Pre-experimental evaluation of a psychotherapeutic intervention using AI in a simulated case of alcoholism
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Published
2025-04-30
Horacio Daniel García
María Paz Hauser
Simon Martin Riberi Zunino
https://orcid.org/0000-0003-2628-6636
Abstract
We evaluated the effectiveness of a virtual assistant trained in cognitive-behavioral therapy to intervene in a simulated case of alcoholism. Using a single-measure pre-experimental design, two experts assessed the response of a GPT-4o-based virtual assistant, properly configured by parameter adjustment, to a simulated case of alcoholism. The measurement instrument was an ad hoc questionnaire with nine items and response options on a Likert scale from 0 to 9 points. The results showed a mean score of 7.66 in Intervention Skills, with high scores in Goal Setting (8.5), Ethical Adjustment (8), and Identification of Underlying Beliefs (8). The Change dimension obtained the lowest score (7). We observed discrepancies between the experts, particularly in Analytical Capacity and Self-Awareness. We conclude that the virtual assistant shows promising potential to complement traditional approaches to alcoholism treatment. This work is a pioneer in evaluating a virtual assistant specifically designed for alcoholism treatment in a Spanish-speaking context, which could significantly improve access and targeting of interventions in this population. We suggest conducting additional, more robust research, such as randomized controlled trials, to validate and expand these findings.
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Keywords
Alcoholism, Artificial Intelligence, Psychotherapy, Virtual Assistant
References
Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2(15), 100006. https://doi.org/10.1016/j.mlwa.2020.100006.
Beck, A. T., Wright, F. D., Newman, C. F., & Liese, B. S. (1993). Cognitive therapy of substance abuse. The Guilford Press.
Comisión Interamericana para el Control del Abuso de Drogas (CICAD). (2019). Informe sobre el consumo de drogas en las Américas 2019. Washington DC. https://www.oas.org/es/centro_noticias/comunicado_prensa.asp?sCodigo=C-014/19
Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018). Natural language processing of social media as screening for suicide risk. Biomedical Informatics Insights, 10. https://doi.org/10.1177/1178222618792860
Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D. (2020). Quantifying the association between psychotherapy content and clinical outcomes using deep learning. JAMA Psychiatry, 77(1), 35–43. https://doi.org/10.1001/jamapsychiatry.2019.2664
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19.
https://doi.org/10.2196/mental.7785
Gonzalez Larrondo, A. (2023). Aplicación de inteligencia artificial en procesos psicoterapéuticos. [Trabajo final para optar al grado de Licenciatura en Psicología, Universidad de la República]. Repositorio Institucional Universidad de la República.
https://www.colibri.udelar.edu.uy/jspui/bitstream/20.500.12008/39960/1/tfg_alexis_gonzalez_2.pdf.
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116. https://doi.org/10.1007/s11920-019-1094-0.
Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, P. (2014). Metodología de la investigación (6ta ed.). McGraw-Hill Education.
Kiluk, B. D., Nich, C., Buck, M. B., Devore, K. A., Frankforter, T. L., LaPaglia, D. M., Muvvala, S. B., & Carroll, K. M. (2018). Randomized clinical trial of computerized and clinician-delivered CBT in comparison with standard outpatient treatment for substance use disorders: Primary within-treatment and follow-up outcomes. American Journal of Psychiatry, 175(9), 853-863. https://doi.org/10.1176/appi.ajp.2018.17090978
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., & Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072.
Luxton, D. D., Pruitt, L. D., & Osenbach, J. E. (2014). Best practices for remote psychological assessment via telehealth technologies. Professional Psychology: Research and Practice, 45(1), 27–35. https://doi.org/10.1037/a0034547.
Malte, C. A., Dulin, P. L., Baer, J. S., Fortney, J. C., Danner, A. N., Lott, A. M. K., & Hawkins, E. J. (2021). Usability and acceptability of a mobile app for the self-management of alcohol misuse among veterans (Step Away): Pilot cohort study. JMIR Mhealth Uhealth, 9(4), e25927. https://doi.org/10.2196/25927
Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change (3rd edition). Guilford Press.
Ministerio de Salud de la Nación. (2023). Políticas regulatorias para reducir el uso nocivo de alcohol: Alcances, limitaciones y estrategias a implementar. Programa Nacional de Prevención y Lucha Contra el Consumo Excesivo de Alcohol. https://www.argentina.gob.ar/sites/default/files/bancos/2023-01/2023-01-politicas-regulatorias-para-reducir-uso-nocivo-alcohol.pdf
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. https://doi.org/10.1146/annurev-clinpsy-032816-044949.
