Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67873, first published .
Technology Acceptance Model in Medical Education: Systematic Review

Technology Acceptance Model in Medical Education: Systematic Review

Technology Acceptance Model in Medical Education: Systematic Review

1Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore, Singapore

2Faculty of Arts and Social Science, National University of Singapore, Singapore, Singapore

3Imperial College, London, United Kingdom

Corresponding Author:

Jason Wen Yau Lee, PhD, MSc, BIT


Background: With the growing use of technology in medical education, a framework is needed to evaluate learners’ and educators’ acceptance of these technologies. In this context, the Technology Acceptance Model (TAM) offers a valuable theoretical framework, providing insights into the determinants influencing users’ acceptance and adoption of technology.

Objective: This review aims to systematically synthesize the body of research in medical education that uses the TAM.

Methods: An electronic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach in February 2024 on the Embase, MEDLINE, PsycINFO, PubMed, and Web of Science databases, yielding 680 articles. Upon elimination of duplicates and applying the exclusion criteria, a total of 39 articles were retained. To evaluate the quality of the study, the Medical Education Research Study Quality Instrument score was calculated for each analysis with a qualitative component.

Results: Studies using TAM in medical education began in 2010, with the model’s application relatively rare up to 2016. Most of the studies were quantitative, operationalizing the TAM as a survey instrument, but it was also used as a research framework in qualitative data analysis. Structural equation modeling, descriptive analysis, and correlation analysis were the most common data analysis approaches in the studies. E-learning and mobile learning were the predominant learning interventions explored, but there were indications that novel learning technologies such as augmented reality, virtual reality, and 3D printing were being investigated.

Conclusions: The study’s findings reveal an expanding scholarly engagement with using TAM in medical education. Although the TAM has been mostly used as a survey instrument, it can also be adapted as a qualitative research framework to analyze data. This systematic review provides a foundation for future research to understand the factors influencing users’ acceptance of technology, especially in medical education.

JMIR Med Educ 2025;11:e67873

doi:10.2196/67873

Keywords



Technology has changed how we learn and access knowledge, particularly with the introduction of digital devices and the internet. No longer are we constrained by time or space, and information is available anytime and anywhere. Today, learning can happen through massive open online courses such as Khan Academy [Khan Academy. URL: https://www.khanacademy.org/ [Accessed 2024-04-18] 1], edX [edX. URL: https://www.edx.org/ [Accessed 2024-04-18] 2], and Coursera [Coursera. URL: https://www.coursera.org/ [Accessed 2024-04-18] 3], or simply by viewing one of countless video tutorials online. Books can be supplemented or even replaced with multimedia resources that can provide learners with a richer learning experience. The way that knowledge is accessed has changed dramatically over the past 2 decades with the development of new technologies.

Medical education has traditionally relied on time-honored teaching methodologies. Cadaveric dissection has always been considered the gold standard for anatomy instruction [Johnson EO, Charchanti AV, Troupis TG. Modernization of an anatomy class: from conceptualization to implementation. A case for integrated multimodal-multidisciplinary teaching. Anat Sci Educ. 2012;5(6):354-366. [CrossRef] [Medline]4], providing students with hands-on experience with human tissues and structures. However, as educational resources face constraints and medical knowledge expands, these traditional approaches have begun to be transformed through the use of technology. Teaching modalities such as virtual [Birbara NS, Sammut C, Pather N. Virtual reality in anatomy: a pilot study evaluating different delivery modalities. Anat Sci Educ. Jul 2020;13(4):445-457. [CrossRef] [Medline]5] and augmented reality [Duncan-Vaidya EA, Stevenson EL. The effectiveness of an augmented reality head-mounted display in learning skull anatomy at a community college. Anat Sci Educ. Mar 2021;14(2):221-231. [CrossRef] [Medline]6] provide students with an immersive 3D learning experience. Three-dimensional printing technology [Adams JW, Paxton L, Dawes K, Burlak K, Quayle M, McMenamin PG. 3D printed reproductions of orbital dissections: a novel mode of visualising anatomy for trainees in ophthalmology or optometry. Br J Ophthalmol. Sep 2015;99(9):1162-1167. [CrossRef] [Medline]7] has enabled the creation of anatomical models on demand that can be customized for specific learning outcomes [Lee JWY, Ong DW, Soh RCC, Rao JP, Bello F. Exploring student acceptance of learning technologies in anatomy education: a mixed-method approach. Clin Anat. Apr 2025;38(3):334-346. [CrossRef] [Medline]8], and e-learning resources [Baptiste YM. Digital feast and physical famine: the altered ecosystem of anatomy education due to the Covid-19 pandemic. Anat Sci Educ. Jul 2021;14(4):399-407. [CrossRef] [Medline]9] have democratized access to high-quality learning materials. In a study on rural posting clerkships, iPads equipped with mobile health information resources have positively influenced medical students’ information-seeking behavior [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10]. With the increasing use of technology in medical education, it is essential to understand how it is accepted for use in learning.

The Technology Acceptance Model (TAM) provides a framework to understand the factors influencing the decision to use new technologies in medical education [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10-Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12]. The perceived ease of use is the extent to which a person believes the system will be free of effort. In contrast, perceived usefulness is the extent to which a person believes using the system would improve their productivity or job performance. However, one shortcoming of the TAM when applied to complex medical teaching environments is that it does not consider broader contextual factors, such as organizational culture, social influence, and other affective factors like attitudes and beliefs that may significantly impact the acceptance of educational technology.

To address this issue, the Technology Acceptance Model 2 (TAM2) was proposed by Venkatesh and Davis [Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Manage Sci. Feb 2000;46(2):186-204. [CrossRef]13] as an improvement to the original model to include social influence and cognitive processes that may influence an individual’s acceptance of technology. The purpose of developing the TAM2 was to include additional crucial factors influencing perceived usefulness and usage intention constructs to explain user behavior and acceptance. These factors include subjective norms, output quality, result demonstrability, and social factors, among others, that explain user behavior and acceptance. By integrating these factors, the TAM2 offers a more comprehensive framework for analyzing how individual and social variables influence beliefs, attitudes, and intentions to use the technology in medical education.

Despite the growing use of technology in medical education, understanding the factors that influence its adoption remains challenging for educators and institutions. Previous research has identified barriers to technology implementation such as technical difficulties [Papapanou M, Routsi E, Tsamakis K, et al. Medical education challenges and innovations during COVID-19 pandemic. Postgrad Med J. May 2022;98(1159):321-327. [CrossRef] [Medline]14], resistance to change, and varying acceptance by faculty and students [Gagnon MP, Ngangue P, Payne-Gagnon J, Desmartis M. m-Health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. Jan 2016;23(1):212-220. [CrossRef] [Medline]15]. The TAM has emerged as a valuable theoretical framework for examining these adoption challenges [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10,Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A systematic review of the Technology Acceptance Model in health informatics. Appl Clin Inform. Jul 2018;9(3):604-634. [CrossRef] [Medline]16,Barteit S, Neuhann F, Bärnighausen T, et al. Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study. J Med Internet Res. Oct 9, 2019;21(10):e14748. [CrossRef] [Medline]17], yet its application within medical education contexts remains fragmented and inconsistently synthesized [Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A systematic review of the Technology Acceptance Model in health informatics. Appl Clin Inform. Jul 2018;9(3):604-634. [CrossRef] [Medline]16,AlQudah AA, Al-Emran M, Shaalan K. Technology acceptance in healthcare: a systematic review. Appl Sci (Basel). Jan 2021;11(22):10537. [CrossRef]18]. This knowledge gap may hinder evidence-based decision-making on the use of education technology that could enhance teaching and learning outcomes in medical education.

To address this limitation, this systematic review aims to synthesize the current research on the application of the TAM in medical education to provide insights into the factors influencing technology acceptance among medical professionals and students. The guiding questions that we aim to answer with this systematic review are as follows:

  1. What is the state of TAM in medical education?
  2. How has TAM been operationalized?
  3. What education interventions are used in such studies?

This review was designed and is reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. Jul 21, 2009;339(jul21 1):b2535. [CrossRef] [Medline]19].

Search Strategy

A systematic search was conducted in February 2024 to identify original published articles on TAM and medical education from January 2003 to December 2023. We set the search criteria to focus on the last 20 years to capture only the most recent advancements in the field. An author (JYT) then systematically searched 5 databases accessible through the university library. A search of “all fields” with the keywords “TAM” or “Technology Acceptance Model” and “Medical Education” was used for the Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases.

