Effectiveness of Artificial Intelligence (AI) Interventions on Students' Rumination and Emotion Regulation: A Systematic Review
Downloads
Objective: This systematic review examined the evidence on the effectiveness of artificial intelligence (AI)-based interventions in improving emotion regulation and reducing rumination among students. Given the growing prevalence of emotional difficulties in academic settings, evaluating the role of AI-driven mental health tools is increasingly important.
Methods and Materials: A systematic review was conducted in accordance with PRISMA principles. Electronic databases, including PubMed, Scopus, ScienceDirect, and Google Scholar, were searched for relevant studies published between 2015 and 2024. Eligible studies examined AI-based psychological or digital mental health interventions targeting rumination, emotion regulation, or related mental health outcomes in student populations. After screening and eligibility assessment, 22 studies were included in the final synthesis. Owing to heterogeneity in study designs, interventions, and outcome measures, a narrative synthesis was performed.
Findings: The included studies comprised randomized controlled trials, quasi-experimental studies, mixed-methods research, and observational designs. Most studies reported favorable effects of AI-based tools, including chatbots, mobile applications, and digital platforms, on emotion regulation, stress reduction, and rumination-related outcomes. Improvements were particularly noted in cognitive reappraisal, mindfulness, and self-regulation, along with reductions in repetitive negative thinking. However, the overall strength of evidence was limited by methodological heterogeneity, inconsistent statistical reporting, small sample sizes in some studies, and insufficient long-term follow-up.
Conclusion: AI-based interventions appear promising as accessible and scalable tools to support students’ mental health, particularly by enhancing emotion regulation and reducing rumination. Nevertheless, current evidence remains preliminary, and more rigorous longitudinal and controlled studies are needed to establish their sustained effectiveness and clinical utility.
Downloads
Alligood, B., Fletcher, A., Vrshek-Schallhorn, S., & Jensen, M. (2024). Rumination as a Moderator of the Relation between Childhood Adversity Exposure and College Students’ Psychological Distress. Journal of Trauma Studies in Education, 3(2), 45-68. https://doi.org/10.70085/jtse.v3i2.6073
Austin, T., Smith, J., Rabin, B., Lindamer, L., Pittman, J., Justice, S., ... & Lantrip, C. (2024). The Effects of a Single-Session Virtual Rumination Intervention to Enhance Cognitive Functioning in Veterans with Subjective Cognitive Symptoms: Multimethod Pilot Study. JMIR Formative Research, 8, e48525. https://doi.org/10.2196/48525
Balabdaoui, F., Dittmann-Domenichini, N., Grosse, H., Schlienger, C., & Kortemeyer, G. (2024). A survey on students’ use of AI at a technical university. Discover Education, 3(1), 51. https://doi.org/10.1007/s44217-024-00136-4
Berking, M., & Wupperman, P. (2012). Emotion regulation and mental health: recent findings, current challenges, and future directions. Current opinion in psychiatry, 25(2), 128-134. https://journals.lww.com/co-psychiatry/abstract/2012/03000/emotion_regulation_and_mental_health__recent.11.aspx
Blanke, E. S., Neubauer, A. B., Houben, M., Erbas, Y., & Brose, A. (2022). Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect using dynamic structural equation modeling. Emotion, 22(8), 1773–1786. https://doi.org/10.1037/emo0000946
Boemo, T., Nieto, I., Vazquez, C., & Sánchez-López, A. (2022). Relations between emotion regulation strategies and affect in daily life: A systematic review and meta-analysis of studies using ecological momentary assessments. Neuroscience & Biobehavioral Reviews, 139, 104747. https://doi.org/10.1016/j.neubiorev.2022.104747
