Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT
Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/12/1/29 |
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| author | Valeria Cioffi Ottavio Ragozzino Lucia Luciana Mosca Enrico Moretto Enrica Tortora Annamaria Acocella Claudia Montanari Antonio Ferrara Stefano Crispino Elena Gigante Alexander Lommatzsch Mariano Pizzimenti Efisio Temporin Valentina Barlacchi Claudio Billi Giovanni Salonia Raffaele Sperandeo |
| author_facet | Valeria Cioffi Ottavio Ragozzino Lucia Luciana Mosca Enrico Moretto Enrica Tortora Annamaria Acocella Claudia Montanari Antonio Ferrara Stefano Crispino Elena Gigante Alexander Lommatzsch Mariano Pizzimenti Efisio Temporin Valentina Barlacchi Claudio Billi Giovanni Salonia Raffaele Sperandeo |
| author_sort | Valeria Cioffi |
| collection | DOAJ |
| description | Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their feedback was evaluated by Gestalt psychotherapy trainees using a Likert scale rating of satisfaction. Statistical analysis, using the statistical package SPSS version 25 and applying principal component analysis (PCA) and one-way analysis of variance (ANOVA), demonstrated significant differences in favor of pre-trained AI feedback. PCA highlighted four components of the questionnaire: relational and emotional (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction obtained from the three kinds of supervisory feedback were compared using ANOVA. The feedback generated by the pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3)) in C4; in C1, the superiority of f2 over f1 but not over f3 appears significant. These results suggest that AI, when appropriately calibrated, may be an appreciable tool for complementing the effectiveness of clinical supervision, offering an innovative blended supervision methodology, in particular in the area of career guidance. |
| format | Article |
| id | doaj-art-ca16cf12a7ef49dd9155a158adb32aa4 |
| institution | OA Journals |
| issn | 2227-9709 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-ca16cf12a7ef49dd9155a158adb32aa42025-08-20T02:11:26ZengMDPI AGInformatics2227-97092025-03-011212910.3390/informatics12010029Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPTValeria Cioffi0Ottavio Ragozzino1Lucia Luciana Mosca2Enrico Moretto3Enrica Tortora4Annamaria Acocella5Claudia Montanari6Antonio Ferrara7Stefano Crispino8Elena Gigante9Alexander Lommatzsch10Mariano Pizzimenti11Efisio Temporin12Valentina Barlacchi13Claudio Billi14Giovanni Salonia15Raffaele Sperandeo16SiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalyIPGE Istituto di Psicoterapia della Gestalt Espressiva, Via Costantino Morin, 24-00195 Roma, ItalyASPIC Scuola di Psicoterapia, Via Vittore Carpaccio, 32-00147 Roma, ItalyiGAT Istituto di Psicoterapia della Gestalt e Analisi Transazionale, Via Pirro Ligorio, 20-80129 Napoli, ItalyIGA Istituto Gestalt Analitica, Via Padre Semeria, 33-00154 Roma, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 91100 Trapani, ItalyIGP Istituto Gestalt di Puglia, 73010 Arnesano, ItalySGT Scuola Gestalt Torino, Via Po, 14-10123 Torino, ItalyIGR Istituto Gestalt Romagna, 48121 Ravenna, ItalyIGF Istituto Gestalt Firenze, Scuola di Specializzazione in Psicoterapia della Gestalt a Orientamento Fenomenologico-Esistenziale, 50100 Firenze, ItalyScuola di Specializzazione in Psicoterapia della Gestalt CGV Centro Gestalt viva Claudio Naranjo, 57125 Livorno, ItalyGestalt Therapy Institute, HCC—Human Communication Center, 97100 Ragusa, ItalySiPGI–Postgraduate School of Integrated Gestalt Psychotherapy, 80058 Torre Annunziata, ItalyClinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their feedback was evaluated by Gestalt psychotherapy trainees using a Likert scale rating of satisfaction. Statistical analysis, using the statistical package SPSS version 25 and applying principal component analysis (PCA) and one-way analysis of variance (ANOVA), demonstrated significant differences in favor of pre-trained AI feedback. PCA highlighted four components of the questionnaire: relational and emotional (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction obtained from the three kinds of supervisory feedback were compared using ANOVA. The feedback generated by the pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3)) in C4; in C1, the superiority of f2 over f1 but not over f3 appears significant. These results suggest that AI, when appropriately calibrated, may be an appreciable tool for complementing the effectiveness of clinical supervision, offering an innovative blended supervision methodology, in particular in the area of career guidance.https://www.mdpi.com/2227-9709/12/1/29clinical supervisionAI technologiesgestalt psychotherapyprincipal component analysis (PCA)career guidance |
| spellingShingle | Valeria Cioffi Ottavio Ragozzino Lucia Luciana Mosca Enrico Moretto Enrica Tortora Annamaria Acocella Claudia Montanari Antonio Ferrara Stefano Crispino Elena Gigante Alexander Lommatzsch Mariano Pizzimenti Efisio Temporin Valentina Barlacchi Claudio Billi Giovanni Salonia Raffaele Sperandeo Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT Informatics clinical supervision AI technologies gestalt psychotherapy principal component analysis (PCA) career guidance |
| title | Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT |
| title_full | Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT |
| title_fullStr | Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT |
| title_full_unstemmed | Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT |
| title_short | Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT |
| title_sort | can ai technologies support clinical supervision assessing the potential of chatgpt |
| topic | clinical supervision AI technologies gestalt psychotherapy principal component analysis (PCA) career guidance |
| url | https://www.mdpi.com/2227-9709/12/1/29 |
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