Inclusion and Exclusion Criteria

Peer-reviewed articles were included if they used the TAM as a survey instrument in the study methodology or as a theoretical framework in medical education. This includes using the original TAM model proposed by Davis [Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Sep 1989;13(3):319. [CrossRef]20] or the TAM2 model proposed by Venkatesh and Davis [Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Manage Sci. Feb 2000;46(2):186-204. [CrossRef]13]. We define medical education–related studies as training medical professionals, residents, and students pursuing their undergraduate, clerkship, postgraduate, or continuing medical education. If the study comprised a mix of medical students and students from other health care science professions (eg, nursing, pharmacy, emergency response), they were also included as part of the review.

Studies were excluded from our research if they were not related to medical education, such as research focused solely on nursing and allied health professions like pharmacy and physiotherapy, articles not written in English, articles published before 2003, and non–peer-reviewed documents, including theses or conference abstracts lacking comprehensive methodological details. When the cohort under study comprised a mixture of health professionals, including those who met the inclusion criteria, the entire cohort was included in the research analysis.

Final Study Selection

After retrieving the search results from the identified database, JYT removed the duplicates and uploaded the articles into a shared Microsoft Teams [Microsoft Teams. Microsoft. 2023. URL: https://www.microsoft.com/en-us/microsoft-teams/group-chat-software [Accessed 2025-06-19] 21] folder. The shortlisted articles were entered into an Excel spreadsheet for screening by the authors. The final screening process involved JYT noting articles for inclusion or exclusion based on the title or abstract, which was verified independently by the primary author (JWYL). JYT also assigned a reason for exclusion for each excluded article. In cases of uncertainty, the articles in question were retained and screened together by both authors (JWYL and JYT). JYT extracted the full text of the retained articles and this was verified by JWYL for consistency.

Data Extraction and Analysis

After the shortlisted studies were identified, details of the studies were entered into the spreadsheet, including (1) general study information (eg, authors, title, and publication year), (2) participant-related information, (3) sample size, (4) application of the TAM framework, (5) study design, (6) statistical analysis used, (7) education intervention investigated, and (8) study quality (Medical Education Research Study Quality Instrument [MERSQI] score). Please see Table 1 for information gathered from the shortlisted studies.

