Optimization of Imagery Rescripting Research Using Generative Artificial Intelligence

This pilot randomized controlled trial will explore the application of large language models (LLMs) in the development of personalized therapeutic interventions. The study will focus on the emotional and psychophysiological effects of listening to autobiographical scenarios based on participants' childhood experiences of parental criticism and neutral events. All participants will be asked to recall and describe two critical and two neutral childhood memories. Based on this input, personalized scripts will be generated using Gemini, a large language model. Each script will be reviewed and, if necessary, modified by trained experimenters to ensure therapeutic coherence and alignment with imagery rescripting (ImRs) principles. On Day 1, all participants will listen to both critical and neutral personalized scenarios during the laboratory session. Participants will be randomly assigned to one of two groups: The experimental group will listen to modified versions of the critical memory scripts, in which a therapist figure will intervene to address the child's unmet needs-an application of imagery rescripting. The control group will listen to the same autobiographical content without any therapeutic intervention or modification. To assess physiological arousal, skin conductance will be continuously recorded throughout the session. After each scenario, participants will rate their emotional intensity and specific feelings (e.g., fear, sadness) on Likert scales. The experimental group will receive the ImRs intervention after the initial scenario phase. One week later, all participants will complete follow-up questionnaires assessing generalized anxiety (GAD-7) and the frequency of intrusive thoughts related to the memories. In addition, a panel of licensed cognitive-behavioral therapists will evaluate the generated scenarios for therapeutic quality. Their feedback will be used to assess the acceptability and coherence of AI-assisted therapeutic scripts. The study will test the feasibility of using LLM-generated content in clinical settings and aims to determine whether such interventions can reduce distress and intrusiveness while eliciting measurable emotional and physiological responses. Hypotheses: The AI-generated scripts will be positively evaluated by cognitive-behavioral therapists, with an average rating above 6 out of 10. The therapist ratings of the AI-generated scripts will not significantly differ from those of human-written scripts based on clinical interviews used in prior research. The criticism scenarios generated by the model will elicit anxiety responses in all participants. The level of anxiety evoked by the AI-generated criticism scenarios will correlate with participants' baseline fear of failure. Participants in the ImRs group will report fewer intrusive thoughts and lower generalized anxiety levels one week after the intervention compared to the control group.