Where's the Structure? A Systematic Literature Review of Empirical Research on Human-AI Collaboration and Hybrid Intelligence for Learning
arXiv:2606.05222v1 Announce Type: cross Abstract: Artificial intelligence (AI) has been applied across educational contexts to support learning. One approach to such support is "human-AI collaboration" (also termed "hybrid intelligence"), where human(s) and AI components interact to promote human learning. However, as in human-to-human computer-supported collaborative learning (CSCL), unstructured interaction does not necessarily produce an effective learning experience. This paper reports a systematic literature review of empirical studies (N=62) on human-AI collaboration and hybrid intellige
The proliferation of AI in education necessitates a deeper understanding of effective human-AI interaction for learning outcomes.
Optimizing human-AI collaboration is crucial for maximizing the educational benefits of AI, impacting future workforce capabilities and societal development.
A systematic review provides empirical foundations for designing more effective hybrid intelligence systems in educational settings.
- · EdTech companies
- · Educators
- · Students
- · AI researchers
- · Ineffective AI education solutions
- · Traditional, static learning models
Improved design principles for educational AI systems will emerge, fostering better learning experiences.
Enhanced human learning through optimized AI collaboration could lead to accelerated skill development and innovation.
A more skilled global workforce, less reliant on traditional educational infrastructure, could emerge, impacting economic and social structures.
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