Table 1. Articles included in the study.
AuthorsPaper titlePublication yearCountryStudy participantsSample sizeTAMa applicationStudy designStatistical analysisEducation interventionMERSQIb score
Wong G et al (2010) [Wong G, Greenhalgh T, Pawson R. Internet-based medical education: a realist review of what works, for whom and in what circumstances. BMC Med Educ. Feb 2, 2010;10(1):12. [CrossRef] [Medline]22]Internet-based medical education: a realist review of what works, for whom and in what circumstances2010United KingdomN/Ac (systematic review)N/AResearch frameworkQualitativeN/AE-learningN/A
McGowan BS et al (2012) [McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res. Sep 24, 2012;14(5):e117. [CrossRef] [Medline]23]Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information2012United StatesHealth care professionals485Survey instrumentQuantitativeCorrelationE-learning9.5
Knight JF (2013) [Knight JF. Acceptability of video games technology for medical emergency training. Int J Gaming Comput Mediat Simul. Oct 2013;5(4):86-99. [CrossRef]24]Acceptability of video games technology for medical emergency training2013DenmarkHealth care professionals37Survey instrumentQuantitativeMultiple regressionSerious game12
Fang TY et al (2014) [Fang TY, Wang PC, Liu CH, Su MC, Yeh SC. Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training. Comput Methods Programs Biomed. Feb 2014;113(2):674-681. [CrossRef] [Medline]25]Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training2014TaiwanMedical undergraduates and health care professionals14Survey instrumentQuantitativet testHaptic device9
Briz-Ponce L and Garcia-Penalvo F (2015) [Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12]An empirical assessment of a Technology Acceptance Model for apps in medical education2015SpainMedical undergraduates and health care professionals124Survey instrumentQuantitativeSEM (CB)dMobile learning10
Ryan JR et al (2015) [Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26]Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting2015United StatesMedical undergraduates10Survey instrumentQuantitativeDescriptive3D printing7
Huang HM et al (2016) [Huang HM, Liaw SS, Lai CM. Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems. Interactive Learning Environments. Jan 2, 2016;24(1):3-19. [CrossRef]27]Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems2016TaiwanMedical undergraduates230Survey instrumentQuantitativeCorrelationVirtual reality10.5
Briz-Ponce L et al (2017) [Briz-Ponce L, Pereira A, Carvalho L, Juanes-Méndez JA, García-Peñalvo FJ. Learning with mobile technologies – students’ behavior. Comput Human Behav. Jul 2017;72:612-620. [CrossRef]28]Learning with mobile technologies — students’ behavior2017SpainMedical undergraduates124Survey instrumentQuantitativeSEM (PLS)eE-learning9
Tahamtan I et al (2017) [Tahamtan I, Pajouhanfar S, Sedghi S, Azad M, Roudbari M. Factors affecting smartphone adoption for accessing information in medical settings. Health Info Libraries J. Jun 2017;34(2):134-145. [CrossRef]29]Factors affecting smartphone adoption for accessing information in medical settings2017IranMedical undergraduates112Survey instrumentMixedSEM (CB)Mobile learning10
Yeom S et al (2017) [Yeom S, Choi-Lundberg DL, Fluck AE, Sale A. Factors influencing undergraduate students’ acceptance of a haptic interface for learning gross anatomy. ITSE. Apr 18, 2017;14(1):50-66. [CrossRef]30]Factors influencing undergraduate students’ acceptance of a haptic interface for learning gross anatomy2017AustraliaGeneral undergraduates89Research frameworkQuantitativeDescriptiveHaptic device10
Basoglu N et al (2018) [Basoglu N, Goken M, Dabic M, Ozdemir Gungor D, Daim TU. Exploring adoption of augmented reality smart glasses: applications in the medical industry. Front Eng. 2018;0:0. [CrossRef]31]Exploring adoption of augmented reality smart glasses: applications in the medical industry2018TurkeyMedical undergraduates and health care professionals71Survey instrumentQuantitativeSEM (PLS)Augmented reality9
Duch Christensen M et al (2018) [Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32]Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training2018DenmarkHealth care professionals21Research frameworkQualitativeN/ASimulation-based trainingN/A
Barteit S et al (2019) [Barteit S, Neuhann F, Bärnighausen T, et al. Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study. J Med Internet Res. Oct 9, 2019;21(10):e14748. [CrossRef] [Medline]17]Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study2019ZambiaMedical undergraduates and health care professionals109Survey instrumentQuantitativeCorrelationE-learning9
Chan KS and Zary N (2019)[Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. Jun 15, 2019;5(1):e13930. [CrossRef] [Medline]33]Applications and challenges of implementing artificial intelligence in medical education: integrative review2019United Arab EmiratesN/A (systematic review)N/AResearch frameworkQualitativeN/AArtificial intelligence in medical educationN/A
Johnson EM and Howard C (2019) [Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34]A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship2019United StatesMedical undergraduates9Survey instrumentMixedDescriptiveMobile learning9
Abdekhoda M et al (2020) [Abdekhoda M, Maserat E, Ranjbaran F. A conceptual model of flipped classroom adoption in medical higher education. ITSE. Mar 14, 2020;17(4):393-401. [CrossRef]35]A conceptual model of flipped classroom adoption in medical higher education2020IranMedical undergraduates110Survey instrumentQuantitativeCorrelationTeaching approach11
Kucuk S et al (2020) [Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36]A model for medical students’ behavioral intention to use mobile learning2020TurkeyMedical undergraduates376Survey instrumentQuantitativeSEM (CB)Mobile learning10
Lee CW et al (2020) [Lee CW, Chen GL, Lee YK. User experience evaluation of the EPAs-based e-portfolio system and an analysis of its impact. J Acute Med. Sep 1, 2020;10(3):115-125. [CrossRef] [Medline]37]User experience evaluation of the EPAs-based e-portfolio system and an analysis of its impact2020TaiwanHealth care professionals20Research frameworkQualitativeN/AE-learningN/A
Jeyakumar T et al (2021) [Jeyakumar T, Ambata-Villanueva S, McClure S, Henderson C, Wiljer D. Best practices for the implementation and sustainment of virtual health information system training: qualitative study. JMIR Med Educ. Oct 22, 2021;7(4):e30613. [CrossRef] [Medline]38]Best practices for the implementation and sustainment of virtual health information system training: qualitative study2021CanadaHealth care educators18Research frameworkQualitativeN/AE-learningN/A
Lee SS et al (2021) [Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. Dec 2021;33(4):349-367. [CrossRef] [Medline]39]Mobile learning in clinical settings: unveiling the paradox2021SingaporeHealth care professionals171Research frameworkMixedDescriptiveMobile learning8.5
Zalat MM et al (2021) [Zalat MM, Hamed MS, Bolbol SA. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. In: Hwang GJ, editor. PLoS One. 2021;16(3):e0248758. [CrossRef] [Medline]40]The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff2021EgyptHealth care professionals346Survey instrumentQuantitativeDescriptiveE-learning8.5
Almarzouqi A et al (2022) [Almarzouqi A, Aburayya A, Salloum SA. Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach. IEEE Access. 2022;10:43421-43434. [CrossRef]41]Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach2022United Arab EmiratesGeneral undergraduate and postgraduate1858Survey instrumentQuantitativeSEM (PLS)E-learning12
Bhardwaj M et al (2022) [Bhardwaj M, Kashyap S, Aggarwal D, Bhawani R. Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study. JCDR. 2022. [CrossRef]42]Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study2022IndiaMedical undergraduates340Research frameworkQuantitativeDescriptiveE-learning9
Bianchi I et al (2022)
[Bianchi I, Stefani CJM, Santiago P, Zanatta AL, Rieder R. AnemiaAR: a serious game to support teaching of haematology. J Vis Commun Med. Jul 2022;45(3):134-153. [CrossRef] [Medline]43]
AnemiaAR: a serious game to support teaching of haematology2022BrazilMedical undergraduates14Survey instrumentQuantitativeU testSerious game8
Chan E et al (2022) [Chan E, Khong ML, Torda A, Tanner JA, Velan GM, Wong GTC. Medical teachers’ experience of emergency remote teaching during the COVID-19 pandemic: a cross-institutional study. BMC Med Educ. Apr 21, 2022;22(1):303. [CrossRef] [Medline]44]Medical teachers’ experience of emergency remote teaching during the COVID-19 pandemic: a cross-institutional study.2022Hong KongHealth care educators139Research frameworkQuantitativeCorrelationE-learning9.75
Do DH et al (2022) [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10]Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective2022CanadaMedical undergraduates834Survey instrumentQuantitativeSEM (PLS)Mobile learning10.5
Harmon DJ et al (2022) [Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45]Development and assessment of an integrated anatomy mobile app2022United StatesMedical undergraduates195Survey instrumentQuantitativeSEM (CB)Mobile learning10
Komuhangi A et al (2022) [Komuhangi A, Mpirirwe H, Robert L, Githinji FW, Nanyonga RC. Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda. BMC Med Educ. Sep 10, 2022;22(1):671. [CrossRef] [Medline]46]Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda2022UgandaHealth science undergraduates109Survey instrumentQuantitativeRegressionE-learning10
Lau V and Greer M (2022) [Lau KHV, Greer DM. Using technology adoption theories to maximize the uptake of e-learning in medical education. Med Sci Educ. Apr 2022;32(2):545-552. [CrossRef] [Medline]47]Using technology adoption theories to maximize the uptake of e-learning in medical education2022United StatesN/A (systematic review)N/AResearch frameworkQualitativeN/AE-learningN/A
Bugli D et al (2023) [Bugli D, Dick L, Wingate KC, et al. Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality. BMJ Open. May 9, 2023;13(5):e063527. [CrossRef] [Medline]48]Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality2023United StatesHealth care professionals100Survey instrumentQuantitativeCorrelationVirtual reality9
Young Y et al (2023) [Young Y, Leedham-Green K, Jensen-Martin J. Improving transitions between clinical placements. Clin Teach. Aug 2023;20(4):e13580. [CrossRef] [Medline]49]Improving transitions between clinical placements2023United KingdomMedical undergraduates19Research frameworkQualitativeN/AWebsiteN/A
Sallam M et al (2023) [Sallam M, Salim NA, Barakat M, et al. Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study. JMIR Med Educ. Sep 5, 2023;9(1):e48254. [CrossRef] [Medline]50]Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study2023JordanGeneral undergraduates458Survey instrumentQuantitativeCorrelationArtificial intelligence in medical education11
Cabero-Almenara J et al (2023) [Cabero-Almenara J, Llorente-Cejudo C, Palacios-Rodríguez A, Gallego-Pérez Ó. Degree of acceptance of virtual reality by health sciences students. Int J Environ Res Public Health. Apr 18, 2023;20(8):5571. [CrossRef] [Medline]51]Degree of acceptance of virtual reality by health sciences students2023SpainHealth science undergraduates136Survey instrumentQuantitativeRegressionVirtual reality10
Ndlovu K et al (2023) [Ndlovu K, Stein N, Gaopelo R, et al. Evaluating the feasibility and acceptance of a mobile clinical decision support system in a resource-limited country: exploratory study. JMIR Form Res. Oct 10, 2023;7:e48946. [CrossRef] [Medline]52]Evaluating the feasibility and acceptance of a mobile clinical decision support system in a resource-limited country: exploratory study2023BotswanaHealth care professionals28Survey instrumentMixedDescriptiveMobile learning7
Lin CW et al (2023) [Lin CW, Clinciu DL, Salcedo D, Huang CW, Kang EYN, Li YCJ. Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions. PLoS One. 2023;18(11):e0278571. [CrossRef] [Medline]53]Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions2023TaiwanHealth care educators50Survey instrumentQuantitativeCorrelationE-learning11
Rahadiani P et al (2023) [Rahadiani P, Kekalih A, Krisnamurti DGB. Use of H5P interactive learning content in a self-paced MOOC for learning activity preferences and acceptance in an Indonesian medical elective module. African Journal of Science, Technology, Innovation and Development. Nov 10, 2023;15(7):844-851. [CrossRef]54]Use of H5P interactive learning content in a self-paced MOOC [massive open online course] for learning activity preferences and acceptance in an Indonesian medical elective module2023IndonesiaHealth science undergraduates126Survey InstrumentQuantitativeCorrelationE-learning11
De Ruyck O et al (2024) [De Ruyck O, Embo M, Morton J, et al. A comparison of three feedback formats in an ePortfolio to support workplace learning in healthcare education: a mixed method study. Educ Inf Technol. Jun 2024;29(8):9667-9688. [CrossRef]55]A comparison of three feedback formats in an ePortfolio to support workplace learning in healthcare education: a mixed method study2023BelgiumHealth care professionals85Survey instrumentMixedCorrelationE-learning7

aTAM: Technology Acceptance Model.

bMERSQI: Medical Education Research Study Quality Instrument.

cN/A: not applicable.

dSEM (CB): covariance-based structural equation modeling.

eSEM (PLS): partial least squares structural equation modeling.

To assess the study quality of quantitative studies, the MERSQI was used to measure the methodological quality of the selected studies [Reed DA, Cook DA, Beckman TJ, Levine RB, Kern DE, Wright SM. Association between funding and quality of published medical education research. JAMA. Sep 5, 2007;298(9):1002-1009. [CrossRef] [Medline]56]. The MERSQI is an instrument that measures the quality of experimental, quasi-experimental, and observational studies. The MERSQI contains 6 domains (study design, sampling, type of data, validity evidence for the evaluation instrument, data analysis, and outcomes), with a study scoring a possible total of 18. The MERSQI was not designed for use in qualitative studies. Therefore, these studies will not be assessed using the MERSQI.


Overview

A systematic literature retrieval and analysis was methodically executed across 5 authoritative databases (Embase, MEDLINE, PsycINFO, PubMed, and Web of Science), yielding 580 records. The PRISMA checklist informed the review protocol and is depicted in the flow diagram in Figure 1. Using automation tools to narrow the search criteria, 30 studies were removed, and 266 duplicate records were excluded. This resulted in 384 studies that were eligible for screening. Based on the abstract or title, 329 studies that did not meet the study inclusion criteria were eliminated. This left 55 studies for full-text retrieval. Both authors read through all shortlisted papers and excluded a further 18 papers, where 1 was a duplicate, 8 did not use the TAM in the study, and 9 were unrelated to medical education. Therefore, the total number of studies included was 37.

