Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

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

Created by
  • Haebom

Author

Lixiang Yan

Outline

This paper addresses the evolving role of artificial intelligence (AI) in education, highlighting its potential to move beyond mere educational tools and actively engage in the learning process. Specifically, it highlights the lack of a robust conceptual framework for understanding, designing, and evaluating the emerging paradigm of human-AI interaction, driven by the emergence of goal-oriented, autonomous AI agents. This paper proposes the APCP framework, a novel conceptual framework that describes the transition of AI from tool to collaborative partner. It distinguishes between four levels of AI agency (adaptive tool, proactive facilitator, co-learner, and peer collaborator) and provides a structural vocabulary for analyzing the changing roles and responsibilities between humans and AI agents. Furthermore, through a philosophical discussion of whether AI is truly a collaborator, it argues that while AI may not achieve a truly subjective partnership, it can be designed as a highly effective functional collaborator. This paper offers implications for the future of pedagogy, instructional design, and AI education research.

Takeaways, Limitations

Takeaways:
Presenting a new conceptual framework (APCP framework) that expands the role of AI in education from tool to collaborative partner.
By distinguishing the level of AI agency in human-AI collaborative learning, we provide specific directions for educational design and evaluation.
Emphasizes the potential for functional collaboration between AI and highlights the importance of creating a learning environment that leverages the complementary strengths of humans and AI.
Presenting future directions for AI education research.
Limitations:
Lack of empirical research on the practical application and effectiveness of the APCP framework.
The need for a deeper philosophical discussion on the "true cooperation" of AI.
Generalizability needs to be examined across different types of AI systems and learning environments.
👍