Nov 04, 2025
Mapping Factors Influencing the Psychological Well-Being of Medical Students Interacting with Generative Artificial Intelligence: A Scoping Review
- Golchehreh Ahmadi1,
- Noushin Kohan2,
- Rita Mojtahedzadeh3,
- Ken Masters4,
- Hanieh Zehtab-Hashemi5,
- Aeen Mohammadi6
- 1Department of e-Learning in Medical Education, Smart University of Medical Sciences, Tehran, Iran;
- 2Department of Medical Education, Department of E-learning in Medical Education, Smart University of Medical Sciences, Tehran, Iran;
- 3Department of E-learning in Medical Education, Centre of Excellence for E-learning in Medical Education, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran;
- 4Department of Medical Education and Informatics, Sultan Qaboos University, Muscat, Oman;
- 5Department of Health Informatics, Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran;
- 6Department of e-Learning in Medical Education, Center of Excellence for e-Learning in Medical Education, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Protocol Citation: Golchehreh Ahmadi, Noushin Kohan, Rita Mojtahedzadeh, Ken Masters, Hanieh Zehtab-Hashemi, Aeen Mohammadi 2025. Mapping Factors Influencing the Psychological Well-Being of Medical Students Interacting with Generative Artificial Intelligence: A Scoping Review . protocols.io https://dx.doi.org/10.17504/protocols.io.n2bvje1wngk5/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: In development
We are still developing and optimizing this protocol
Created: November 04, 2025
Last Modified: November 04, 2025
Protocol Integer ID: 231439
Keywords: medical students, generative AI, ChatGPT, LLMs, psychological well-being, mental health, medical education, generative artificial intelligence, being of medical student, addressing medical student, psychological well, medical student, experiences with genai, medical education, clinical reasoning, rapid integration of generative artificial intelligence, interactions with genai, adapted conceptual model, genai, eligible study, anxiety about reliability, generative artificial intelligence, educational context, thematic analysis, including anxiety, conceptual model, academic engagement
Abstract
The rapid integration of generative artificial intelligence (GenAI) tools such as ChatGPT and large language models (LLMs) into medical education has created new opportunities for learning, simulation, and clinical reasoning. However, these technologies also introduce potential challenges affecting students’ psychological well-being, including anxiety about reliability, ethical concerns, and changes in academic engagement.
This protocol describes a scoping review designed to map the factors influencing the psychological well-being of medical students in their interactions with GenAI. The review will follow the methodological framework proposed by Arksey and O’Malley and guided by the PRISMA-ScR checklist. Searches will be conducted across international and Persian databases from 2018 onward, using the PCC framework (Population–Concept–Context) to ensure comprehensive coverage.
Eligible studies will include empirical and theoretical works addressing medical students’ experiences with GenAI in educational contexts. Data will be extracted using a structured form and synthesized through descriptive mapping and thematic analysis.
The expected outcome is a conceptual framework summarizing factors that positively or negatively influence medical students’ psychological well-being when engaging with GenAI. This review will identify knowledge gaps and provide evidence to inform educational policy, research, and the development of a culturally adapted conceptual model based on the METUX framework.
Troubleshooting
Before start
This scoping review is conducted as part of the first sub-study of a doctoral dissertation in E-Learning in Medical Sciences, approved and currently underway at Smart University of Medical Sciences (SUMS), Iran.
The overarching PhD project is entitled:
“Design of A Conceptual Model for the Psychological Well-Being of Medical Students Interacting with Generative Artificial Intelligence, Based on the METUX Model.”
The present protocol represents the first phase of the research, aiming to systematically map existing evidence and conceptual components related to the psychological well-being of medical students interacting with generative AI. The findings of this review will form the theoretical foundation for subsequent qualitative and model development phases of the doctoral project.
Methodology
A scoping review will be conducted following the methodological framework proposed by Arksey and O'Malley (2005), refined by Levac et al. (2010), and guided by the PRISMA-ScR checklist (Tricco et al., 2018).