Table 2 presents the source database, publisher, and number of studies found in the search results.

Figure 1. PRISMA flowchart. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Table 2. Databases and search results (N=680).
DatabaseVendor/publisherSearch results, n (%)
EmbaseElsevier318 (46.3)
MEDLINEOvidSP31 (4.6)
PsycINFOAPA24 (3.5)
PubMedPubMed155 (22.8)
Web of ScienceClarivate152 (22.4)

Year of Publication

Despite the TAM being developed in the 1990s and our search spanning from 2003 to 2023, we only found a single study from 2010, which marked the earliest TAM usage in this review. The adoption of TAM in medical education remained relatively rare up to 2016. It was not until 2017 that a consistent uptick in the number of peer-reviewed publications using this model could be observed, with an average of 3 studies each year until 2022, when there was an almost 3-fold increase to 8 studies, which remained constant in 2023 (Figure 2). This surge in numbers was likely driven by the rapid integration of technologies from 2021 onward, which will be covered in the Discussion section.

Figure 2. Number of publications by year from 2010 to 2023.

Country of Study

The included studies were conducted in 21 countries, with no single region dominating the publications. Six of the studies were conducted in the United States [McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res. Sep 24, 2012;14(5):e117. [CrossRef] [Medline]23,Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26,Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34,Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45,Lau KHV, Greer DM. Using technology adoption theories to maximize the uptake of e-learning in medical education. Med Sci Educ. Apr 2022;32(2):545-552. [CrossRef] [Medline]47,Bugli D, Dick L, Wingate KC, et al. Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality. BMJ Open. May 9, 2023;13(5):e063527. [CrossRef] [Medline]48], 4 in Taiwan [Fang TY, Wang PC, Liu CH, Su MC, Yeh SC. Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training. Comput Methods Programs Biomed. Feb 2014;113(2):674-681. [CrossRef] [Medline]25,Huang HM, Liaw SS, Lai CM. Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems. Interactive Learning Environments. Jan 2, 2016;24(1):3-19. [CrossRef]27,Lin CW, Clinciu DL, Salcedo D, Huang CW, Kang EYN, Li YCJ. Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions. PLoS One. 2023;18(11):e0278571. [CrossRef] [Medline]53], 3 in Spain [Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12,Briz-Ponce L, Pereira A, Carvalho L, Juanes-Méndez JA, García-Peñalvo FJ. Learning with mobile technologies – students’ behavior. Comput Human Behav. Jul 2017;72:612-620. [CrossRef]28,Cabero-Almenara J, Llorente-Cejudo C, Palacios-Rodríguez A, Gallego-Pérez Ó. Degree of acceptance of virtual reality by health sciences students. Int J Environ Res Public Health. Apr 18, 2023;20(8):5571. [CrossRef] [Medline]51]; 2 each in Canada [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10,Jeyakumar T, Ambata-Villanueva S, McClure S, Henderson C, Wiljer D. Best practices for the implementation and sustainment of virtual health information system training: qualitative study. JMIR Med Educ. Oct 22, 2021;7(4):e30613. [CrossRef] [Medline]38], Denmark [Knight JF. Acceptability of video games technology for medical emergency training. Int J Gaming Comput Mediat Simul. Oct 2013;5(4):86-99. [CrossRef]24,Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32], Iran [Tahamtan I, Pajouhanfar S, Sedghi S, Azad M, Roudbari M. Factors affecting smartphone adoption for accessing information in medical settings. Health Info Libraries J. Jun 2017;34(2):134-145. [CrossRef]29,Abdekhoda M, Maserat E, Ranjbaran F. A conceptual model of flipped classroom adoption in medical higher education. ITSE. Mar 14, 2020;17(4):393-401. [CrossRef]35], Turkey [Basoglu N, Goken M, Dabic M, Ozdemir Gungor D, Daim TU. Exploring adoption of augmented reality smart glasses: applications in the medical industry. Front Eng. 2018;0:0. [CrossRef]31,Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36], United Arab Emirates [Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. Jun 15, 2019;5(1):e13930. [CrossRef] [Medline]33,Almarzouqi A, Aburayya A, Salloum SA. Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach. IEEE Access. 2022;10:43421-43434. [CrossRef]41], and the United Kingdom [Wong G, Greenhalgh T, Pawson R. Internet-based medical education: a realist review of what works, for whom and in what circumstances. BMC Med Educ. Feb 2, 2010;10(1):12. [CrossRef] [Medline]22,Young Y, Leedham-Green K, Jensen-Martin J. Improving transitions between clinical placements. Clin Teach. Aug 2023;20(4):e13580. [CrossRef] [Medline]49]; and 1 each in Australia [Yeom S, Choi-Lundberg DL, Fluck AE, Sale A. Factors influencing undergraduate students’ acceptance of a haptic interface for learning gross anatomy. ITSE. Apr 18, 2017;14(1):50-66. [CrossRef]30], Belgium [De Ruyck O, Embo M, Morton J, et al. A comparison of three feedback formats in an ePortfolio to support workplace learning in healthcare education: a mixed method study. Educ Inf Technol. Jun 2024;29(8):9667-9688. [CrossRef]55], Botswana [Ndlovu K, Stein N, Gaopelo R, et al. Evaluating the feasibility and acceptance of a mobile clinical decision support system in a resource-limited country: exploratory study. JMIR Form Res. Oct 10, 2023;7:e48946. [CrossRef] [Medline]52], Brazil [Bianchi I, Stefani CJM, Santiago P, Zanatta AL, Rieder R. AnemiaAR: a serious game to support teaching of haematology. J Vis Commun Med. Jul 2022;45(3):134-153. [CrossRef] [Medline]43], Egypt [Zalat MM, Hamed MS, Bolbol SA. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. In: Hwang GJ, editor. PLoS One. 2021;16(3):e0248758. [CrossRef] [Medline]40], Hong Kong [Chan E, Khong ML, Torda A, Tanner JA, Velan GM, Wong GTC. Medical teachers’ experience of emergency remote teaching during the COVID-19 pandemic: a cross-institutional study. BMC Med Educ. Apr 21, 2022;22(1):303. [CrossRef] [Medline]44], India [Bhardwaj M, Kashyap S, Aggarwal D, Bhawani R. Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study. JCDR. 2022. [CrossRef]42], Indonesia [Rahadiani P, Kekalih A, Krisnamurti DGB. Use of H5P interactive learning content in a self-paced MOOC for learning activity preferences and acceptance in an Indonesian medical elective module. African Journal of Science, Technology, Innovation and Development. Nov 10, 2023;15(7):844-851. [CrossRef]54], Jordan [Sallam M, Salim NA, Barakat M, et al. Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study. JMIR Med Educ. Sep 5, 2023;9(1):e48254. [CrossRef] [Medline]50], Singapore [Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. Dec 2021;33(4):349-367. [CrossRef] [Medline]39], Uganda [Komuhangi A, Mpirirwe H, Robert L, Githinji FW, Nanyonga RC. Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda. BMC Med Educ. Sep 10, 2022;22(1):671. [CrossRef] [Medline]46], and Zambia [Barteit S, Neuhann F, Bärnighausen T, et al. Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study. J Med Internet Res. Oct 9, 2019;21(10):e14748. [CrossRef] [Medline]17]. This diverse set of countries suggests that TAM has been applied globally across low-income and high-income nations, reflecting its adaptability to various education and technological contexts. Table 3 is a description of the countries by location.

