AI Technologies for Social Emotional Learning: Recent Research and Future Directions
Open accessAbstract
Purpose: This study aims to explore the potential benefits of integrating Artificial Intelligence (AI) with Social Emotional Learning (SEL) in educational settings. Design/methodology/approach: A systematic review of emerging AI technologies such as virtual reality, chatbots, sentiment analysis tools, gamification and wearable devices is conducted to assess their applicability in enhancing SEL. Findings: AI technologies present opportunities for personalized support, increased engagement, empathy development and promotion of well-being within SEL frameworks. Research limitations/implications: Future research should focus on addressing ethical concerns, fostering interdisciplinary collaborations, conducting longitudinal studies, promoting cultural sensitivity and developing robust ecosystems for AI in SEL. Originality/value: This study contributes by outlining pathways for leveraging AI to create inclusive and supportive learning environments that nurture students' socio-emotional competencies, preparing them for success in a globally connected world.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many AI applications in education tend to prioritize individualized learning over collaborative and socio-emotional learning.
“This gap supports critiques raised by Sethi and Jain [60] and Zheng et al. [61], who argue that many AI applications in education tend to prioritize individualized learning over collaborative and socio-emotional learning.”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [60]
Cites
Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011) · Child Development · doi:10.1111/j.1467-8624.2010.01564.x · Cited by 8,746 · see citing works
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372 · see citing works
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification
Hamari, J., Koivisto, J., & Sarsa, H. (2014) · 2014 47th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2014.377 · Cited by 5,201 · see citing works
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396 · see citing works
Enhancing Our Lives with Immersive Virtual Reality
Slater, M., & Sanchez-Vives, M. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00074 · Cited by 1,993 · see citing works
A LITERATURE REVIEW ON IMMERSIVE VIRTUAL REALITY IN EDUCATION: STATE OF THE ART AND PERSPECTIVES.
Freina, L., & Ott, M. (2015) · eLearning and Software for Education · doi:10.12753/2066-026x-15-020 · Cited by 1,427 · see citing works
Conversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · see citing works
From Game Design Elements to Gamefulness: Defining “Gamification”
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. E. (2011) · IT University Of Copenhagen (IT University of Copenhagen) · Cited by 1,341 · see citing works
Augmented Reality in Education and Training
Lee, K. (2012) · TechTrends · doi:10.1007/s11528-012-0559-3 · Cited by 1,115 · see citing works
Dynamics of affective states during complex learning
D’Mello, S. K., & Graesser, A. C. (2011) · Learning and Instruction · doi:10.1016/j.learninstruc.2011.10.001 · Cited by 1,101 · see citing works
Parsons, T. D., & Rizzo, A. A. (2008) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2007.07.007 · Cited by 994 · see citing works
Baker, R. S. J. D., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.12.003 · Cited by 936 · see citing works
Immersion and Emotion: Their Impact on the Sense of Presence
Baños, R., Botella, C., Alcañíz, M., Liaño, V., Guerrero, B., & Solaz, B. R. (2004) · CyberPsychology & Behavior · doi:10.1089/cpb.2004.7.734 · Cited by 661 · see citing works
Dickey, M. D. (2005) · Educational Technology Research and Development · doi:10.1007/bf02504866 · Cited by 628 · see citing works
Fundamentals of physiological computing
Fairclough, S. H. (2009) · Interacting with Computers · doi:10.1016/j.intcom.2008.10.011 · Cited by 608 · see citing works
Chatbots and the new world of HCI
Følstad, A., & Brandtzæg, P. B. (2017) · interactions · doi:10.1145/3085558 · Cited by 520 · see citing works
(2012) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 484 · see citing works
An Empirical Test of the Theory of Gamified Learning
Landers, R. N., & Landers, A. K. (2014) · Simulation & Gaming · doi:10.1177/1046878114563662 · Cited by 363 · see citing works
Bartneck, C., Duenser, A., Moltchanova, E., & Zawieska, K. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121595 · Cited by 317 · see citing works
When the going gets tough: Grit predicts costly perseverance
Lucas, G., Gratch, J. S., Cheng, L., & Marsella, S. (2015) · Journal of Research in Personality · doi:10.1016/j.jrp.2015.08.004 · Cited by 219 · see citing works
Assessing reactivity to virtual reality alcohol based cues
Bordnick, P. S., Traylor, A., Copp, H. L., Graap, K. M., Carter, B., Ferrer, M., & Walton, A. P. (2008) · Addictive Behaviors · doi:10.1016/j.addbeh.2007.12.010 · Cited by 193 · see citing works
Barab, S. A., Pettyjohn, P., Gresalfi, M. S., Volk, C., & Solomou, M. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.08.001 · Cited by 154 · see citing works
Riva, G., Gaggioli, A., Grassi, A., Raspelli, S., Cipresso, P., Pallavicini, F., Cinzia, V., Andrea, G., Stefano, G., & Donvito, G. (2011) · Studies in health technology and informatics · doi:10.3233/978-1-60750-706-2-493 · Cited by 80 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Systematic review of artificial intelligence in education: trends, benefits, and challenges
Garzón, J., Patiño, E., & Marulanda, C. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9080084 · Cited by 265 · see citing works
“This gap supports critiques raised by Sethi and Jain [60] and Zheng et al. [61], who argue that many AI applications in education tend to prioritize individualized learning over collaborative and socio-emotional learning.”
Shi, J., Liu, W., & Hu, K. (2025) · Behavioral Sciences · doi:10.3390/bs15050705 · Cited by 77 · see citing works
Digital Mirrors: AI Companions and the Self
Kouros, T., & Papa, V. (2024) · Societies · doi:10.3390/soc14100200 · Cited by 66 · see citing works
- Authors
- Sethi, S. S., & Jain, K.
- Affiliations
- Malaviya National Institute of Technology Jaipur (IN)
- Emory University (US)
- Corresponding author
- Sethi, Surbhi Seema
- ORCID
- Jain, Kanishk
- Venue
- Journal of Research in Innovative Teaching & Learning
- Publisher
- Emerald
- Published
- 2024-05-27
- Volume
- 17
- Issue
- 2
- Pages
- 213-225
- ISSN
- 2397-7604 (ISSN-L)
- OpenAlex
- W4398244382
- Semantic Scholar
- 8420e9bc27b01fe9d8fd09c6d62940b2584dc294
- Cited by
- 111
- References
- 24
- Citations by year
- 2026: 47 · 2025: 54 · 2024: 10
- FWCI
- 41.74
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Research in Innovative Teaching & Learning (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Early Childhood Education and Development
- Keywords
- artificial intelligence in education, social-emotional learning, virtual reality, chatbots, sentiment analysis, gamification, wearable devices, personalized learning, student engagement, empathy development, student well-being, socioemotional skills, AI ethics in education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
