ARTIFICIAL INTELLIGENCE (AI) TOOL USE VERSUS EXPERT-LED INSTRUCTION IN PRECLINICAL MEDICAL EDUCATION: A CROSSSECTIONAL SURVEY OF STUDENT EXPERIENCE, PERCEPTIONS, AND ACADEMIC PERFORMANCE
Aug 2026· Journal of Southeast Asian Medical Research· Vol 10, pp. e0304· 0 citations· 38 references
TL;DR
These findings support incorporating AI literacy and ethics into preclinical curricula while preserving the strengths of expert-led instruction.
Abstract
Background: Artificial Intelligence (AI) is increasingly integrated into preclinical medical education. Despite rapid adoption, comparative data on student satisfaction, perceptions, learning preferences, and academic outcomes remain limited. This study aimed to evaluate these outcomes among preclinical medical students.
Methods: A cross-sectional study was conducted among preclinical medical students at Phramongkutklao College of Medicine, Bangkok, Thailand (June 2024–May 2025). A validated questionnaire was administered. Academic performance was measured by cumulative grade point average (GPAX) and stratified by AI usage intensity: more-AI-using (>5 h/week) and less-AI-using (≤5 h/week). Statistical analyses included the Wilcoxon signed-rank test and an independent t-test (p < 0.05).
Results: A total of 220 students completed the questionnaire (Year 2: 44.1%; male: 55.9%; mean age: 19.4 ± 1.6 years). 90.9% had previously used AI tools in preclinical studies, most commonly for 3–5 hours per week. Expert-led instruction was rated significantly higher than AI for effectiveness, preclinical confidence, clinical readiness, and overall satisfaction. 90.5% preferred expert-led instruction as the primary modality; however, 87.7% endorsed an integrated AI–expert framework (concurrent or AI after expert). Despite 93.6% of students reporting that AI positively impacted their learning outcomes and 82.7% reporting improved problem-solving skills, GPAX did not differ significantly between the more AI-using (>5 hours/week, 3.21 ± 0.64) and less AI-using (≤5 hours/week, 3.27 ± 0.56) groups (p = 0.100).
Conclusion: Preclinical medical students used AI tools extensively but rated expert-led instruction higher in terms of overall satisfaction. No significant association was found between self-reported AI-use intensity and academic performance. Students favored a blended model that positions AI as a complement to expert-led instruction. These findings support incorporating AI literacy and ethics into preclinical curricula while preserving the strengths of expert-led instruction.
Artificial intelligence (AI) platforms have increasingly integrated into medical education, offering innovative avenues for enhancing critical thinking, subjective learning, and clinical reasoning. Large language models such as ChatGPT, Gemini, and Meta AI simulate human-like responses and are increasingly being utilized as adjunctive educational tools. However, systematic evaluations comparing their performance within competency-based medical education (CBME) in pharmacology remain limited.
A cross-sectional comparative study was conducted to evaluate ChatGPT, Gemini, and Meta AI in answering 55 higher-order pharmacology questions mapped to CBME undergraduate competencies and postgraduate standards. Responses were anonymized and independently scored by three blinded pharmacology faculty members for accuracy, completeness, and clarity. Statistical analysis included the Friedman test, Wilcoxon signed-rank test for pairwise comparisons, and repeated measures analysis of variance (ANOVA). Data visualization was performed using bar graphs and box-and-whisker plots.
ChatGPT achieved the highest mean score (8.5 ± 1.0), followed by Gemini (7.9 ± 1.2) and Meta AI (6.4 ± 1.5). The Friedman test indicated a highly significant difference among the platforms (
χ
² = 96.69,
P <
0.001).
Post hoc
Wilcoxon signed-rank tests showed that ChatGPT significantly outperformed both Gemini and Meta AI (
P <
0.001), while Gemini also outperformed Meta AI (
P <
0.001). Repeated measures ANOVA confirmed significant differences in mean scores across platforms (
F
= 217.36,
P <
0.001).