Table 3. Location of study by country.
CountryNumber of studies
Australia1
Belgium1
Botswana1
Brazil1
Canada2
Denmark2
Egypt1
Hong Kong1
India1
Indonesia1
Iran2
Jordan1
Singapore1
Spain3
Taiwan4
Turkey2
Uganda1
United Arab Emirates2
United Kingdom2
United States6
Zambia1

Study Participants

The array of participants in the studies analyzed is quite diverse, reflecting the multifaceted nature of medical education. The review included 2 studies on general undergraduates of various disciplines [Sallam M, Salim NA, Barakat M, et al. Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study. JMIR Med Educ. Sep 5, 2023;9(1):e48254. [CrossRef] [Medline]50], 1 on general undergraduate and postgraduate students of various disciplines [Almarzouqi A, Aburayya A, Salloum SA. Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach. IEEE Access. 2022;10:43421-43434. [CrossRef]41], 3 on health care educators [Jeyakumar T, Ambata-Villanueva S, McClure S, Henderson C, Wiljer D. Best practices for the implementation and sustainment of virtual health information system training: qualitative study. JMIR Med Educ. Oct 22, 2021;7(4):e30613. [CrossRef] [Medline]38,Chan E, Khong ML, Torda A, Tanner JA, Velan GM, Wong GTC. Medical teachers’ experience of emergency remote teaching during the COVID-19 pandemic: a cross-institutional study. BMC Med Educ. Apr 21, 2022;22(1):303. [CrossRef] [Medline]44,Lin CW, Clinciu DL, Salcedo D, Huang CW, Kang EYN, Li YCJ. Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions. PLoS One. 2023;18(11):e0278571. [CrossRef] [Medline]53], and 9 on health care professionals [McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res. Sep 24, 2012;14(5):e117. [CrossRef] [Medline]23,Knight JF. Acceptability of video games technology for medical emergency training. Int J Gaming Comput Mediat Simul. Oct 2013;5(4):86-99. [CrossRef]24,Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32,Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. Dec 2021;33(4):349-367. [CrossRef] [Medline]39,Zalat MM, Hamed MS, Bolbol SA. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. In: Hwang GJ, editor. PLoS One. 2021;16(3):e0248758. [CrossRef] [Medline]40,Bugli D, Dick L, Wingate KC, et al. Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality. BMJ Open. May 9, 2023;13(5):e063527. [CrossRef] [Medline]48,Ndlovu K, Stein N, Gaopelo R, et al. Evaluating the feasibility and acceptance of a mobile clinical decision support system in a resource-limited country: exploratory study. JMIR Form Res. Oct 10, 2023;7:e48946. [CrossRef] [Medline]52,De Ruyck O, Embo M, Morton J, et al. A comparison of three feedback formats in an ePortfolio to support workplace learning in healthcare education: a mixed method study. Educ Inf Technol. Jun 2024;29(8):9667-9688. [CrossRef]55Lee CW, Chen GL, Lee YK. User experience evaluation of the EPAs-based e-portfolio system and an analysis of its impact. J Acute Med. Sep 1, 2020;10(3):115-125. [CrossRef] [Medline]37]. Three studies centered around health science undergraduates [Komuhangi A, Mpirirwe H, Robert L, Githinji FW, Nanyonga RC. Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda. BMC Med Educ. Sep 10, 2022;22(1):671. [CrossRef] [Medline]46,Cabero-Almenara J, Llorente-Cejudo C, Palacios-Rodríguez A, Gallego-Pérez Ó. Degree of acceptance of virtual reality by health sciences students. Int J Environ Res Public Health. Apr 18, 2023;20(8):5571. [CrossRef] [Medline]51,Rahadiani P, Kekalih A, Krisnamurti DGB. Use of H5P interactive learning content in a self-paced MOOC for learning activity preferences and acceptance in an Indonesian medical elective module. African Journal of Science, Technology, Innovation and Development. Nov 10, 2023;15(7):844-851. [CrossRef]54], 12 studies focused on undergraduate medical students [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10,Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26-Tahamtan I, Pajouhanfar S, Sedghi S, Azad M, Roudbari M. Factors affecting smartphone adoption for accessing information in medical settings. Health Info Libraries J. Jun 2017;34(2):134-145. [CrossRef]29,Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34-Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36,Bhardwaj M, Kashyap S, Aggarwal D, Bhawani R. Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study. JCDR. 2022. [CrossRef]42,Bianchi I, Stefani CJM, Santiago P, Zanatta AL, Rieder R. AnemiaAR: a serious game to support teaching of haematology. J Vis Commun Med. Jul 2022;45(3):134-153. [CrossRef] [Medline]43,Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45,Young Y, Leedham-Green K, Jensen-Martin J. Improving transitions between clinical placements. Clin Teach. Aug 2023;20(4):e13580. [CrossRef] [Medline]49], and 4 investigated undergraduate medical students and health care professionals [Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12,Barteit S, Neuhann F, Bärnighausen T, et al. Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study. J Med Internet Res. Oct 9, 2019;21(10):e14748. [CrossRef] [Medline]17,Fang TY, Wang PC, Liu CH, Su MC, Yeh SC. Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training. Comput Methods Programs Biomed. Feb 2014;113(2):674-681. [CrossRef] [Medline]25,Basoglu N, Goken M, Dabic M, Ozdemir Gungor D, Daim TU. Exploring adoption of augmented reality smart glasses: applications in the medical industry. Front Eng. 2018;0:0. [CrossRef]31]. Notably, 3 studies were systematic or scoping reviews [Wong G, Greenhalgh T, Pawson R. Internet-based medical education: a realist review of what works, for whom and in what circumstances. BMC Med Educ. Feb 2, 2010;10(1):12. [CrossRef] [Medline]22,Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. Jun 15, 2019;5(1):e13930. [CrossRef] [Medline]33,Lau KHV, Greer DM. Using technology adoption theories to maximize the uptake of e-learning in medical education. Med Sci Educ. Apr 2022;32(2):545-552. [CrossRef] [Medline]47], which, by their nature, did not involve direct study participants. Table 4 presents a summary of publications by study participants.

Table 4. Summary of publications by study participants.
Study participantsPublication count
General undergraduates2
General undergraduate and postgraduate students1
Health care educators3
Health care professionals (doctors, nurses, pharmacists, residents)9
Health science undergraduate students3
Medical undergraduates12
Medical undergraduates and health care professionals4
Review articles (scoping or systematic review)3

Application of TAM

The TAM served dual purposes in the surveyed studies. In 26 (70%) of the studies, it functioned as a survey instrument, quantitatively measuring the variables influencing user acceptance of and interaction with educational technology. The remaining 11 (30%) studies incorporated TAM as a foundational research framework, which involved thematic analysis of the collected data or shaping the methodology for data collection. This 2-pronged application of the TAM highlights its adaptability and role in the empirical and theoretical examination of technology adoption in medical education. Table 5 is a summary of the application of TAM.

Table 5. Summary of the applications of the Technology Acceptance Model.
ApplicationCount
Research framework11
Survey instrument26

Study Design

The studies reviewed encompass quantitative, qualitative, and mixed methods research methodologies, each engaging the TAM differently. The quantitative studies operationalize the TAM through survey instruments, measuring variables such as perceived ease of use and perceived usefulness to explain the users’ behavioral intentions and actual technology use. In contrast, qualitative studies contextualize the TAM within the broader theoretical landscape, using it to guide the thematic analysis of focus group discourse or to underpin systematic reviews that explore the factors influencing technology adoption. The mixed methods approach combines both, where survey data are analyzed quantitatively while concurrently using qualitative techniques such as semistructured interviews or textual analysis to capture the subtleties of user experience and perception. Most of the included studies (25/37) were quantitative, 7 were qualitative, and 5 adopted a mixed methods approach, as described in Table 6.

Table 6. Summary of study methodology.
MethodologyCount
Quantitative25
Qualitative7
Mixed method5

Statistical Analysis

Correlation analysis was the predominant quantitative technique used in 10 studies to delineate the degree and direction of the linear relationship between the variables of interest. The next most used approach was structural equation modeling (SEM), with an equal number of studies (n=4) that used the covariance-based structural equation model and partial least squares structural equation model. Descriptive analysis was the third most frequently used method, implemented in 7 studies to succinctly summarize and describe the collected survey data. Three studies used regression analysis to predict the effect of the dependent variable based on the independent variable, and 2 other studies leveraged hypothesis testing, specifically the t test and U test, to conduct a comparative analysis of survey outcomes across different intervention groups. Seven studies were qualitative and therefore did not include statistical analysis. Table 7 summarizes the statistical approach taken by the reviewed studies, sorted by the complexity of the analysis.