The review will follow five compulsory stages:
Identifying the research question
Identifying relevant studies
Study selection
Charting the data
Collating, summarizing, and reporting results
Search Strategy
The search strategy will be developed using the PCC framework (Population, Concept, Context) recommended by JBI.
Population: Medical students
Concept: Psychological well-being in interaction with generative AI
Context: Medical education and learning environments
Databases: PubMed, Scopus, Web of Science, ERIC, IEEE Xplore, Google Scholar, Litmaps, and Persian databases.
Keywords: medical students, generative AI, ChatGPT, LLMs, psychological well-being, mental health, medical education.
Inclusion criteria: studies involving medical students; focus on interactions with GenAI; outcomes related to psychological or mental well-being; English or Persian publications from 2018 onward.
Exclusion criteria: non-medical student populations; studies unrelated to GenAI; lack of full-text availability.
Study Selection and Data Charting
After removing duplicates, studies will be screened by two independent reviewers using inclusion and exclusion criteria. Disagreements will be resolved through discussion or third-party consultation.
A data extraction form (Excel-based) will collect key information: author, year, country, AI model, population details, positive/negative factors, outcomes, and instruments used.
Critical Appraisal and Data Synthesis
Although optional in scoping reviews, methodological quality will be assessed using the Mixed Methods Appraisal Tool (MMAT).
Data will be synthesized through both numerical mapping (frequency and distribution) and thematic analysis, based on Braun and Clarke's (2006) six-phase framework.
Reporting
The results will be reported according to PRISMA-ScR items (Tricco et al., 2018). The findings will include identified themes, conceptual relationships, and gaps to inform the next qualitative phase of the doctoral project.
Expected Outcomes
A conceptual thematic framework describing factors affecting the psychological well-being of medical students in interaction with GenAI.
Identification of research and educational gaps in GenAI-assisted medical learning.
Foundational input for developing a culturally adapted conceptual model based on METUX.
Protocol references
Alkhaaldi SMI, Kassab CH, Dimassi Z, Oyoun Alsoud L, Al Fahim M, Al Hageh C, et al. Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study. JMIR Med Educ. 2023 Dec 22;9:e51302.
Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005 Feb;8(1):19–32.
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006
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Foster ED, Deardorff A. Open Science Framework (OSF). J Med Libr Assoc [Internet]. 2017 Apr 4 [cited 2025 Aug 27];105(2). Available from: http://jmla.pitt.edu/ojs/jmla/article/view/88
Hale J, Alexander S, Wright ST, Gilliland K. Generative AI in Undergraduate Medical Education: A Rapid Review. J Med Educ Curric Dev. 2024 Jan;11:23821205241266697.
Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology.
Implement Sci. 2010 Dec;5(1):69.
Mak S, Thomas A. Steps for Conducting a Scoping Review. J Grad Med Educ. 2022 Oct 1;14(5):565–7. 7.
Peters MDJ, Godfrey C, McInerney P, Khalil H, Larsen P, Marnie C, et al. Best practice
guidance and reporting items for the development of scoping review protocols.
JBI Evid Synth. 2022 Apr;20(4):953–68.
Pollock D, Peters MDJ, Khalil H, McInerney P, Alexander L, Tricco AC, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth. 2023 Mar;21(3):520–32.
Sallam M, Al-Mahzoum K, Almutairi YM, Alaqeel O, Abu Salami A, Almutairi ZE, et al. Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study. Int Med Educ. 2024 Oct 9;3(4):406–25.
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 2;169(7):467–73.
Acknowledgements
The authors gratefully acknowledge the valuable contributions of Dr. Ziba Farajzadegan, who assisted in developing the search strategy, and Dr. Shadi Asadzandi, who supported the execution and management of the database search process. Their expertise and collaboration were instrumental in strengthening the methodological rigour of this scoping review protocol.