Date
Publisher
arXiv
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.
Study at a glance
- Question: How can AI systems answer student questions about university policies while providing clear, logical explanations?
- Setting and data: IJCNN 2025 competition; dataset of student questions about university policies created with logic templates and reviewed by students.
- Method: Description and analysis of a hackathon competition, its dataset construction, and evaluation protocol.
Why it matters
The growing integration of artificial intelligence into education has intensified the need for transparency and interpretability. While rapid prototyping environments exist, few have directly addressed explainable AI in real-world educational contexts.
Key findings
- In a university setting, question-answering systems can be built to answer student policy queries while providing clear, logic-based explanations.
- Bridging lightweight language models with symbolic reasoning promotes transparency and explainability in educational artificial intelligence tools.
- Combining logic-based templates with student review ensures evaluation datasets align with real-world academic scenarios.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
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Who age?
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Study design
