Artificial Intelligence Literacy and Educational Needs among Students at a Korean Medicine College: A Mixed-Methods Study

Artificial Intelligence Literacy and Educational Needs among Students at a Korean Medicine College: A Mixed-Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial Intelligence Literacy and Educational Needs among Students at a Korean Medicine College: A Mixed-Methods Study Seonghoon Bae, Seojae Jeon, Wonbae Ha, Junghan Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8525533/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study assessed artificial intelligence literacy among students at a college of Korean Medicine and examined their educational needs and preferred curriculum formats to inform the effective integration of artificial intelligence literacy into future curricula. A mixed-methods design was employed involving 562 students from Wonkwang University College of Korean Medicine. A structured questionnaire was administered across all academic years, and semi-structured interviews were conducted annually with 30 randomly selected students. The findings revealed uneven levels of artificial intelligence literacy, with relatively low technical understanding and practical ability but high levels of critical thinking and ethical awareness. Most students recognized artificial intelligence’s potential in Korean Medicine, particularly for diagnostic support, and expressed a strong need for formal artificial intelligence education. Practice-based and clinically oriented training formats were preferred. Cross-analysis indicated that clinical and educational experiences significantly shaped students’ perceptions of artificial intelligence; students with prior artificial intelligence exposure or higher levels of artificial intelligence literacy demonstrated greater interest in further training. Overall, participants acknowledged both the promise of artificial intelligence and the importance of structured, progressive education that extends beyond theoretical learning toward clinical applications. Stepwise curricula, progressing from foundational knowledge in premedical years to clinical applications in later stages, may be most effective in fostering artificial intelligence literacy in Korean Medicine education. medical artificial intelligence artificial intelligence literacy Korean medicine educational needs mixed-methods study Figures Figure 1 Figure 2 Figure 3 1. Introduction Artificial intelligence (AI) holds substantial potential for application across a wide range of medical domains, including image interpretation, data analysis, treatment planning, and prognosis prediction. Amidst these transformations, AI literacy has emerged as a core competency for future healthcare professionals. As the use of AI in clinical practice continues to expand, the importance of AI literacy in medical education has become increasingly evident. This emphasis extends beyond the acquisition of technological skills; it represents an essential prerequisite for healthcare professionals to safely and responsibly utilize AI. AI literacy encompasses a comprehensive set of competencies that extend beyond operational skills. It involves understanding the fundamental concepts and mechanisms of AI, developing practical application abilities, cultivating critical evaluation skills, and recognizing the ethical and social implications of AI [ 1 ]. Internationally, four key dimensions have been proposed as frameworks for AI literacy: (1) technical understanding, (2) practical ability, (3) critical thinking, and (4) ethical and social awareness [ 2 ]. In this study, a fifth domain, educational needs, was included as an independent analytical component. Although not a direct indicator of literacy competence, educational needs have been consistently emphasized in national healthcare education policies and serve as important indicators of students’ perceived learning requirements [ 3 ]. In the field of Korean medicine, AI applications have gradually begun to emerge. Recent domestic and international studies have reported various initiatives, including the development of predictive models for acupuncture treatment response [ 4 ], the introduction of clinical education programs using large language models such as ChatGPT [ 5 ], and the construction of diagnostic support algorithms based on electronic medical records (EMR) [ 6 ]. These studies demonstrate the feasibility and potential of AI implementation in Korean medicine. As the need for AI education in healthcare becomes widely recognized, diverse efforts have been made to integrate AI-related content into medical curricula. However, the content and depth of such education vary significantly across countries as well as institutions, and standardized curricula and guidelines remain limited [ 7 , 8 ]. Although discussions on AI education have begun in the context of Korean medicine, practical implementation remains limited. Most prior studies were conducted among Western medical students, and research involving Korean medical students remains scarce [ 9 , 10 ]. Therefore, this study aimed to evaluate the level of AI literacy among students enrolled in colleges of Korean medicine and to analyze their educational needs and preferred instructional formats. The objective was to provide foundational data to support the effective integration of AI literacy into Korean medical curricula. By examining students’ perceptions of necessary educational content and appropriate curriculum structures, this study seeks to contribute to establishing clear directions and institutional foundations for implementing AI literacy education, specifically regarding timing, content design, and priority setting, within Korean medical education. 