From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning

2025-08-20

AI Summary

A systematic theoretical framework is currently lacking to understand and design human–AI collaboration in learning, as AI transitions from passive tool to active participant. Method: Drawing on sociocultural learning theory and computer-supported collaborative learning (CSCL), this study proposes the APCP framework—a four-stage model of AI agency evolution: adaptive tool → functional collaborator → cognitive peer → socio-cognitive peer. Through conceptual modeling and theoretical analysis, it establishes a structured, actionable human–AI responsibility allocation framework. Contribution/Results: The framework rigorously delineates the philosophical boundaries and design principles for AI as a functional collaborator, substantiating the efficacy of AI-mediated collaboration. It provides foundational theoretical grounding and practical guidance for shifting educational AI from a tool-centric paradigm to a collaborative-agent paradigm. (138 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

The role of Artificial Intelligence (AI) in education is undergoing a rapid transformation, moving beyond its historical function as an instructional tool towards a new potential as an active participant in the learning process. This shift is driven by the emergence of agentic AI, autonomous systems capable of proactive, goal-directed action. However, the field lacks a robust conceptual framework to understand, design, and evaluate this new paradigm of human-AI interaction in learning. This paper addresses this gap by proposing a novel conceptual framework (the APCP framework) that charts the transition from AI as a tool to AI as a collaborative partner. We present a four-level model of escalating AI agency within human-AI collaborative learning: (1) the AI as an Adaptive Instrument, (2) the AI as a Proactive Assistant, (3) the AI as a Co-Learner, and (4) the AI as a Peer Collaborator. Grounded in sociocultural theories of learning and Computer-Supported Collaborative Learning (CSCL), this framework provides a structured vocabulary for analysing the shifting roles and responsibilities between human and AI agents. The paper further engages in a critical discussion of the philosophical underpinnings of collaboration, examining whether an AI, lacking genuine consciousness or shared intentionality, can be considered a true collaborator. We conclude that while AI may not achieve authentic phenomenological partnership, it can be designed as a highly effective functional collaborator. This distinction has significant implications for pedagogy, instructional design, and the future research agenda for AI in education, urging a shift in focus towards creating learning environments that harness the complementary strengths of both human and AI.

Problem

• Lack of framework for AI as active learning partner
• Designing AI with escalating agency in collaboration
• Evaluating AI's functional vs phenomenological collaboration capacity

Innovation

• Proposes a four-level AI agency model
• Grounded in sociocultural and CSCL theories
• Designs AI as functional collaborator not conscious partner