AI-Guided Psychodrama Materials for Psychiatric Nursing: Feasibility of AI-Validated Synthetic Cases
Other Titles
AI-Guided Psychodrama Materials for Psychiatric Nursing: Feasibility of AI-Validated Synthetic Cases: A Six-Stage APP Validation Report [Title Slide]
Abstract
Background: Psychodrama is widely used in psychiatric–mental health nursing to help learners recognize and reorganize dysfunctional family-of-origin or role patterns, but faculty still lack standardized, reproducible, and ethically safe scripts (Giacomucci, 2021; Beauvais, Özbas, & Wheeler, 2019; Çataldas, Atkan, & Eminoğlu, 2024). Recent work on AI in nursing education and on privacy-preserving synthetic data suggests that high-fidelity, non–patient-derived materials can be generated if outputs are quality-checked (Lifshits & Rosenberg, 2024; Cucci, 2025; Qian et al., 2024; Mendes, Barbar, & Refaie, 2025).
Purpose: To test the feasibility of an AI workflow that generates a synthetic psychodrama case, converts it into multimodal teaching assets, and releases media products only when fidelity indicators reach ≥ .80.
Methods: A six-stage workflow using a large language model and a multimodal note tool was run.
- Stage 1 created an English scenario of an adult daughter unable to express needs to a critical mother.
- Stage 2 expanded it into a psychodrama script with scene-by-scene directions, named roles (protagonist, mother, internalized critical voice), and explicit techniques (soliloquy, externalization, role reversal, doubling).
- Stage 3 auto-generated narration text, a slide-style outline, and scene-level visual descriptors.
- Stage 4 compared the original script (TEXT A) and each auto-generated product (TEXT B) on three indicators: role/character consistency, therapeutic-intention alignment, and emotional-intensity retention.
- Stage 5 regenerated only the sections whose scores were < .80.
- Stage 6 produced narration/audio text and scene-by-scene video prompts once all products met the ≥ .80 requirement.
Results: The pipeline ran entirely on synthetic data. Early outputs showed small losses of emotional tone and occasional omission of technique labels; targeted regeneration corrected these issues and raised all indicators to ≥ .80. Media-ready assets were aligned with the instructional outline, and indicator reports provided faculty with an auditable basis for release.
Conclusions/Implications: This workflow is an instructional-design innovation that can shorten faculty preparation, preserve core psychodrama elements, and standardize multimodal materials in psychiatric–mental health nursing. It also offers a repeatable QA loop for LMS/hybrid delivery without exposing identifiable clinical material; effectiveness claims should await expert validation and outcome data.
Notes
References:
Beauvais, A. M., Özbas, A. A., & Wheeler, K. (2019). End-of-life psychodrama: Influencing nursing students’ communication skills, attitudes, emotional intelligence and self-reflection. Journal of Psychiatric Nursing, 10(3), 103–110. https://doi.org/10.14744/phd.2019.96636
Çataldas, S. K., Atkan, F., & Eminoğlu, A. (2024). The effect of psychodrama-based intervention on therapeutic communication skills and cognitive flexibility among nursing students: A 12-month follow-up study. Nurse Education in Practice, 80, 104118. https://doi.org/10.1016/j.nepr.2024.104118
Giacomucci, S. (2021). History of sociometry, psychodrama, group psychotherapy, and Jacob L. Moreno. In S. Giacomucci (Ed.), Social work, sociometry, and psychodrama (pp. 31–52). Springer. https://doi.org/10.1007/978-981-33-6342-7_3
Lifshits, I., & Rosenberg, D. (2024). Artificial intelligence in nursing education: A scoping review. Nurse Education in Practice, 80, 104148. https://doi.org/10.1016/j.nepr.2024.104148
Cucci, F., Marasciulo, D., Romani, M., Soldano, G., Cascio, D., De Nunzio, G., Caldararo, C., Rubbi, I., Vitale, E., Lupo, R., & Conte, L. (2025). The Contribution of Artificial Intelligence in Nursing Education: A Scoping Review of the Literature. Nursing Reports, 15(8), 283. https://doi.org/10.3390/nursrep15080283
Qian, Z., MacDonald, P., Wickramasinghe, N., Denaxas, S., & Titericz, G. (2024). Synthetic data for privacy-preserving clinical risk prediction. Scientific Reports, 14, 25676. https://doi.org/10.1038/s41598-024-72894-y
Mendes, J. M., Barbar, A., & Refaie, M. (2025). Synthetic data generation: A privacy-preserving approach to accelerate rare disease research. Frontiers in Digital Health, 7, 1563991. https://doi.org/10.3389/fdgth.2025.1563991
Sigma Membership
Lambda Beta at-Large
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Teaching and Learning Strategies, Faculty Development, Curriculum Development, Artificial Intelligence, Psychodrama
Recommended Citation
Ho, Yen-Chung and Sung, Chien Mei, "AI-Guided Psychodrama Materials for Psychiatric Nursing: Feasibility of AI-Validated Synthetic Cases" (2026). International Nursing Research Congress (INRC). 77.
