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

• URL: http://arxiv.org/abs/2508.18406v1
• Date: Mon, 25 Aug 2025 18:46:59 GMT
• Title: Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education
• Authors: Ryan Hare, Ying Tang,
• Abstract summary: One of the enduring challenges in education is how to empower students to take ownership of their learning.<n>Recent advances in large language models (LLMs) and neuro-symbolic systems offer a transformative opportunity to reimagine how support is delivered in digital learning environments.<n>This paper presents a multi-agent, neuro-symbolic framework designed to resolve the aforementioned challenges.
• Score: 2.336538451986937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• A Survey on Agentic Multimodal Large Language Models [84.18778056010629]
  We present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs)<n>We explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents.<n>To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs.
  arXiv  Detail & Related papers  (2025-10-13T04:07:01Z)
• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey [103.32591749156416]
  The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL)<n>This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL.
  arXiv  Detail & Related papers  (2025-09-02T17:46:26Z)
• Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architectures [0.0]
  Generative AI is no longer a peripheral tool in higher education.<n>This paper presents findings from a controlled experiment evaluating a Socratic AI Tutor.<n>Students using the Tutor reported significantly greater support for critical, independent, and reflective thinking.
  arXiv  Detail & Related papers  (2025-08-07T07:49:03Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• FLoRA: An Advanced AI-Powered Engine to Facilitate Hybrid Human-AI Regulated Learning [6.965423433136556]
  This paper introduces the enhanced FLoRA Engine, which incorporates advanced Generative Artificial Intelligence (GenAI) features and state-of-the-art learning analytics.<n>The FLoRA Engine offers instrumentation tools such as collaborative writing, multi-agents chatbots, and detailed learning trace logging to support dynamic, adaptive scaffolding tailored to individual needs in real time.
  arXiv  Detail & Related papers  (2025-07-10T01:11:52Z)
• AI Pedagogy: Dialogic Social Learning for Artificial Agents [0.6553587309274792]
  This study explores the potential of socially mediated learning paradigms to address limitations of traditional AI training approaches.<n>We introduce a dynamic environment, termed the 'AI Social Gym', where an AI learner agent engages in dyadic pedagogical dialogues with knowledgeable AI teacher agents.<n>Our investigation focuses on how different pedagogical strategies impact the AI learning process in the context of acquisition.
  arXiv  Detail & Related papers  (2025-05-25T11:19:48Z)
• A Computational Model of Inclusive Pedagogy: From Understanding to Application [1.2058600649065616]
  Human education transcends mere knowledge transfer, it relies on co-adaptation dynamics.<n>Despite its centrality, computational models of co-adaptive teacher-student interactions (T-SI) remain underdeveloped.<n>We present a computational T-SI model that integrates contextual insights on human education into a testable framework.
  arXiv  Detail & Related papers  (2025-05-02T12:26:31Z)
• Evolution of AI in Education: Agentic Workflows [2.1681971652284857]
  Artificial intelligence (AI) has transformed various aspects of education.<n>Large language models (LLMs) are driving advancements in automated tutoring, assessment, and content generation.<n>To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation.
  arXiv  Detail & Related papers  (2025-04-25T13:44:57Z)
• LLM Agents for Education: Advances and Applications [49.3663528354802]
  Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.<n>This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
  arXiv  Detail & Related papers  (2025-03-14T11:53:44Z)
• Guiding Empowerment Model: Liberating Neurodiversity in Online Higher Education [2.703906279696349]
  We address the equity gap for neurodivergent and situationally limited learners by identifying the spectrum of dynamic factors that impact learning and function. We suggest that by applying the mode through technology-enabled features such as customizable task management, guided varied content access, and guided multi-modal collaboration, major learning barriers will be removed.
  arXiv  Detail & Related papers  (2024-10-24T16:05:38Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.