Mixed Methods Feasibility Study of Socrates 2.0, an AI-Based Cognitive Reappraisal Tool for Reducing Mental Health Symptoms

Background: Innovative, scalable mental health tools are needed to address systemic provider shortages and accessibility barriers. Large language model (LLM)-based tools can provide real-time, tailored feedback to help users engage in cognitive reappraisal outside traditional therapy sessions. Socrates 2.0 is a multi-agent artificial intelligence (AI) tool that guides users through Socratic dialogue. This study examined its feasibility, acceptability, and potential for symptom reduction.Methods: Sixty-one adults enrolled in a four-week mixed-methods pre-clinical feasibility study. Participants used Socrates 2.0 as desired and completed self-report measures of depression, social anxiety, posttraumatic stress, and obsessive-compulsive symptoms at baseline and one-month follow-up. Feasibility, acceptability, and appropriateness along with usability and working alliance were assessed via validated measures. Semi-structured interviews explored user experiences and perceptions.Results: Participants engaged with Socrates 2.0 an average of 6.70 (SD=4.57) times over four weeks. Feasibility (mean=4.26, SD=0.67), acceptability (mean=4.16, SD=0.84), and usability ratings were high. Participants reported moderate reductions in depression (effect size d=0.30), social anxiety (d=0.25), obsessive-compulsive (d=0.33), and posttraumatic stress (d=0.28) symptoms. Working alliance scores suggested a moderately strong perceived bond with the AI tool. Qualitative feedback indicated that the nonjudgmental, on-demand nature of Socrates 2.0 encouraged self-reflection and exploration. Some users critiqued the repeated questioning style and limited conversation depth.Conclusions: Socrates 2.0 was perceived as feasible, acceptable, and moderately helpful for self-guided cognitive reappraisal, demonstrating potential as an adjunct to traditional therapy. Further research, including randomized trials, is needed to determine effectiveness across different populations, optimize personalization, and address repetitive conversational loops.