Collaborative and social learning has proven to be an essential pedagogical approach for developing cognitive, metacognitive, and socio-emotional skills. Simply placing a group of people to work on a common task does not guarantee true collaboration; it requires structuring activities that foster effective participation and, consequently, collaboration. With recent advances in artificial intelligence (AI), the ways in which people learn, communicate, and collaboratively construct knowledge are being profoundly transformed. This special section aims to critically analyze how AI can support, enrich, or transform collaborative and social dynamics in educational and training contexts.
The coexistence of humans and intelligent systems opens new possibilities for designing more inclusive, adaptive, and effective learning experiences. At the same time, it raises critical challenges related to shared intelligence, equity, trust, transparency, the social regulation of knowledge, and the co-creation of content using artificial agents. Therefore, we invite research that addresses both the potential and the ethical, social, and pedagogical implications of these technologies.
This special section aims to consolidate an interdisciplinary space where approaches from computer science, educational psychology, instructional design, human-computer interaction, technology ethics, the sociology of learning, and related fields converge.
Suggested topics (not exclusive): • Social and collaborative dynamics with AI agents • AI design for collaborative learning • Evaluation of the collaborative process using AI • Modeling of social processes • Generative AI in co-creation activities • Metacognitive and affective regulation • Equity, inclusion, and accessibility • Ethical considerations, privacy, and co-agency • Empirical studies in real-world contexts
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy and Practice Reviews
Review
Systematic Review
Technology and Code
Keywords: Collaborative Learning with AI, Social Learning Dynamics, AI-Supported Knowledge Co-Construction, Ethical and Inclusive AI in Education, Human–AI Co-agency in Learning Environments
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