Clamor, A., Lincoln, T. M., & Schulze, L. (2024). Emotion Regulation Difficulties in Mental Disorders: A Systematic Review and Multilevel Meta-Analysis of 25 Years of Questionnaire Research. https://doi.org/10.31234/osf.io/yzuk8
Conley, C. S., Gonzales, C. H., Huguenel, B. M., Rauch, A. A., Kahrilas, I. J., Duffecy, J., & Silton, R. L. (2024). Benefits of a Technology-Delivered Mindfulness Intervention for Psychological Distress and Positive Wellbeing in Depressed College Students: Post-Intervention and Follow-Up Effects from an RCT. Mindfulness, 15(7), 1739-1758. https://doi.org/10.1007/s12671-024-02398-3
Conley, C. S., Kirsch, A. C., Dickson, D. A., & Bryant, F. B. (2014). Negotiating the transition to college: Developmental trajectories and gender differences in psychological functioning, cognitive-affective strategies, and social well-being. Emerging Adulthood, 2(3), 195-210. https://doi.org/10.1177/2167696814521808
Cook, L., Mostazir, M., & Watkins, E. (2019). Reducing stress and preventing depression (RESPOND): Randomized controlled trial of web-based rumination-focused cognitive behavioral therapy for high-ruminating university students. Journal of Medical Internet Research, 21(5), e11349. https://doi.org/10.2196/11349
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR mental health, 4(2), e7785. https://doi.org/10.2196/mental.7785
Frischholz, K., Tanaka, H., Shidara, K., Onishi, K., & Nakamura, S. (2024). Examining the Effects of Cognitive Behavioral Therapy with a Virtual Agent on User Motivation and Improvement in Psychological Distress and Anxiety: Two-Session Experimental Study. JMIR Formative Research, 8(1), e55234. https://doi.org/10.2196/mental.7785
Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019, September). EMMA: an emotion-aware wellbeing chatbot. In 2019, the 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1-7). IEEE. https://doi.org/10.1109/ACII.2019.8925455
Gross, J. J. (1999). Emotion and emotion regulation. Handbook of personality: Theory and research, 2, 525-552.
Gross, J. J. (2024). Conceptual foundations of emotion regulation. In J. J. Gross & B. Q. Ford (Eds.), Handbook of emotion regulation (3rd ed., pp. 3–12). The Guilford Press.
Hamiduzzaman, M., Gaffney, H. J., Jindal, S., Patra, M., Gudur, R., Pit, S., & Rahman, A. (2024). Virtual healthcare for older adults with preventable chronic conditions: A meta-synthesis of quality aspects. Journal of Applied Gerontology, 07334648241296791. https://doi.org/10.1177/07334648241296791
He, T., Fu, G., Yu, Y., Wang, F., Li, J., Zhao, Q., ... & Yang, B. X. (2023). Towards a psychological generalist AI: A survey of current applications of large language models and future prospects. arXiv preprint arXiv:2312.04578. https://doi.org/10.48550/arXiv.2312.04578
Hoffner, C. A., & Lee, S. (2015). Mobile phone use, emotion regulation, and well-being. Cyberpsychology, Behavior, and Social Networking, 18(7), 411-416. https://doi.org/10.1089/cyber.2014.0487
Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and health, 6(11), e12106. https://doi.org/10.2196/12106
Lewis, S., Ainsworth, J., Sanders, C., Stockton-Powdrell, C., Machin, M., Whelan, P., ... & Wykes, T. (2020). Smartphone-enhanced symptom management in psychosis: open, randomized controlled trial. Journal of Medical Internet Research, 22(8), e17019. https://doi.org/10.2196/17019
Li, Y., Chung, T. Y., Lu, W., Li, M., Ho, Y. W. B., He, M., ... & Bressington, D. (2024). Chatbot-Based Mindfulness-Based Stress Reduction Program for University Students With Depressive Symptoms: Intervention Development and Pilot Evaluation. Journal of the American Psychiatric Nurses Association, 10783903241302092. https://doi.org/10.1177/10783903241302092
Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022). Using AI chatbots to provide self-help depression interventions for university students: A randomized trial of effectiveness. Internet Interventions, 27, 100495. https://doi.org/10.1016/j.invent.2022.100495
Liu, J. M., Gao, M., Sabour, S., Chen, Z., Huang, M., & Lee, T. (2025). Enhanced Large Language Models for Effective Screening of Depression and Anxiety. arXiv preprint arXiv:2501.08769 https://doi.org/10.48550/arXiv.2501.08769
Liu, Y., Ding, X., Peng, S., & Zhang, C. (2024). Leveraging ChatGPT to optimize depression intervention through explainable deep learning. Frontiers in psychiatry, 15, 1383648. https://doi.org/10.3389/fpsyt.2024.1383648
Kallivalappil, N., D’souza, K., Deshmukh, A., Kadam, C., & Sharma, N. (2023, July). Empath. AI: a context-aware chatbot for emotional detection and support. In 2023, the 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCCNT56998.2023.10306584
Marques, H., Brites, R., Nunes, O., Hipólito, J., & Brandão, T. (2023). Attachment, emotion regulation, and burnout among university students: a mediational hypothesis. Educational Psychology, 43(4), 344–362. https://doi.org/10.1080/01443410.2023.22128893
Mulawarman, M., Antika, E. R., Afriwilda, M. T., Prabawa, A. F. I., Nadhita, G., & Purboaji, N. (2023). How Does Resilience Predict Cognitive Rumination in College Students? KONSELOR, 12(4), 302-312. https://counselor.ppj.unp.ac.id/index.php/konselor/about/submissions
Muris, P., Roelofs, J., Rassin, E., Franken, I., & Mayer, B. (2005). Mediating effects of rumination and worry on the links between neuroticism, anxiety, and depression. Personality and Individual Differences, 39(6), 1105–1111. https://doi.org/10.1016/j.paid.2005.04.005
Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking Rumination. Perspectives on Psychological Science, 3(5), 400-424. https://doi.org/10.1111/j.1745-6924.2008.00088.x
Marques, H., Brites, R., Nunes, O., Hipólito, J., & Brandão, T. (2023). Attachment, emotion regulation, and burnout among university students: a mediational hypothesis. Educational Psychology, 43(4), 344–362. https://doi.org/10.1080/01443410.2023.22128893
Enny, Fitriani, Nurasyah, Johannes, Rini, Fadhillah, Putri. (2024). Emotion Regulation in Psychology in Students. doi: 10.55299/ijere.v3i1.831
Blanke, E. S., Neubauer, A. B., Houben, M., Erbas, Y., & Brose, A. (2022). Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect using dynamic structural equation modeling. Emotion, 22(8), 1773–1786. https://doi.org/10.1037/emo0000946
Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and Decision Making. Annual Review of Psychology, 66(1), 799–823. https://doi.org/10.1146/annurev-psych-010213-115043
Mulawarman, Mulawarman., Eni, Rindi, Antika., Mayang, T., Afriwilda., Abi, Fa'izzarahman, Prabawa., Galuh, Nadhita., Nawang, Purboaji. (2024). How does Resilience Predict Cognitive Rumination in College Students? Konselor, 12(4):302-312
Nolen-Hoeksema, S., & Morrow, J. (1993). Effects of rumination and distraction on naturally occurring depressed mood. Cognition and Emotion, 7(6), 561–570. https://doi.org/10.1016/j.paid.2005.04.005
Nolen-Hoeksema, S., Parker, L. E., & Larson, J. (1994). Ruminative coping with depressed mood following loss. Journal of Personality and Social Psychology, 67(1), 92–111. https://doi.org/10.1037/0022-3514.67.1.92
Oliveira, A. L. S., Matos, L. N., Junior, M. C., & Delabrida, Z. N. C. (2021, September). An initial assessment of a chatbot for rumination-focused cognitive behavioral therapy (RFCBT) in college students. In International Conference on Computational Science and Its Applications (pp. 549-564). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86979-3_39