Among the evaluated AI platforms, ChatGPT demonstrated superior performance in solving higher-order pharmacology questions aligned with CBME frameworks. These findings support the integration of AI tools, as adjunctive resources in pharmacology education.
Tasneem Hussain, Manan Parmar, Ashutosh Tiwari et al.· National Journal of Pharmaco...· 0 citations
AI-based technologies are rapidly being adopted in healthcare. This underscores the need for AI literacy among physicians and healthcare professionals, encompassing core knowledge, practical skills, and ethical awareness. This study assessed a 2-credit course, “Artificial Intelligence in Healthcare,” given to graduate-level medical students. A one-group pretest-posttest quasi-experimental design was used. Thirty-nine students completed both the knowledge assessment and the satisfaction survey. Students' knowledge scores rose from pretest $(M=7.13)$ to posttest $(M=8.10)$, with a mean difference of 0.97 (95% CI: $0.62-1.31; \mathrm{t}(38)=5.72,\ \mathrm{p}<0.001)$, showing a large effect size (Cohen's $\mathrm{d}=0.92)$. Student satisfaction was high (Mean = 4.63-4.81; Cronbach's $\alpha=0.951)$, indicating the course content met learner needs. Findings suggest short-term, practice-oriented AI education boosts AI literacy. Key improvements were seen in prompt engineering, ethical awareness, and clinical application. More research is needed, especially with performance-based assessments and multi-center designs.
Nyamkhuu Lkhagva, Orgilmaa Battulga, Bayarmaa Purewsuren et al.· 2026 6th International Confe...· 0 citations
Background: The emergence of generative artificial intelligence (AI), particularly with platforms like OpenAI, has brought about a paradigm shift in problem-solving and decision-making approaches. One sector that has notably embraced AI is education, where its integration has revolutionized traditional teaching and learning methods. While prior reports have highlighted the opportunities AI presents in education, they also emphasize the associated risks. Despite these concerns, proponents argue that incorporating AI into education could potentially better prepare students for evolving industry demands.
Objective: The objective of this study is to explore the perceptions of faculty and students at the University of [blinded] regarding AI usage in education.
Methods: The study, approved by the [blinded] Institutional Review Board, employed a mixed-methods design. Quantitative data were collected through a cross-sectional survey distributed to faculty and students. The survey included questions on demographics and perceptions of AI in education. Participants were also given the option to express interest in follow-up interviews. Interviews were conducted with willing participants to further explore their views on AI in education.
Results: The quantitative data revealed that the majority of the faculty and students were aware of and somewhat familiar with AI tools. They generally perceived AI as a beneficial addition to education. However, concerns about AI included potential dependency and the ethical implications of AI-generated content. The follow-up interviews provided deeper insights, with participants expressing optimism about AI's potential to transform education while emphasizing the need for robust ethical guidelines and training to maximize its benefits.
Conclusion: The findings suggest a positive perception of AI among both faculty and students at [blinded], highlighting its potential to enhance personalized learning and better prepare students for future industry demands. However, there is a clear need for comprehensive ethical guidelines and training to address concerns about dependency and the ethical use of AI. The study emphasizes the importance of balancing the benefits of AI with the associated risks to ensure its effective and responsible integration into educational practices. Continued research and dialogue are essential to navigate the evolving landscape of AI in education, ultimately aiming to enhance the learning experience and outcomes for students in healthcare education.
Keywords: Artificial intelligence, perceptions, education, survey, interview
X. Gordy· The Journal of Scholarship o...· 0 citations
ABSTRACT Introduction: Artificial Intelligence (AI) has been progressively incorporated into medical education, modifying how knowledge is accessed and utilized. Objective: To analyze medical students’ perceptions regarding the use of Artificial Intelligence in the learning process. Methods: A descriptive, cross-sectional study with a mixed-methods approach was conducted with 108 medical students. Data were collected through an online structured questionnaire and analyzed using descriptive statistics and thematic content analysis. Results: Most participants (97.2%) reported using AI tools, with ChatGPT being the most frequently used (95.4%). The main applications included tutoring (84.3%), academic activities (65.7%), and basic disciplines, particularly anatomy/morphofunctional sciences (50.9%) and physiology (41.7%). Reported benefits included improved understanding of complex content (85.2%), time optimization (78.7%), and rapid access to information (63%). Concerns were identified regarding information reliability, algorithmic bias, and technological dependence. Most students (79.6%) showed a preference for hybrid learning methods. Conclusion: Artificial Intelligence is widely integrated into students’ academic routines and is perceived as a complementary tool whose use requires a critical approach.
