Jul 2026· Journal of Advances in Medical Education and Professionalism· Vol 14, pp. 220 - 237· 0 citations· 65 references
Medicine
TL;DR
The integration of Artificial Intelligence into individualized educational experiences represents a transformative model for medical education that enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes.
Abstract
Introduction: The rapid advancement of Artificial Intelligence (AI) in medical education is driving a shift from traditional instructional design methods toward personalized and adaptive learning models. Despite numerous promising applications, the available evidence remains limited and fragmented; therefore, a comprehensive synthesis of the evidence is needed to support robust conclusions. Methods: This study employed the four-phase meta-synthesis framework proposed by Sandelowski and Barroso. A systematic search was conducted across Medline, Embase, CINAHL, PsycINFO, PubMed, Web of Science, ScienceDirect, Wiley Online Library, SpringerLink, Taylor & Francis Online, SAGE Journals, and Scopus, covering publications from 2010 to 2025. Studies were screened according to predefined inclusion and exclusion criteria, and their methodological quality was evaluated using the Critical Appraisal Skills Programme (CASP). Coding reliability was assessed through a test–retest procedure, resulting in a reliability coefficient of 0.81. Results: A total of 273 records were identified, of which 16 studies met the inclusion criteria and obtained CASP scores exceeding the threshold of 30. Content analysis revealed five principal domains: faculty-related applications (21%), student-related applications (28%), applications in the learning process (15%), curriculum development (13%), and assessment mechanisms (23%). Student-related applications constituted the largest proportion, highlighting the pivotal role of learner-centered personalization in AI-driven medical education. Conclusion: The integration of Artificial Intelligence (AI) into individualized educational experiences represents a transformative model for medical education. AI enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes. This synthesis proposes an overarching conceptual framework to inform policy, research, and implementation in the context of AI-supported personalized medical education.
Keyword and thematic analyses showed that current research attention is mainly concentrated on ChatGPT, large language models, natural language processing, clinical reasoning simulation, personalized learning, virtual patient interaction, and ethical governance.
Jia Zhang, Yuanzhou Liu, Bo Wang· Medicine· 0 citations
Artificial intelligence (AI) is transforming medical education, enhancing knowledge acquisition, teaching, assessment, and curriculum delivery. While AI offers the potential to democratise access and improve inclusivity, little is known about how equity is addressed in AI-enabled medical education. This scoping review maps current evidence, identifies gaps, and provides insights for equitable implementation.
A scoping review was conducted following the Joanna Briggs Institute methodology. Peer-reviewed literature was identified through MEDLINE, Evidence-Based Medicine Reviews (Health Technology Assessment), Health and Psychosocial Instruments and Global Health. Screening, data extraction, and thematic analysis were performed by multiple reviewers. Quantitative data were summarised descriptively, and qualitative data were synthesised to identify narrative themes.
Of 256 records identified, 80 full-text articles were reviewed, and 35 studies were included. Most were published between 2022 and 2025 and originated predominantly from high-income countries. AI applications focused on large language models (48%), with curriculum design (49%) being the most common area of focus. Equity themes were: (1) equitable access (demographic, geographic, and distributed learning disparities); (2) bias (algorithmic, cultural, and socioeconomic); and (3) AI literacy (limited curricular exposure and preparedness).
Previous literature reviews have examined AI and its ethical use in medical education, but not through an equity lens. This scoping review highlights that medical education is at a pivotal juncture, shaped by converging imperatives: inclusivity and the rapid evolution of AI. To expand access and standardisation, equitable implementation requires AI literacy, contextually relevant tools, and active bias mitigation. Future research should prioritise inclusive, contextually appropriate, and accessible AI interventions across medical education.
J. Beattie, N. Waidyatillake, Cailin Mellberg et al.· International Conference on...· 0 citations
Aim: This meta-analysis examined the effectiveness of artificial intelligence (AI)-based educational interventions on learning outcomes in nursing students.Material and Method: A systematic search of PubMed, Scopus, CINAHL, and Web of Science identified studies published between January 2021 and May 2025. Following PRISMA guidelines, 11 studies with 1,539 students were included, comprising randomized controlled trials and quasi-experimental designs. Outcomes assessed were knowledge, clinical reasoning, satisfaction, clinical performance, attitude, and self-efficacy. Risk of bias was evaluated with the RoB 2 tool, and random-effects models were used for meta-analysis.Results: AI-based interventions significantly improved knowledge acquisition (MD = 4.47, 95% CI [2.60, 6.34], p
Orkun Erkayıran· Bandırma Onyedi Eylül Üniver...· 0 citations
The findings showed that AI can enhance clinical teaching, improve nursing students' self-efficacy, and support teaching and learning and that the use of AI in nursing education is instrumental in improving the acquisition of clinical skills and teaching and learning.
S. Khunou, Carine Prinsloo· Indonesian Contemporary Nurs...· 0 citations
Nursing students’ experiences with generative AI are shaped by both the opportunities and challenges associated with its use in learning, highlighting the need for nursing educators to strengthen students’ AI literacy, critical thinking, and ethical awareness.
Shanshan Du, Sha Wang, Feng-ming Yan et al.· Frontiers in Medicine· 0 citations
Artificial intelligence (AI) is increasingly transforming mathematics education through adaptive tutoring systems, automated feedback, and AI-supported assessment tools. However, evidence regarding the effectiveness of these technologies remains dispersed across diverse contexts and study designs. This systematic review synthesised empirical research on the effectiveness of AI-based tutoring and assessment systems in mathematics education published between 2015 and 2025. Guided by the PRISMA framework, a comprehensive search was conducted in Scopus and Web of Science using database-specific search strings. After screening 1,749 records and assessing 76 full-text articles for eligibility, 12 studies met the inclusion criteria and were included in the final synthesis. Data were extracted using a structured framework and study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Due to heterogeneity in interventions, populations, and outcome measures, a narrative synthesis was employed. Findings indicate that AI-based tutoring systems and adaptive learning platforms generally support improvements in mathematics achievement, particularly among lower-performing learners, although effectiveness varies depending on implementation fidelity, learner characteristics, and instructional context. Studies focusing on generative AI tools such as ChatGPT primarily reported positive perceptions and increased engagement, but evidence of direct achievement gains remains limited. Overall, the evidence base demonstrates moderate methodological quality, with stronger conclusions drawn from experimental and quasi-experimental designs. The review highlights the potential of AI-based tutoring and assessment systems to enhance mathematics learning while emphasising the need for rigorous, large-scale experimental research and clearer reporting of intervention mechanisms.
Neo J. Molemane, Moeketsi Mosia, F. Egara· Discover Education· 2 citations