Table 7. Statistical analysis approach of reviewed studies.
Statistical analysis approachCount
Correlation analysis10
SEM-CB4
SEM-PLS4
Descriptive analysis7
Regression analysis3
Hypothesis testing2
Not applicable7

Types of Education Intervention

The studies reviewed can be classified broadly into 2 categories of education interventions: education technologies and education methodologies. Under education technologies, 1 study examined 3D printing [Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26], 2 studies examined artificial intelligence [Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. Jun 15, 2019;5(1):e13930. [CrossRef] [Medline]33,Sallam M, Salim NA, Barakat M, et al. Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study. JMIR Med Educ. Sep 5, 2023;9(1):e48254. [CrossRef] [Medline]50], 3 studies focused on virtual reality [Huang HM, Liaw SS, Lai CM. Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems. Interactive Learning Environments. Jan 2, 2016;24(1):3-19. [CrossRef]27,Bugli D, Dick L, Wingate KC, et al. Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality. BMJ Open. May 9, 2023;13(5):e063527. [CrossRef] [Medline]48,Cabero-Almenara J, Llorente-Cejudo C, Palacios-Rodríguez A, Gallego-Pérez Ó. Degree of acceptance of virtual reality by health sciences students. Int J Environ Res Public Health. Apr 18, 2023;20(8):5571. [CrossRef] [Medline]51], and 1 on augmented reality smart glasses [Basoglu N, Goken M, Dabic M, Ozdemir Gungor D, Daim TU. Exploring adoption of augmented reality smart glasses: applications in the medical industry. Front Eng. 2018;0:0. [CrossRef]31], indicating an interest in integrating cutting-edge approaches into medical education. E-learning emerged as the most prevalent intervention, with 15 studies emphasizing digital learning [Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12,Barteit S, Neuhann F, Bärnighausen T, et al. Technology acceptance and information system success of a mobile electronic platform for nonphysician clinical students in Zambia: prospective, nonrandomized intervention study. J Med Internet Res. Oct 9, 2019;21(10):e14748. [CrossRef] [Medline]17,Wong G, Greenhalgh T, Pawson R. Internet-based medical education: a realist review of what works, for whom and in what circumstances. BMC Med Educ. Feb 2, 2010;10(1):12. [CrossRef] [Medline]22,McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res. Sep 24, 2012;14(5):e117. [CrossRef] [Medline]23,Lee CW, Chen GL, Lee YK. User experience evaluation of the EPAs-based e-portfolio system and an analysis of its impact. J Acute Med. Sep 1, 2020;10(3):115-125. [CrossRef] [Medline]37,Jeyakumar T, Ambata-Villanueva S, McClure S, Henderson C, Wiljer D. Best practices for the implementation and sustainment of virtual health information system training: qualitative study. JMIR Med Educ. Oct 22, 2021;7(4):e30613. [CrossRef] [Medline]38,Zalat MM, Hamed MS, Bolbol SA. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. In: Hwang GJ, editor. PLoS One. 2021;16(3):e0248758. [CrossRef] [Medline]40-Bhardwaj M, Kashyap S, Aggarwal D, Bhawani R. Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study. JCDR. 2022. [CrossRef]42,Chan E, Khong ML, Torda A, Tanner JA, Velan GM, Wong GTC. Medical teachers’ experience of emergency remote teaching during the COVID-19 pandemic: a cross-institutional study. BMC Med Educ. Apr 21, 2022;22(1):303. [CrossRef] [Medline]44,Komuhangi A, Mpirirwe H, Robert L, Githinji FW, Nanyonga RC. Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda. BMC Med Educ. Sep 10, 2022;22(1):671. [CrossRef] [Medline]46,Lau KHV, Greer DM. Using technology adoption theories to maximize the uptake of e-learning in medical education. Med Sci Educ. Apr 2022;32(2):545-552. [CrossRef] [Medline]47,Lin CW, Clinciu DL, Salcedo D, Huang CW, Kang EYN, Li YCJ. Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions. PLoS One. 2023;18(11):e0278571. [CrossRef] [Medline]53-De Ruyck O, Embo M, Morton J, et al. A comparison of three feedback formats in an ePortfolio to support workplace learning in healthcare education: a mixed method study. Educ Inf Technol. Jun 2024;29(8):9667-9688. [CrossRef]55]. In comparison, using mobile devices for learning was explored in 8 studies [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10,Briz-Ponce L, Pereira A, Carvalho L, Juanes-Méndez JA, García-Peñalvo FJ. Learning with mobile technologies – students’ behavior. Comput Human Behav. Jul 2017;72:612-620. [CrossRef]28,Tahamtan I, Pajouhanfar S, Sedghi S, Azad M, Roudbari M. Factors affecting smartphone adoption for accessing information in medical settings. Health Info Libraries J. Jun 2017;34(2):134-145. [CrossRef]29,Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34,Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36,Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. Dec 2021;33(4):349-367. [CrossRef] [Medline]39,Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45,Ndlovu K, Stein N, Gaopelo R, et al. Evaluating the feasibility and acceptance of a mobile clinical decision support system in a resource-limited country: exploratory study. JMIR Form Res. Oct 10, 2023;7:e48946. [CrossRef] [Medline]52]. Two studies each investigated the use of serious games [Knight JF. Acceptability of video games technology for medical emergency training. Int J Gaming Comput Mediat Simul. Oct 2013;5(4):86-99. [CrossRef]24,Bianchi I, Stefani CJM, Santiago P, Zanatta AL, Rieder R. AnemiaAR: a serious game to support teaching of haematology. J Vis Commun Med. Jul 2022;45(3):134-153. [CrossRef] [Medline]43] and haptic devices [Fang TY, Wang PC, Liu CH, Su MC, Yeh SC. Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training. Comput Methods Programs Biomed. Feb 2014;113(2):674-681. [CrossRef] [Medline]25,Yeom S, Choi-Lundberg DL, Fluck AE, Sale A. Factors influencing undergraduate students’ acceptance of a haptic interface for learning gross anatomy. ITSE. Apr 18, 2017;14(1):50-66. [CrossRef]30]. One study evaluated the use of a website to improve transitions between clinical placements [Young Y, Leedham-Green K, Jensen-Martin J. Improving transitions between clinical placements. Clin Teach. Aug 2023;20(4):e13580. [CrossRef] [Medline]49]. Lastly, under the category of education methodologies, 1 study explored remote simulation training [Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34] and another explored flipped learning [Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45] in medical education. Table 8 summarizes the types of interventions investigated in the reviewed studies.

Table 8. Breakdown of the types of interventions investigated.
InterventionCount
Education technologies
 3D printing1
 Artificial intelligence2
 Augmented/virtual reality4
 E-learning15
 Haptic device2
 Mobile device8
 Serious games2
 Website1
Education methodologies
 Remote simulation training1
 Flipped learning1

Study Quality

The MERSQI can be used to evaluate study quality in medical education research as it provides a validated, comprehensive framework for assessing methodological rigor across multiple dimensions. The MERSQI is a validated tool [Smith RP, Learman LA. A plea for MERSQI: the Medical Education Research Study Quality Instrument. Obstet Gynecol. Oct 2017;130(4):686-690. [CrossRef] [Medline]57] consisting of 10 items across 6 domains: study design, sampling, data type, instrument validity, data analysis, and outcomes. Each domain can be scored up to 3, bringing the maximum score to 18. Thirty studies (25 quantitative and 5 mixed methods studies) were scored by the researchers using the MERSQI. The minimum score for the reviewed papers was 7, while the maximum was 12. The mean score was 9.58 (SD 1.31), with the mixed methods studies scoring generally below the mean score. Figure 3 is a boxplot diagram of the reviewed studies. Qualitative studies were not measured using the MERSQI. Table 1 displays a detailed summary of the MERSQI scores of the studies reviewed.

Figure 3. Boxplot diagram of the MERSQI score of reviewed studies. MERSQI: Medical Education Research Study Quality Instrument.

Opportunities for TAM in Medical Education

Over the past two decades, technological progress has significantly shaped the education landscape. Traditional teaching approaches are enhanced with technology, making learning no longer bound by space or time. Yet, this systematic review found that the number of studies in medical education that use the TAM is notably infrequent when contrasted with other fields such as health informatics (134 studies) [Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A systematic review of the Technology Acceptance Model in health informatics. Appl Clin Inform. Jul 2018;9(3):604-634. [CrossRef] [Medline]16], higher education (104 studies) [Rosli MS, Saleh NS, Md. Ali A, Abu Bakar S, Mohd Tahir L. A systematic review of the Technology Acceptance Model for the sustainability of higher education during the COVID-19 pandemic and identified research gaps. Sustainability. Sep 10, 2022;14(18):11389. [CrossRef]58], mobile learning (87 studies) [Al-Emran M, Mezhuyev V, Kamaludin A. Technology Acceptance Model in M-learning context: a systematic review. Comput Educ. Oct 2018;125:389-412. [CrossRef]59], and health profession education (142 studies) [AlQudah AA, Al-Emran M, Shaalan K. Technology acceptance in healthcare: a systematic review. Appl Sci (Basel). Jan 2021;11(22):10537. [CrossRef]18]. The review found a modest output of 1 study per year from 2010 to 2016. There was an uptick of 3 publications per year through 2021, followed by a large increase to 8 studies in 2022 and 2023, suggesting a growing interest in and recognition of TAM’s relevance in medical education research.

