。
AI中文摘要：
学生对一项学习推荐的投入，离不开他们对该推荐为何被推荐给自己的理解，也离不开他们基于这种理解对推荐进行修改的能力。在各类可解释性方法中，聊天机器人有潜力让学生参与对话，类似于与同伴或导师进行讨论。然而，尽管生成式人工智能（GenAI）和大型语言模型（LLM）取得了进展，聊天机器人的能力仍不足以取代人类导师。因此，我们提出一种方法，将聊天机器人用作对话的中介以及有限且受控的解释生成来源，以挖掘 LLM 的潜力，同时降低其潜在风险。所提出的基于 LLM 的聊天机器人支持学生理解学习路径推荐。我们使用知识图谱（KG）作为人工策展的信息源，通过定义 LLM 提示的上下文来规范其输出。系统还开发了群聊方法，以便在学生需要时，或在超出聊天机器人预定义任务的情况下，将学生与人类导师连接起来。我们通过一项用户研究评估该聊天机器人，以提供概念验证，并强调在对话式可解释性中使用聊天机器人的潜在需求和局限。
英文摘要：
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.