Priyanka, M., & Subashini, R. (2024). Does artificial intelligence mediate between ergonomics and the drivers of ergonomics innovations–Empirical evidence. Int. Res. J. Multidisc. Scope, 5(2), 162-174. https://www.irjms.com/wp-content/uploads/2024/04/Manuscript_IRJMS_0398_WS.pdf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf
Sadeh-Sharvit, S., Camp, T. D., Horton, S. E., Hefner, J. D., Berry, J. M., Grossman, E., & Hollon, S. D. (2023). Effects of an artificial intelligence platform for behavioral interventions on depression and anxiety symptoms: randomized clinical trial. Journal of Medical Internet Research, 25, e46781. https://doi.org/10.2196/46781
Shati, A. A. G., & Nasser, Z. A. R. (2024). Emotional Regulation Among Postgraduate Students. Thi Qar Arts Journal, 1(45), 225-225. https://doi.org/10.32792/tqartj.v1i45.547
Schlosser, D. A., Campellone, T. R., Truong, B., Etter, K., Vergani, S., Komaiko, K., & Vinogradov, S. (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophrenia Bulletin, 44(5), 1010-1020. https://doi.org/10.1093/schbul/sby078
Schillings, C., Meißner, E., Erb, B., Bendig, E., Schultchen, D., & Pollatos, O. (2024). Effects of a chatbot-based intervention on stress and health-related parameters in a stressed sample: randomized controlled trial. JMIR Mental Health, 11(1), e50454. https://doi.org/10.2196/50454
Shidara, K., Tanaka, H., Adachi, H., Kanayama, D., Sakagami, Y., Kudo, T., & Nakamura, S. (2022). Automatic thoughts and facial expressions in cognitive restructuring with virtual agents. Frontiers in Computer Science, 4, 762424. https://doi.org/10.3389/fcomp.2022.762424
Shin, M., & Kim, J. (2023). Enhancing Human Persuasion with Large Language Models. arXiv preprint arXiv:2311.16466. https://doi.org/10.48550/arXiv.2311.16466
Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition, assessment, and conceptualization of this multifaceted construct. Clinical psychology review, 29(2), 116-128. https://doi.org/10.1016/j.cpr.2008.10.003
Striegl, J., Richter, J. W., Grossmann, L., Bråstad, B., Gotthardt, M., Rück, C., ... & Loitsch, C. (2024). Deep learning-based dimensional emotion recognition for conversational agent-based cognitive behavioral therapy. PeerJ Computer Science, 10, e2104. https://doi.org/10.7717/peerj-cs.2104
Vestad, L., & Tharaldsen, K. B. (2022). Building social and emotional competencies for coping with academic stress among students in lower secondary school. Scandinavian Journal of Educational Research, 66(5), 907-921. https://doi.org/10.1080/00313831.2021.1939145
Wang, H., Burić, I., Chang, M. L., & Gross, J. J. (2023). Teachers’ emotion regulation and related environmental, personal, instructional, and well-being factors: A meta-analysis. Social psychology of education, 26(6), 1651-1696. https://doi.org/10.1007/s11218-023-09810-1
Wang, L., & Miller, L. (2023). Assessment and disruption of ruminative episodes to enhance mobile cognitive behavioral therapy just-in-time adaptive interventions in clinical depression: pilot randomized controlled trial. JMIR Formative Research, 7, e37270. https://doi.org/10.2196/37270
Weiss, E. M., Staggl, S., Holzner, B., Rumpold, G., Dresen, V., & Canazei, M. (2024). Preventive Effect of a 7-Week App-Based Passive Psychoeducational Stress Management Program on Students. Behavioral Sciences, 14(3), 180. https://doi.org/10.3390/bs14030180
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., ... & Wang, D. (2024). Mental-LLM: Leveraging large language models for mental health prediction via online text data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(1), 1-32. https://doi.org/10.1145/3643540
Zafar, M. (2024). Enhancing University Students’ Mental Health under Artificial Intelligence: Principles of Behaviour Therapy. OBM Neurobiology, 8(2), 1-5. http://dx.doi.org/10.21926/obm.neurobiol.2402225
Copyright (c) 2026 International Journal of Body, Mind and Culture
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
