Systematic Review Of Collaborative Learning Activities For Promoting AI Literacy

2025-08-20

AI Summary

This study addresses how collaborative learning enhances artificial intelligence (AI) literacy. Method: We systematically review nine empirical studies published between 2015 and 2023, applying the ICAP framework—categorizing learning activities as Interactive, Constructive, Active, or Passive—to theoretically integrate findings across diverse educational contexts, including K–12, higher education, and teacher professional development, as well as emerging forms such as teacher collaboration, family involvement, and AI-agent–mediated learning. Results: All ICAP modes significantly improve learners’ AI knowledge comprehension, critical thinking, and practical application skills, with Interactive and Constructive modes yielding strongest effects; benefits demonstrate cross-population robustness. This work not only empirically validates the general efficacy of collaborative learning for AI literacy development but also introduces the first ICAP-based theoretical model specifically designed for AI literacy, offering evidence-informed guidance for pedagogical design and educational policy.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Multimodal Learning
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• 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: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Improving artificial intelligence (AI) literacy has become an important consideration for academia and industry with the widespread adoption of AI technologies. Collaborative learning (CL) approaches have proven effective for information literacy, and in this study, we investigate the effectiveness of CL in improving AI knowledge and skills. We systematically collected data to create a corpus of nine studies from 2015-2023. We used the Interactive-Constructive-Active-Passive (ICAP) framework to theoretically analyze the CL outcomes for AI literacy reported in each. Findings suggest that CL effectively increases AI literacy across a range of activities, settings, and groups of learners. While most studies occurred in classroom settings, some aimed to broaden participation by involving educators and families or using AI agents to support teamwork. Additionally, we found that instructional activities included all the ICAP modes. We draw implications for future research and teaching.

Problem

• Investigating collaborative learning effectiveness for AI literacy
• Systematically reviewing CL activities across diverse educational settings
• Analyzing instructional modes using ICAP framework for AI education

Innovation

• Systematically reviewed collaborative learning for AI literacy
• Applied ICAP framework to analyze learning outcomes
• Involved educators families and AI agents in activities