Using Generative Artificial Intelligence to Support Social Interactions in STEM Education

Recently the educational technology landscape has been captivated by the promise of Generative Artificial Intelligence (GenAI) to deliver personalized, 1-to-1 tutoring and streamline administrative tasks. However, learning sciences shows that knowledge building is an inherently social and collaborative endeavor. By emphasizing individualization and efficiency, many current AI tools bypass the productive friction and peer-to-peer discourse that are crucial for developing deep STEM understanding and competency. In addition, the current workforce requires that people work together to solve difficult problems, so a primarily individualized approach to instruction is not preparing our students for the future. As the limitations of solitary, AI-driven learning models become more apparent, there is an urgent need to re-evaluate the role of GenAI in STEM education. This Research Topic explores the untapped potential of GenAI to purposefully facilitate and elevate social interactions, shared experiences, and collaborative problem-solving within STEM education.

The goal of this Research Topic is to present a new vision for the future of GenAI in STEM education, one that prioritizes community, shared experiences, and social learning over individualization and efficiency. Our objective is to assemble forward-thinking research, design principles, and frameworks that illustrate how AI can be intentionally designed and implemented to foster social interactions in STEM education. We aim to address the problem of student silos by exploring interventions where AI prompts group discussions, supports collaborative problem-solving, creates culturally responsive educational group activities, and assists educators in making diverse student thinking visible to the entire class. By highlighting alternative applications of AI that champion the social and relational aspects of learning, we hope to provide educators, policymakers, and developers with a clear roadmap for utilizing these technologies to strengthen the education ecosystem. This collection will ultimately focus on ensuring that the integration of GenAI in STEM serves to amplify, not sideline, human connection and collaboration.

This research topic is dedicated to advancing knowledge on the socially constructive uses of GenAI in STEM education. We seek submissions, including, but not limited to Brief Research Reports, Policy and Practice Reviews, and Original Research, that challenge efficiency- and individualization-centric models by highlighting collaborative AI integration.

Preliminary Review Process: Authors are required to submit an initial summary due by November 20, 2026, prior to submitting a full manuscript. The editorial team will review all summaries for scope and relevance, and will provide feedback indicating whether the submission is suitable for full manuscript development.

Key themes include:

The use of AI to generate cooperative learning tasks;
AI systems that analyze and support small-group interactions;
Using AI to build on student interests through culturally responsive collaborative activities, and instructional designs that leverage AI to highlight diverse student reasoning for whole-class discussion.

Submissions should prioritize the enhancement of social interactions and provide actionable insights into overcoming the barriers of hyper-individualized learning. We welcome interdisciplinary perspectives that bridge the learning sciences, pedagogy, and technology design to ensure that future AI applications actively cultivate a sense of belonging and shared discovery in STEM education.

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
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

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
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Systematic Review

Keywords: STEM Education, Mathematics Education, Generative Artificial Intelligence, Classroom Discourse, Social Learning, Computer-Supported Collaborative Learning (CSCL), Technology-supported learning

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.