This could be because the health care education field takes a conservative approach when adopting new digital initiatives. The curricula in medical education are highly structured and content-heavy, thus leaving little room for incorporating digital technologies. However, more recently, there have been calls for reforms within the curriculum [Buja LM. Medical education today: all that glitters is not gold. BMC Med Educ. Apr 16, 2019;19(1):110. [CrossRef] [Medline]60], especially to integrate technology to enhance students’ learning experience [Goh PS, Sandars J. A vision of the use of technology in medical education after the COVID-19 pandemic. MedEdPublish (2016). 2020;9:49. [CrossRef] [Medline]61,Kaul V, Gallo de Moraes A, Khateeb D, et al. Medical education during the COVID-19 pandemic. Chest. May 2021;159(5):1949-1960. [CrossRef] [Medline]62], which has been shown to affect student learning outcomes positively [Vallée A, Blacher J, Cariou A, Sorbets E. Blended learning compared to traditional learning in medical education: systematic review and meta-analysis. J Med Internet Res. Aug 10, 2020;22(8):e16504. [CrossRef] [Medline]63]. This can explain the steady increase in the number of studies that use TAM to understand user acceptance of learning interventions.

The low adoption rate of the TAM within medical education may be due to the focus on prioritizing satisfaction and basic usage statistics [Gray JA, DiLoreto M. The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation. 2016. URL: https://eric.ed.gov/?id=EJ1103654 [Accessed 2025-06-19] 64-Wu JH, Tennyson RD, Hsia TL. A study of student satisfaction in a blended e-learning system environment. Comput Educ. Aug 2010;55(1):155-164. [CrossRef]66] when evaluating new technologies for learning. This approach overlooks the more nuanced dimensions that TAM examines, such as perceived usefulness and perceived ease of use. This emphasis on program-level satisfaction metrics fails to capture the complex psychological and organizational factors influencing technology acceptance in health care educational environments. Such reliance on superficial evaluation matrices creates a significant gap between measuring program satisfaction and truly understanding the complex factors driving technology acceptance and sustained use in medical education.

The COVID-19 pandemic caused a global shift to digital platforms for learning. This created an urgent need to understand technology adoption in education and health care. The TAM became a framework for evaluating user acceptance of rapidly implemented technologies like e-learning platforms [Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26,Johnson EM, Howard C. A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc. Jan 2019;107(1):30-42. [CrossRef] [Medline]34,Reed DA, Cook DA, Beckman TJ, Levine RB, Kern DE, Wright SM. Association between funding and quality of published medical education research. JAMA. Sep 5, 2007;298(9):1002-1009. [CrossRef] [Medline]56] and mobile learning [Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. Jul 21, 2009;339(jul21 1):b2535. [CrossRef] [Medline]19-Microsoft Teams. Microsoft. 2023. URL: https://www.microsoft.com/en-us/microsoft-teams/group-chat-software [Accessed 2025-06-19] 21]. The forced accelerated adoption of mobile and web-based learning highlighted the importance of TAM in assessing factors such as perceived usefulness and ease of use for remote teaching tools. The pandemic served as a global natural experiment in technology adoption, driving researchers to apply TAM across diverse contexts to address barriers to digital transitions. This surge in TAM applications demonstrated its adaptability in analyzing critical acceptance factors [Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32,Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36,Bhardwaj M, Kashyap S, Aggarwal D, Bhawani R. Perceptions and experience of medical students regarding e-learning during COVID-19 lockdown- a cross-sectional study. JCDR. 2022. [CrossRef]42,Komuhangi A, Mpirirwe H, Robert L, Githinji FW, Nanyonga RC. Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda. BMC Med Educ. Sep 10, 2022;22(1):671. [CrossRef] [Medline]46] during systemic disruptions, offering insights into user behavior that were essential for navigating the rapid technological transformations brought on by the crisis.

In this systematic review, each study with a quantitative element was appraised using the MERSQI, which is designed to assess the quality of published medical education research. Typically, a higher score is often associated with greater methodological rigor and would result in higher acceptance to quality journals [Reed DA, Beckman TJ, Wright SM, Levine RB, Kern DE, Cook DA. Predictive validity evidence for medical education research study quality instrument scores: quality of submissions to JGIM’s Medical Education Special Issue. J Gen Intern Med. Jul 2008;23(7):903-907. [CrossRef] [Medline]67,Reed DA, Beckman TJ, Wright SM. An assessment of the methodologic quality of medical education research studies published in The American Journal of Surgery. Am J Surg. Sep 2009;198(3):442-444. [CrossRef] [Medline]68]. With a mean MERSQI score of 9.6 (SD 1.17), the average score found within this review was higher than that found in a paper by Smith and Learman [Smith RP, Learman LA. A plea for MERSQI: the Medical Education Research Study Quality Instrument. Obstet Gynecol. Oct 2017;130(4):686-690. [CrossRef] [Medline]57], yet it did not reach the benchmark of the high-quality score of 10.5 (SD 2.5) described by Reed et al [Reed DA, Beckman TJ, Wright SM, Levine RB, Kern DE, Cook DA. Predictive validity evidence for medical education research study quality instrument scores: quality of submissions to JGIM’s Medical Education Special Issue. J Gen Intern Med. Jul 2008;23(7):903-907. [CrossRef] [Medline]67]. Our analysis indicates that the substantial scores in this review are partly due to the inclusion of the TAM. Given that the TAM is a validated survey tool, its use—whether in its original or modified version—immediately contributes to a base score of five: 3 points for the tool’s validity and 2 points for measuring behavioral outcomes. A study can accrue 4-5 points by using a methodologically robust and sound approach in the study design and reporting. Therefore, incorporating the TAM may potentially contribute to a higher quality of publication output.

Operationalizing the TAM

The TAM was originally developed as a theoretical framework based on the Theory of Planned Behavior [Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. Dec 1991;50(2):179-211. [CrossRef]69], which can be operationalized as a survey based on the constructs within the model. This review found that the prevalent application of TAM in studies is through survey instruments, aligning with findings from other reviews [Al-Emran M, Mezhuyev V, Kamaludin A. Technology Acceptance Model in M-learning context: a systematic review. Comput Educ. Oct 2018;125:389-412. [CrossRef]59]. Apart from the survey instrument, the TAM can be used qualitatively, such as adapting the constructs to guide the discourse in focus group discussions [Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32] or semistructured interviews [Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. Dec 2021;33(4):349-367. [CrossRef] [Medline]39].

Several different statistical analysis approaches were used to analyze the qualitative data. SEM stands out as one of the most comprehensive methods, adept at testing hypotheses concerning both observed and latent variables [Burnette J, Williams L. Structural equation modeling (SEM): an introduction to basic techniques and advanced issues. In: Research in Organizations. Berrett-Koehler Publishers; 2005. ISBN: 978-1-60509-333-870]; these studies generally had higher MERSQI scores [Do DH, Lakhal S, Bernier M, Bisson J, Bergeron L, St-Onge C. Drivers of iPad use by undergraduate medical students: the Technology Acceptance Model perspective. BMC Med Educ. Feb 8, 2022;22(1):87. [CrossRef] [Medline]10,Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12,Tahamtan I, Pajouhanfar S, Sedghi S, Azad M, Roudbari M. Factors affecting smartphone adoption for accessing information in medical settings. Health Info Libraries J. Jun 2017;34(2):134-145. [CrossRef]29,Kucuk S, Baydas Onlu O, Kapakin S. A model for medical students’ behavioral intention to use mobile learning. J Med Educ Curric Dev. 2020;7:2382120520973222. [CrossRef] [Medline]36,Almarzouqi A, Aburayya A, Salloum SA. Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach. IEEE Access. 2022;10:43421-43434. [CrossRef]41,Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45]. Despite its robustness, SEM demands a thorough grasp of complex statistical concepts and a sufficiently large sample size to ensure the stability and accuracy of its estimates [Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. EBR. Jan 14, 2019;31(1):2-24. [CrossRef]71]. Correlation analysis offers a more straightforward approach to measuring the strength and direction of relationships between variables. Regression analysis further extends the analytical capability by providing predictive insights and facilitating the exploration of potential causal links between factors. Additionally, the TAM is frequently used in a descriptive capacity, offering an interpretive lens to dissect and articulate the intricacies of user interactions with technology, their attitudes, and the behavioral intentions that these factors precipitate.

