用神经符号框架整合智能导师与同伴，实现可适配的教育支持。
Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education
- 分角色设计强化学习导师与大模型同伴，协同提供教学与社交支持。
- 在大学与中学场景中验证跨领域适应性，支持多样化学习需求。
- 基于统一教育本体融合技术与社会支持，提升自主学习能力。
教育长期面临如何让学生主动设定目标、追踪进展并应对挫折的挑战。研究表明，以学习者为中心的学习需依托结构化、支持性的环境，促进引导练习、支架式探究与协作对话。为此，教育领域正越来越多采用人工智能驱动的数字学习环境，包括教育应用、虚拟实验和严肃游戏。近期大语言模型（LLMs）与神经符号系统的发展，为重新构想数字学习中的支持方式提供了变革性机遇。LLMs 赋予学习体验社会互动性，并实现跨领域、可扩展的自适应支持；神经符号 AI 则为设计既自适应又跨域可扩展的智能代理开辟新路径。本文提出一种多智能体神经符号框架，通过赋予专用代理不同教学角色：基于强化学习的‘导师’提供非言语支架，基于 LLM 的‘同伴’推动学习的社会维度。此前研究多孤立探索此类代理，本框架的创新在于通过中心化教育本体实现统一。案例研究涵盖大学与初中场景，证明其跨领域适应能力。最后，总结关键洞察与未来方向，推动智能学习环境发展。
原文摘要 · Abstract (English)
One of the enduring challenges in education is how to empower students to take ownership of their learning by setting meaningful goals, tracking their progress, and adapting their strategies when faced with setbacks. Research has shown that this form of leaner-centered learning is best cultivated through structured, supportive environments that promote guided practice, scaffolded inquiry, and collaborative dialogue. In response, educational efforts have increasingly embraced artificial-intelligence (AI)-powered digital learning environments, ranging from educational apps and virtual labs to serious games. Recent advances in large language models (LLMs) and neuro-symbolic systems, meanwhile, offer a transformative opportunity to reimagine how support is delivered in digital learning environments. LLMs are enabling socially interactive learning experiences and scalable, cross-domain learning support that can adapt instructional strategies across varied subjects and contexts. In parallel, neuro-symbolic AI provides new avenues for designing these agents that are not only adaptive but also scalable across domains. Based on these remarks, this paper presents a multi-agent, neuro-symbolic framework designed to resolve the aforementioned challenges. The framework assigns distinct pedagogical roles to specialized agents: an RL-based 'tutor' agent provides authoritative, non-verbal scaffolding, while a proactive, LLM-powered 'peer' agent facilitates the social dimensions of learning. While prior work has explored such agents in isolation, our framework's novelty lies in unifying them through a central educational ontology. Through case studies in both college-level and middle school settings, we demonstrate the framework's adaptability across domains. We conclude by outlining key insights and future directions for advancing AI-driven learning environments.
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