Isabel Mitsu Brito Kanashiro, Naudia da Silva Dias, Ana Cecília Perotes Albuquerque et al.· Revista Brasileira de Educaç...· 0 citations
Background: The rapid growth of Artificial Intelligence (AI) is revolutionizing healthcare delivery and health professional education by supporting personalized, technology-driven learning approaches. However, little is known on the uptake and application of AI in middle-level health professional training institutions in Kenya. Kenya Medical Training College (KMTC) is the largest public middle-level health professional training institution which plays a critical role in producing human resource for health. Understanding the extent to which AI is utilized within KMTC is therefore essential for informing institutional policies and strategies for embedding AI technology into health professional education.
Methods: A cross-sectional study was conducted between November 2024 and November 2025 across eight randomly selected KMTC campuses. Multistage sampling technique was employed. Data was collected using structured electronic questionnaires. A total of 581 respondents participated, representing a 97% response rate. Data were analyzed using SPSS to generate descriptive statistics and associations.
Results: Overall, 76.3% of students reported using AI during their studies, while 56.3% of lecturers had used AI to support teaching and learning activities. ChatGPT was the most commonly used AI tool (66.3%). Among students, AI use was significantly associated with course of study (χ² = 66.79, p < 0.001), age group (χ² = 12.83, p < 0.046), year of study (χ² = 10.96, p < 0.004), and level of training (χ² = 6.58, p < 0.037). Among lecturers, AI use was significantly associated with level of education (χ² = 13.23, p < 0.039). Major challenges included limited internet access (students 52.5%), limited technical capacity (lecturers 31.3%), and inadequate knowledge of AI tools. Notably, 66.1% of students believed AI will become increasingly important in future medical practice.
Discussion: AI uptake within KMTC setting is growing however variations exists reflecting differences in digital literacy, internet access, and exposure to digital tools across programs and campuses. Limited infrastructure and low technical capacity appears to impede integration of AI into teaching and learning. Strengthening digital infrastructure, building faculty and student capacity in responsible AI use, and developing institutional policies and guidelines could facilitate more effective adoption.
Kipkemoi Moreen, OnyangoA. Christopher, Miriti Anderson et al.· International journal of res...· 0 citations
Artificial intelligence (AI) is rapidly transforming healthcare, medical education, and scientific research. As AI integration into medical education becomes inevitable, faculty development programs are needed to equip medical educators with the necessary knowledge, skills, and attitudes. This study aimed to analyze the AI training needs of faculty members and teaching assistants at the Alexandria Faculty of Medicine (AFM) as a crucial step in planning an AI faculty development program. A web-based cross-sectional survey was conducted to assess respondents’ current knowledge, attitudes and practices regarding the use of AI in their professional practice, along with their learning preferences. A total of 336 faculty members and teaching assistants completed the survey. Quantitative data were analyzed using descriptive statistics, while qualitative responses were summarized via content analysis. While 52.1% of respondents expressed a high interest in integrating AI into their professional practice and 55.6% considered learning about AI highly important, about 56.0% were unaware of AI uses in medicine, and 67.0% had never used AI applications in their work. Key barriers included limited access to AI tools (72.9%), insufficient knowledge (64%), and a lack of training opportunities (60.7%). Most respondents (84.2%) preferred workshop-based training. This study highlights the need for a faculty development program to develop AI competencies of faculty members to fully leverage AI tools and mitigate their limitations at AFM. The findings provide initial guidance for the planning of context‑appropriate AI faculty development initiatives for medical educators.
N. Elnemr, S. R. Aref, Aly Abdelmohsen et al.· Discover Education· 0 citations