This systematic review found that a large number of learning interventions were investigated for e-learning. This could be explained by the shift in higher education over the past 2 decades to web-based learning [Lim CP. Trends in online learning and their implications for schools. Educ Technol. 2002;42(6):43-48. URL: https://www.jstor.org/stable/44428792 [Accessed 2025-06-19] 72], accelerated by the COVID-19 pandemic, which necessitated and expedited the transition to web-based learning across various disciplines [Kumar A, Kumar P, Palvia SCJ, Verma S. Online education worldwide: current status and emerging trends. Journal of Information Technology Case and Application Research. Jan 2, 2017;19(1):3-9. [CrossRef]73], including medical education. The integration of mobile technology into our everyday lives has naturally extended into the realm of education. This has prompted research on mobile devices for information access [Gagnon MP, Ngangue P, Payne-Gagnon J, Desmartis M. m-Health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. Jan 2016;23(1):212-220. [CrossRef] [Medline]15,McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res. Sep 24, 2012;14(5):e117. [CrossRef] [Medline]23,Duch Christensen M, Oestergaard D, Dieckmann P, Watterson L. Learners’ perceptions during simulation-based training: an interview study comparing remote versus locally facilitated simulation-based training. Simul Healthc. Oct 2018;13(5):306-315. [CrossRef] [Medline]32,Lau KHV, Greer DM. Using technology adoption theories to maximize the uptake of e-learning in medical education. Med Sci Educ. Apr 2022;32(2):545-552. [CrossRef] [Medline]47] and mobile apps for learning [Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a Technology Acceptance Model for apps in medical education. J Med Syst. Nov 2015;39(11):176. [CrossRef] [Medline]12,Harmon DJ, Burgoon JM, Kalmar EL. Development and assessment of an integrated anatomy mobile app. Clin Anat. Jul 2022;35(5):686-696. [CrossRef] [Medline]45].

Additionally, this systematic review found studies delving into more innovative educational technologies beyond e-learning, such as virtual reality, augmented reality, serious games, and 3D printing. Virtual reality allows for students to practice their technical skills repeatedly in a risk-free setting, thus increasing their confidence and proficiency without jeopardizing patient safety [Fang TY, Wang PC, Liu CH, Su MC, Yeh SC. Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training. Comput Methods Programs Biomed. Feb 2014;113(2):674-681. [CrossRef] [Medline]25,Hogg ME, Tam V, Zenati M, et al. Mastery-based virtual reality robotic simulation curriculum: the first step toward operative robotic proficiency. J Surg Educ. 2017;74(3):477-485. [CrossRef] [Medline]74]. Augmented reality overlays digital information onto physical or live environments, allowing students to understand complex anatomical structures [Duncan-Vaidya EA, Stevenson EL. The effectiveness of an augmented reality head-mounted display in learning skull anatomy at a community college. Anat Sci Educ. Mar 2021;14(2):221-231. [CrossRef] [Medline]6] and to gain spatial awareness [Küçük S, Kapakin S, Göktaş Y. Learning anatomy via mobile augmented reality: effects on achievement and cognitive load. Anat Sci Educ. Oct 2016;9(5):411-421. [CrossRef] [Medline]75]. Another emerging tool that combines interactive gameplay with educational outcomes is serious games, which simulate real-world medical scenarios in a controlled and engaging environment [Knight JF. Acceptability of video games technology for medical emergency training. Int J Gaming Comput Mediat Simul. Oct 2013;5(4):86-99. [CrossRef]24,Bianchi I, Stefani CJM, Santiago P, Zanatta AL, Rieder R. AnemiaAR: a serious game to support teaching of haematology. J Vis Commun Med. Jul 2022;45(3):134-153. [CrossRef] [Medline]43]. 3D printing allows for the rapid development of customized models that can be used for teaching and learning [Ryan JR, Chen T, Nakaji P, Frakes DH, Gonzalez LF. Ventriculostomy simulation using patient-specific ventricular anatomy, 3D printing, and hydrogel casting. World Neurosurg. Nov 2015;84(5):1333-1339. [CrossRef] [Medline]26]. Together, these technologies are changing the way learning is happening in medical education by providing immersive, interactive, and accessible tools to complement traditional teaching approaches. The TAM can serve as a valuable framework for understanding how these new and existing technologies are adopted and used for learning in medical education. By having a framework, educators and institutions can use the TAM to evaluate the integration of these technologies into their curricula and their potential for improving educational outcomes.

Limitations and Future Research

One limitation of this review is that it only encompasses data published up until 2023. Given the observed publication trend, it is plausible that subsequent studies using TAM in 2024 and beyond fall outside the scope of this review. Consequently, the conclusions drawn here are pertinent to the specified research period. Future studies should consider extending the review to include these additional years, thereby capturing a more comprehensive dataset, potentially offering a more current evaluation of TAM’s application in the field.

Although this review contributes valuable insights into the use and application of TAM in education, the findings are primarily within the context of medical education and exclude other health professions, including nursing and allied health professionals. Medical education is a highly specialized domain with unique challenges and practices that may not directly translate to the broader educational context outside of medicine. Furthermore, the complexity and heterogeneity within medical education, such as the variation in curricula and culture, may pose an additional challenge to generalizing findings even within the discipline.

Despite these limitations, the framework used in this review offers significant potential for broader application across other health care disciplines. Researchers could adapt this approach to explore TAM’s adoption and effectiveness in nursing education, allied health training, or interdisciplinary health care programs. Expanding research beyond medical education would enhance the generalizability of findings and provide comparative insights into how TAM influences technology adoption across diverse health care professions. Additionally, extending TAM-based research to non–health care fields could further enrich our understanding of its applicability and utility in varied educational contexts.

Future researchers should consider adopting the additional constructs in TAM2 to better understand how social and cognitive factors influence technology acceptance beyond the perceived ease of use and perceived usefulness. For example, researchers can investigate how subjective norms or job relevance may influence the students’ willingness to adopt new technologies and professional identities in an educational context.

Conclusions

This systematic review aimed to understand the use of the TAM in medical education over the past two decades, highlighting its utility as both a theoretical framework and survey instrument. This study reported on TAM’s increasing popularity and versatility for measuring and understanding the learners’ acceptance of the intervention. With the increasing integration of e-learning, digital learning, and other new learning modalities, it is critical that researchers can leverage technologies that learners will adopt. Such curriculum innovations are critical for maintaining educational continuity in the face of global health challenges by facilitating remote learning and continuous professional development. Consequently, these curricular reforms are expected to catalyze a significant surge in the adoption of digital technologies within medical education.

The growing importance of the TAM in understanding technology acceptance cannot be overstated, especially in medical education, where the use of artificial intelligence, virtual reality, and other adaptive learning platforms is increasingly popular. Educators and developers can use TAM as a theoretical framework to design curricula or interventions considering barriers to adoption, such as organizational support or the intervention’s technical complexity. The TAM is relevant as an evaluation tool and can guide future innovations in medical education. Policymakers should consider using the insights gained using the TAM to develop strategies for the education system while meeting the challenges of cost, accessibility, and infrastructure development.

This review provides a comprehensive understanding of how the TAM has been applied within the field of medical education over the past 20 years. As the field continues to innovate, TAM will continue to play an important role in helping educators, policymakers, and researchers understand the dynamics of technology integration and the impact on teaching and student learning outcomes.

Acknowledgments

This work was supported by the National University of Singapore’s Learning Improvement Teaching Enhancement Grant (TEG).

Conflicts of Interest

None declared.

Checklist 1

PRISMA checklist. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

PDF File, 62 KB

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MERSQI: Medical Education Research Study Quality Instrument
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
SEM: structural equation modeling
TAM: Technology Acceptance Model


Edited by Joshua Moen; submitted 23.10.24; peer-reviewed by Kamel Mouloudj, Olanrewaju Egunlae; final revised version received 26.03.25; accepted 12.05.25; published 16.07.25.

Copyright

© Jason Wen Yau Lee, Jenelle Yingni Tan, Fernando Bello. Originally published in JMIR Medical Education (https://mededu.jmir.org), 16.7.2025.

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