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A Systematic Mapping Review on How Generative Artificial Intelligence Impacts Social and Emotional Learning: A Case of Large Language Model Chatbots

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Title: A Systematic Mapping Review on How Generative Artificial Intelligence Impacts Social and Emotional Learning: A Case of Large Language Model Chatbots
Language: English
Authors: Haolan Liu, Xueqing Fang, Qingyuan Cui, Thomas K. F. Chiu (ORCID 0000-0003-2887-5477)
Source: Review of Education. 2026 14(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 42
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Influence of Technology, Social Emotional Learning, Natural Language Processing, Computer Mediated Communication, Affordances, Critical Thinking, Metacognition, Self Efficacy, Higher Education, Ethics, Risk
DOI: 10.1002/rev3.70141
ISSN: 2049-6613
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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1504119
Database: ERIC
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  Data: A Systematic Mapping Review on How Generative Artificial Intelligence Impacts Social and Emotional Learning: A Case of Large Language Model Chatbots
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  Data: <searchLink fieldCode="AR" term="%22Haolan+Liu%22">Haolan Liu</searchLink><br /><searchLink fieldCode="AR" term="%22Xueqing+Fang%22">Xueqing Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Qingyuan+Cui%22">Qingyuan Cui</searchLink><br /><searchLink fieldCode="AR" term="%22Thomas+K%2E+F%2E+Chiu%22">Thomas K. F. Chiu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2887-5477">0000-0003-2887-5477</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Review+of+Education%22"><i>Review of Education</i></searchLink>. 2026 14(1).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Y
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  Data: 42
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  Data: 2026
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  Data: Journal Articles<br />Information Analyses<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Influence+of+Technology%22">Influence of Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Emotional+Learning%22">Social Emotional Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Mediated+Communication%22">Computer Mediated Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Affordances%22">Affordances</searchLink><br /><searchLink fieldCode="DE" term="%22Critical+Thinking%22">Critical Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Risk%22">Risk</searchLink>
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  Data: 10.1002/rev3.70141
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  Data: 2049-6613
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
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