Monteiro, M. G., Pantani, D., Pinsky, I., & Hernandes Rocha, T. A. (2023). Using the Pan American Health Organization Digital Conversational Agent to educate the public on alcohol use and health: Preliminary analysis. JMIR Formative Research, 7, e43165. https://doi.org/10.2196/43165.
Nilsen, P., Svedberg, P., Nygren, J., Frideros, M., Johansson, J., & Schueller, S. (2022). Accelerating the impact of artificial intelligence in mental healthcare through implementation science. Implementation Research and Practice, 3, 26334895221112033. https://doi.org/10.1177/26334895221112033.
Organización Mundial de la Salud. (2018). Informe mundial sobre el alcohol y la salud 2018. https://www.who.int/publications/i/item/9789241565639.
Organización Mundial de la Salud. (2024). Alcohol. https://www.who.int/es/news-room/fact-sheets/detail/alcohol
Organización Panamericana de la Salud. (2019). Alcohol consumption. PAHO. https://www.paho.org/en/enlace/alcohol-consumption
Roussos, A. J. (2007). El diseño de caso único en investigación en psicología clínica. Un vínculo entre la investigación y la práctica clínica. Revista Argentina de Clínica Psicológica, 16(3), 261-270.
Schwartz, B., Cohen, Z. D., Rubel, J. A., Zimmermann, D., Wittmann, W. W., & Lutz, W. (2021). Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychotherapy research, 31(1), 33–51. https://doi.org/10.1080/10503307.2020.1769219.
Sedotto, R. N. M, Edwards, A. E., Dulin, P. L., & King, D. K. (2024). Engagement with mHealth alcohol interventions: User perspectives on an app or chatbot-delivered program to reduce drinking. Healthcare, 12(1), 101.
https://doi.org/10.3390/healthcare12010101.
Stamatis, A., Dasyras, A., Gerontas, A., & Tambouris, E. (2020). Using chatbots and life events to provide public service information. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, Athens, 54-61. https://doi.org/10.1145/3428502.3428509
Sugarman, D. E., Nich, C., & Carroll, K. M. (2010). Coping strategy use following computerized cognitive-behavioral therapy for substance use disorders. Psychology of addictive behaviors, 24(4), 689–695. https://doi.org/10.1037/a0021584.
Tanana, M. J., Soma, C. S., Kuo, P. B., Bertagnolli, N. M., Dembe, A., Pace, B. T., Srikumar, V., Atkins, D. C., & Imel, Z. E. (2021). How do you feel? Using natural language processing to automatically rate emotion in psychotherapy. Behavior Research Methods, 53, 2069–2082. https://doi.org/10.3758/s13428-020-01531-z
Torous, J., Myrick, K. J., Rauseo-Ricupero, N., & Firth, J. (2020). Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health, 7(3), e18848. https://doi.org/10.2196/18848.
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. The Canadian Journal of Psychiatry, 64(7), 456-464. https://doi.org/10.1177/0706743719828977.
Beck, A. T., Wright, F. D., Newman, C. F., & Liese, B. S. (1993). Cognitive therapy of substance abuse. The Guilford Press.
Comisión Interamericana para el Control del Abuso de Drogas (CICAD). (2019). Informe sobre el consumo de drogas en las Américas 2019. Washington DC. https://www.oas.org/es/centro_noticias/comunicado_prensa.asp?sCodigo=C-014/19
Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018). Natural language processing of social media as screening for suicide risk. Biomedical Informatics Insights, 10. https://doi.org/10.1177/1178222618792860
Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D. (2020). Quantifying the association between psychotherapy content and clinical outcomes using deep learning. JAMA Psychiatry, 77(1), 35–43. https://doi.org/10.1001/jamapsychiatry.2019.2664
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19.
https://doi.org/10.2196/mental.7785
Gonzalez Larrondo, A. (2023). Aplicación de inteligencia artificial en procesos psicoterapéuticos. [Trabajo final para optar al grado de Licenciatura en Psicología, Universidad de la República]. Repositorio Institucional Universidad de la República.
https://www.colibri.udelar.edu.uy/jspui/bitstream/20.500.12008/39960/1/tfg_alexis_gonzalez_2.pdf.
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116. https://doi.org/10.1007/s11920-019-1094-0.
Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, P. (2014). Metodología de la investigación (6ta ed.). McGraw-Hill Education.
Kiluk, B. D., Nich, C., Buck, M. B., Devore, K. A., Frankforter, T. L., LaPaglia, D. M., Muvvala, S. B., & Carroll, K. M. (2018). Randomized clinical trial of computerized and clinician-delivered CBT in comparison with standard outpatient treatment for substance use disorders: Primary within-treatment and follow-up outcomes. American Journal of Psychiatry, 175(9), 853-863. https://doi.org/10.1176/appi.ajp.2018.17090978
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., & Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072.
