Date
Publisher
arXiv
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.
Study at a glance
- Question: How can the escalating roles of agentic AI be conceptualized within human-AI collaborative learning?
- Setting and data: Not stated in the paper
- Method: Conceptual analysis and synthesis
Why it matters
The shift toward autonomous, goal-directed agentic AI challenges traditional educational roles, but the field lacks a robust conceptual framework to understand, design, and evaluate human-AI interaction in collaborative learning.
Key findings
- The APCP framework outlines four escalating levels of AI agency in collaborative learning: Adaptive Instrument, Proactive Assistant, Co-Learner, and Peer Collaborator.
- While agentic AI lacks genuine consciousness or shared intentionality, it can be designed to achieve highly effective functional collaboration through observable collaborative behaviors.
- Designing AI as a functional collaborator requires formalizing human collaboration into explicit rules, which in turn provides a pedagogical tool to help students improve their own teamwork skills.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
