桥接教育问答系统中的大语言模型与符号推理：IJCNN 2025 XAI挑战赛的洞见
Bridging LLMs and Symbolic Reasoning in Educational QA Systems: Insights from the XAI Challenge at IJCNN 2025
- URA Research Group, Ho Chi Minh City University of Technology (HCMUT), Vietnam
- Ho Chi Minh City International University (HCMIU), Vietnam
- University of South-Eastern Norway, Norway
- Japan Advanced Institute of Science
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France
- University of Naples Parthenope, Italy
- VNU Information Technology Institute, Vietnam National University, Vietnam
- Visual Intelligence Lab, School of Computer Science \& Insight Center for Data Analyitcs, University of Galway, Ireland
- Sapienza University of Rome, Italy
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文分析IJCNN 2025 XAI挑战赛，该赛事要求构建轻量级LLM或混合符号系统的教育问答系统，以逻辑解释回答大学政策问题，旨在桥接LLM与符号推理，提升教育AI的可解释性。
AI中文摘要：
人工智能（AI）在教育领域日益深入的整合，加剧了对透明度和可解释性的需求。尽管黑客马拉松长期以来一直作为快速AI原型开发的敏捷环境，但很少有直接针对真实教育场景中的可解释人工智能（XAI）的活动。本文对2025年XAI挑战赛进行了全面分析，这是一场由胡志明市理工大学（HCMUT）与国际神经符号AI可信赖性与可靠性研讨会（TRNS-AI）联合组织的黑客马拉松式竞赛，作为2025年国际联合神经网络大会（IJCNN 2025）的一部分举行。该挑战要求参赛者构建问答（QA）系统，能够回答学生关于大学政策的问题，同时生成清晰的、基于逻辑的自然语言解释。为了促进透明度和可信赖性，解决方案被要求使用轻量级大语言模型（LLM）或混合LLM-符号系统。主办方提供了一个高质量数据集，该数据集通过基于逻辑的模板并借助Z3验证构建，并经过专家学生评审以确保与真实学术场景的一致性。我们描述了该挑战的动机、结构、数据集构建和评估协议。将此次竞赛置于AI黑客马拉松更广泛的演变背景中，我们认为它代表了在可解释性服务中桥接LLM与符号推理的一次新颖尝试。我们的发现为未来以XAI为中心的教育系统和竞争性研究举措提供了可操作的洞见。
英文摘要：
The growing integration of Artificial Intelligence (AI) into education has intensified the need for transparency and interpretability. While hackathons have long served as agile environments for rapid AI prototyping, few have directly addressed eXplainable AI (XAI) in real-world educational contexts. This paper presents a comprehensive analysis of the XAI Challenge 2025, a hackathon-style competition jointly organized by Ho Chi Minh City University of Technology (HCMUT) and the International Workshop on Trustworthiness and Reliability in Neurosymbolic AI (TRNS-AI), held as part of the International Joint Conference on Neural Networks (IJCNN 2025). The challenge tasked participants with building Question-Answering (QA) systems capable of answering student queries about university policies while generating clear, logic-based natural language explanations. To promote transparency and trustworthiness, solutions were required to use lightweight Large Language Models (LLMs) or hybrid LLM-symbolic systems. A high-quality dataset was provided, constructed via logic-based templates with Z3 validation and refined through expert student review to ensure alignment with real-world academic scenarios. We describe the challenge's motivation, structure, dataset construction, and evaluation protocol. Situating the competition within the broader evolution of AI hackathons, we argue that it represents a novel effort to bridge LLMs and symbolic reasoning in service of explainability. Our findings offer actionable insights for future XAI-centered educational systems and competitive research initiatives.
