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dc.contributor.advisorGabrieli, John
dc.contributor.authorLiu, Andi
dc.date.accessioned2025-10-06T17:38:12Z
dc.date.available2025-10-06T17:38:12Z
dc.date.issued2025-05
dc.date.submitted2025-06-23T14:02:52.798Z
dc.identifier.urihttps://hdl.handle.net/1721.1/162985
dc.description.abstractThis thesis tests two design questions for Large Language Model (LLM) Chatbot Therapists: Which therapeutic school suits an LLM best, and does an explicit Theory-of-Mind (ToM) reflection improve outcomes? We prompted GPT-4.1-mini to act as eight therapists — CBT, Narrative, Psychodynamic, and SFBT, each with and without a ToM step — and held 240 simulated sessions with scripted AI patients. SFBT achieved the greatest projected PHQ-9 improvement (around 4 points), significantly higher than CBT, Narrative, or Psychodynamic approaches. Immediate distress (SUDS) fell modestly and uniformly across schools. ToM reasoning did not alter either measure. The findings show that extra “thinking time” might not automatically translate into therapeutic gain, but also highlight a current strength of LLMs: executing brief, rule-based therapies.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://rightsstatements.org/page/InC-EDU/1.0/
dc.titleAll Therapies Are Equal - Unless You’re a Bot: Evaluating the Effectiveness of Four Therapy Schools for AI Chatbot Therapists
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


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