Luxton, D. D., Pruitt, L. D., & Osenbach, J. E. (2014). Best practices for remote psychological assessment via telehealth technologies. Professional Psychology: Research and Practice, 45(1), 27–35. https://doi.org/10.1037/a0034547.
Malte, C. A., Dulin, P. L., Baer, J. S., Fortney, J. C., Danner, A. N., Lott, A. M. K., & Hawkins, E. J. (2021). Usability and acceptability of a mobile app for the self-management of alcohol misuse among veterans (Step Away): Pilot cohort study. JMIR Mhealth Uhealth, 9(4), e25927. https://doi.org/10.2196/25927
Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change (3rd edition). Guilford Press.
Ministerio de Salud de la Nación. (2023). Políticas regulatorias para reducir el uso nocivo de alcohol: Alcances, limitaciones y estrategias a implementar. Programa Nacional de Prevención y Lucha Contra el Consumo Excesivo de Alcohol. https://www.argentina.gob.ar/sites/default/files/bancos/2023-01/2023-01-politicas-regulatorias-para-reducir-uso-nocivo-alcohol.pdf
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. https://doi.org/10.1146/annurev-clinpsy-032816-044949.
Monteiro, M. G., Pantani, D., Pinsky, I., & Hernandes Rocha, T. A. (2023). Using the Pan American Health Organization Digital Conversational Agent to educate the public on alcohol use and health: Preliminary analysis. JMIR Formative Research, 7, e43165. https://doi.org/10.2196/43165.
Nilsen, P., Svedberg, P., Nygren, J., Frideros, M., Johansson, J., & Schueller, S. (2022). Accelerating the impact of artificial intelligence in mental healthcare through implementation science. Implementation Research and Practice, 3, 26334895221112033. https://doi.org/10.1177/26334895221112033.
Organización Mundial de la Salud. (2018). Informe mundial sobre el alcohol y la salud 2018. https://www.who.int/publications/i/item/9789241565639.
Organización Mundial de la Salud. (2024). Alcohol. https://www.who.int/es/news-room/fact-sheets/detail/alcohol
Organización Panamericana de la Salud. (2019). Alcohol consumption. PAHO. https://www.paho.org/en/enlace/alcohol-consumption
Roussos, A. J. (2007). El diseño de caso único en investigación en psicología clínica. Un vínculo entre la investigación y la práctica clínica. Revista Argentina de Clínica Psicológica, 16(3), 261-270.
Schwartz, B., Cohen, Z. D., Rubel, J. A., Zimmermann, D., Wittmann, W. W., & Lutz, W. (2021). Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychotherapy research, 31(1), 33–51. https://doi.org/10.1080/10503307.2020.1769219.
Sedotto, R. N. M, Edwards, A. E., Dulin, P. L., & King, D. K. (2024). Engagement with mHealth alcohol interventions: User perspectives on an app or chatbot-delivered program to reduce drinking. Healthcare, 12(1), 101.
https://doi.org/10.3390/healthcare12010101.
Stamatis, A., Dasyras, A., Gerontas, A., & Tambouris, E. (2020). Using chatbots and life events to provide public service information. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, Athens, 54-61. https://doi.org/10.1145/3428502.3428509
Sugarman, D. E., Nich, C., & Carroll, K. M. (2010). Coping strategy use following computerized cognitive-behavioral therapy for substance use disorders. Psychology of addictive behaviors, 24(4), 689–695. https://doi.org/10.1037/a0021584.
Tanana, M. J., Soma, C. S., Kuo, P. B., Bertagnolli, N. M., Dembe, A., Pace, B. T., Srikumar, V., Atkins, D. C., & Imel, Z. E. (2021). How do you feel? Using natural language processing to automatically rate emotion in psychotherapy. Behavior Research Methods, 53, 2069–2082. https://doi.org/10.3758/s13428-020-01531-z
Torous, J., Myrick, K. J., Rauseo-Ricupero, N., & Firth, J. (2020). Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health, 7(3), e18848. https://doi.org/10.2196/18848.
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. The Canadian Journal of Psychiatry, 64(7), 456-464. https://doi.org/10.1177/0706743719828977.
How to Cite
Pre-experimental evaluation of a psychotherapeutic intervention using AI in a simulated case of alcoholism. (2025). Puerto Rican Journal of Psychology, 36(1), 28-38. https://doi.org/10.55611/
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Artículo de investigación
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How to Cite
Pre-experimental evaluation of a psychotherapeutic intervention using AI in a simulated case of alcoholism. (2025). Puerto Rican Journal of Psychology, 36(1), 28-38. https://doi.org/10.55611/