https://www.sigmarepository.org/inrc/2026/presentations_2026/77
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-27
AI-Guided Psychodrama Materials for Psychiatric Nursing: Feasibility of AI-Validated Synthetic Cases
Toronto, Ontario, Canada
Background: Psychodrama is widely used in psychiatric–mental health nursing to help learners recognize and reorganize dysfunctional family-of-origin or role patterns, but faculty still lack standardized, reproducible, and ethically safe scripts (Giacomucci, 2021; Beauvais, Özbas, & Wheeler, 2019; Çataldas, Atkan, & Eminoğlu, 2024). Recent work on AI in nursing education and on privacy-preserving synthetic data suggests that high-fidelity, non–patient-derived materials can be generated if outputs are quality-checked (Lifshits & Rosenberg, 2024; Cucci, 2025; Qian et al., 2024; Mendes, Barbar, & Refaie, 2025).
Purpose: To test the feasibility of an AI workflow that generates a synthetic psychodrama case, converts it into multimodal teaching assets, and releases media products only when fidelity indicators reach ≥ .80.
Methods: A six-stage workflow using a large language model and a multimodal note tool was run.
- Stage 1 created an English scenario of an adult daughter unable to express needs to a critical mother.
- Stage 2 expanded it into a psychodrama script with scene-by-scene directions, named roles (protagonist, mother, internalized critical voice), and explicit techniques (soliloquy, externalization, role reversal, doubling).
- Stage 3 auto-generated narration text, a slide-style outline, and scene-level visual descriptors.
- Stage 4 compared the original script (TEXT A) and each auto-generated product (TEXT B) on three indicators: role/character consistency, therapeutic-intention alignment, and emotional-intensity retention.
- Stage 5 regenerated only the sections whose scores were < .80.
- Stage 6 produced narration/audio text and scene-by-scene video prompts once all products met the ≥ .80 requirement.
Results: The pipeline ran entirely on synthetic data. Early outputs showed small losses of emotional tone and occasional omission of technique labels; targeted regeneration corrected these issues and raised all indicators to ≥ .80. Media-ready assets were aligned with the instructional outline, and indicator reports provided faculty with an auditable basis for release.
Conclusions/Implications: This workflow is an instructional-design innovation that can shorten faculty preparation, preserve core psychodrama elements, and standardize multimodal materials in psychiatric–mental health nursing. It also offers a repeatable QA loop for LMS/hybrid delivery without exposing identifiable clinical material; effectiveness claims should await expert validation and outcome data.
Description
This presentation presents a six-stage AI workflow that turns a synthetic psychodrama script into validated, media-ready teaching materials for psychiatric–mental health nursing, using automated ≥ .80 checks to preserve roles, therapeutic intention, and emotional tone while reducing faculty workload.