Large Language Model–Based Intervention for Emotion Regulation and Rumination Among University Students: A Quasi-Experimental Study
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Objective: This study evaluated the effectiveness of a large language model–based intervention in improving emotion regulation and reducing rumination among university students.
Methods and Materials: This quasi-experimental controlled study used a pretest–posttest design with a three-month follow-up. Thirty university students were assigned to an intervention group (n = 15) or a control group (n = 15). The intervention group received eight structured sessions delivered through ChatGPT, focusing on emotional awareness, cognitive restructuring, mindfulness, stress management, problem-solving, and prevention of ruminative relapse. The control group received no intervention. Data were collected using the Emotion Regulation Questionnaire and the Ruminative Responses Scale. Analyses included paired t-tests, ANCOVA, and repeated-measures ANOVA.
Findings: In the intervention group, rumination decreased from 50.73 ± 12.45 at pretest to 35.80 ± 9.80 at posttest (t = 17.39, p < 0.001), while emotion regulation increased from 46.33 ± 6.51 to 55.20 ± 5.59 (t = −16.91, p < 0.001). ANCOVA showed significant between-group effects for rumination (F = 14.75, p = 0.001) and emotion regulation (F = 11.82, p = 0.002). Repeated-measures analysis confirmed significant time × group interactions for rumination (F = 16.4, p < 0.001, η² = 0.47) and emotion regulation (F = 11.5, p < 0.001, η² = 0.35), with effects maintained at three-month follow-up.
Conclusion: The large language model–based intervention significantly reduced rumination and improved emotion regulation among university students. Ethically guided AI-based interventions may support accessible student mental health care.
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