A systematic mapping review on how generative artificial intelligence impacts social and emotional learning: A case of large language model chatbots

Abstract

Abstract Large language model (LLM) chatbots have rapidly emerged as powerful tools in education, offering new avenues to support social and emotional learning (SEL). Literature lacks synthesis studies related to SEL competencies and how LLM chatbots are used to foster them, that is, affordances. Therefore, this systematic mapping review categorizes and synthesizes current literature on the utilization of LLM chatbots for SEL development in higher education. Following PRISMA, we screened and analysed 43 peer‐reviewed studies, identifying 15 competencies across six SEL domains (cognitive, emotional, social, values, perspectives and identity), and 19 LLM chatbot affordances across 7 genres. Findings show that cognitive competencies (e.g., critical thinking, metacognition) and identity competencies (e.g., self‐efficacy, agency) are most frequently targeted, whereas values and perspective domains remain underrepresented. The most common affordances were immediate personalized feedback, information retrieval and interactive question and answer. We also addressed five ethical risks (transparency, privacy, equality, beneficence and affect/identity safety). We proposed an SEL development framework for higher education students using LLM chatbots. By focusing on these specific competencies and affordances, researchers can contribute to the advancement of SEL through the innovative use of LLM chatbots. We encourage researchers to conduct more studies to expand this framework by adding, removing or revising the competencies and affordances. Context and implications Rationale for this study: While large language model (LLM) chatbots are increasingly used in education, no comprehensive review existed to synthesize how their specific affordances map into the development of distinct social and emotional learning (SEL) competencies. A review is needed to address this critical gap for designing effective, ethically aware educational interventions in education. Why the new findings matter: This review provides the first structured framework linking 19 LLM affordances to 15 SEL competencies. The framework reveals a significant imbalance in development focus (e.g., cognitive overvalues) and a widespread lack of essential ethical safeguards, which is crucial for maximizing benefits and minimizing risks. Implications: For researchers, it provides a foundational SEL–LLM framework and agenda to explore underrepresented domains (e.g., values). Practitioners (educators, instructional designers) can use the affordance‐competency map to design targeted SEL interventions. Policy‐makers should prioritize ethical guidelines and support the development of specialized, equitable LLM tools for SEL.

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