Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education

TL;DR

This paper introduces a neuro-symbolic multi-agent AI framework combining reinforcement learning and large language models to support personalized, adaptable, and socially interactive digital learning environments across educational levels.

Contribution

It presents a novel unified neuro-symbolic framework integrating specialized pedagogical agents with a central ontology for scalable, cross-domain AI-supported education.

Findings

Framework demonstrates adaptability in college and middle school settings.

Agents effectively support learner ownership and social interaction.

The unified approach improves scalability and personalization in digital learning.

Abstract

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.