2. Methods 2.1. Study Design and Participants This study was approved by the Institutional Review Board of Jangheung Integrative Medical Hospital, Wonkwang University (IRB No. WKUJIM-202507-003). The target population comprised 562 students enrolled in the College of Korean Medicine at [University Name]. A mixed-methods design was employed, integrating both quantitative surveys and qualitative interviews to comprehensively explore participants’ AI literacy levels and educational needs. A structured questionnaire was developed using an online survey platform (Google Forms). The survey link was distributed to student groups by academic year and participation was voluntary. Quantitative data were collected between August 4 to August 25, 2025. For the qualitative component, semi-structured, and in-depth interviews were conducted using an online videoconferencing platform (Zoom). Thirty participants were randomly selected, comprising five students from each academic year. Participants were selected without considering specific demographic factors such as gender, academic performance, or interest in AI to ensure a balanced representation of experiences and perceptions across academic levels. 2.2. Survey Contents The questionnaire items were developed by the researchers based on prior studies, with modifications to reflect the unique characteristics and educational needs of Korean medical education [ 11 ]. The final questionnaire included 31 items assessing AI literacy competencies, attitudes, general characteristics, perceptions of AI applicability in Korean medicine, and educational needs as well as preferences. All items were rated on a five-point Likert scale, with some questions designed as open-ended questions to obtain more detailed insights. AI literacy was measured across four internationally recognized domains: (1) technical understanding, (2) practical ability, (3) critical thinking, and (4) ethical and social awareness. An additional domain, educational needs, was included to assess students’ perceived demands for AI-related learning opportunities. For the qualitative component, thematic analysis was conducted on semi-structured interview data to identify key categories and themes. Interview questions focused on participants’ experiences with AI, understanding of AI literacy competencies, perceptions of AI–Korean medicine integration, and opinions on the appropriate timing and methods for AI education. 2.3. Statistical Analysis All survey data were analyzed using SPSS Statistics version 28.0 (IBM Corp., Armonk, NY, USA). Participants’ general characteristics were summarized using frequencies and percentages, and AI literacy scores for each domain were presented as means and standard deviations. Differences in AI literacy scores by academic year and sex were examined using one-way analysis of variance (ANOVA) and independent-sample t-tests, respectively. Differences in AI literacy levels and educational needs according to prior AI education experience were also analyzed using independent sample t-tests. To further examine whether the participants’ background characteristics influenced their perceptions of AI applicability to Korean medicine and the necessity of AI education, cross-tabulation analyses (chi-square tests) were conducted. The following comparisons were made: Differences in AI literacy levels and perceptions across academic years; Differences in perceived applicability and educational needs between high- and low-literacy groups; and Differences in literacy levels and perceptions by sex. The threshold for statistical significance was set at p < 0.05. 3. Results 3.1. General Characteristics of the Participants A total of 304 students from the College of Korean Medicine at Wonkwang University participated in the survey. The sex distribution was nearly balanced, with 154 male (50.7%) and 150 female students (49.3%). The participants included 50 first-year premedical students (16.4%), 52 second-year premedical students (17.1%), 51 first-year medical students (16.8%), 53 second-year medical students (17.4%), 47 third-year medical students (15.5%), and 51 fourth-year medical students (16.8%), indicating a relatively even distribution across academic levels. The mean age was 22.75 years, ranging from 18 to 50 years (Table 1 ). Table 1 General characteristics of the participants. Grade Male (%) Female (%) Total (%) Pre-med 1 29 (9.5) 21 (6.9) 50 (16.4) Pre-med 2 24 (7.9) 28 (9.2) 52 (17.1) Med 1 30 (9.9) 21 (6.9) 51 (16.8) Med 2 22 (7.2) 31 (10.2) 53 (17.4) Med 3 25 (8.2) 22 (7.2) 47 (15.5) Med 4 24 (7.9) 27 (8.9) 51 (16.8) Total 154 (50.7) 150 (49.3) 304 (100) Pre-med: premedical course, Med: medical course 3.2. AI Literacy Competencies and Attitudes 3.2.1. Analysis by the Four Domains of AI Literacy Item-level analysis revealed that scores in the technical understanding domain were generally low, ranging from 2.8 to 3.2 on a five-point scale (Table 2 ). Although the students showed adequate understanding of AI’s basic concepts (Q6, mean = 3.17), their comprehension of AI mechanisms (Q7, mean = 2.90), real-world application (Q10, mean = 2.82), and clinical implementation principles (Q11, mean = 2.80) were limited. In the practical ability domain, digital tool usage (Q4) exhibited a moderate mean score of 3.44. Regarding experience with AI-based services (Q5), 64.8% reported frequent use, 34.5% occasional use, and only 0.7% no experience, suggesting that most participants had some degree of practical exposure to AI applications. The critical thinking domain exhibited relatively high scores, ranging from 3.7 to 4.5. Students expressed strong awareness of potential AI errors (Q15, mean = 4.22) and concerns regarding excessive dependence on AI systems (Q16, 4.46). They also recognized potential inconsistencies between AI judgments and physicians’ clinical decisions (Q21, mean = 3.96). However, trust in AI-based diagnostic results (Q22) was comparatively low (mean = 2.70), indicating a generally cautious attitude toward AI-driven diagnoses. The ethical and social awareness domain yielded the highest scores. Students demonstrated a strong recognition of the importance of data protection (Q24, mean = 4.47) and accountability in AI decision-making (Q25, mean = 4.67), reflecting a high level of ethical sensitivity toward AI utilization in healthcare. 3.2.2. Analysis by the Four Domains of AI Literacy The mean scores across the four AI literacy domains are presented in Table 3 . Technical understanding had the lowest mean score of 2.92, indicating limited comprehension of AI-related principles. Practical ability demonstrated a moderate level, with a mean score of 3.44, while critical thinking exhibited a relatively higher mean score of 3.68. The ethical and social awareness domains had the highest score of 4.57, reflecting the students’ strong sensitivity to ethical considerations and social implications associated with AI use. Table 2 Item level results of AI literacy. Domain Question N Mean SD Notes Technical understanding Q6. Understanding of AI concepts 304 3.17 1.02 - Q7. Understanding of AI principles 304 2.90 1.07 - Q10. Recognition of AI applications 304 2.82 1.11 - Q11. Understanding of AI in medical context 304 2.80 0.96 - Practical ability Q4. Digital tool usage ability 304 3.44 0.97 - Q5. Experience with AI services 304 - - None 0.7%, Light use 34.5%, Frequent use 64.8% Critical thinking Q15. Awareness of AI error possibility 304 4.22 0.59 - Q16. Concern about overreliance on AI 304 4.46 0.66 - Q21. Recognition of conflict with physician’s judgment 304 3.96 0.77 - Q22. Trust in AI diagnosis 304 2.70 1.04 - Ethical/Social awareness Q24. Importance of privacy protection 304 4.47 0.64 - Q25. Responsibility in case of AI misdiagnosis 304 4.67 0.52 - SD: standard deviation, AI: artificial intelligence Table 3 Domain level results of AI literacy. Domain No. of Question N Mean ± SD Technical understanding 4 304 2.92 ± 0.85 Practical ability 2 304 3.44 ± 0.97 Critical thinking 4 304 3.68 ± 0.50 Ethical/Social awareness 2 304 4.57 ± 0.47 SD: standard deviation, AI: artificial intelligence 3.2.3. Analysis of Educational Needs and Experience with AI-Related Courses As shown in Table 4 , students expressed a strong perceived need for AI education, with a mean score of 4.16 for the item “The need for AI education” (Q26). Agreement was particularly high for the statement “Korean medicine doctors should possess the competency to utilize AI technologies” (Q13), which received a mean score of 4.51, with over 96% agreeing or strongly agreeing. Despite this recognition, only 37.2% reported prior experience with AI-related courses (Q27), indicating a substantial gap between perceived necessity and actual exposure. Table 4 Educational needs and related items. Question N Mean ± SD Response distribution (%) Q26. Perceived need for AI education 304 4.16 ± 0.70 - Q13. Physicians should acquire competency in AI usage 304 4.51 ± 0.63 - Q27. Experience with AI-related courses 304 - None 56.3; Yes 37.2; No response 6.5 SD: standard deviation, AI: artificial intelligence 3.3. Perceived Applicability of AI in Korean Medicine As shown in Table 5 , participants demonstrated highly positive perceptions of AI applicability in Korean medicine, with mean scores exceeding 4.0 for all related items and over 85% agreeing or strongly agreeing. Respondents expressed particularly favorable perceptions regarding AI integration in diagnostic (Q17, mean = 4.18) and treatment domains (Q18, mean = 4.17). They also expressed strong expectations that Korean medicine doctors will utilize AI technologies in future clinical practice (Q19, mean = 4.30) and exhibited high readiness to adopt AI-based approaches (Q20, mean = 4.38). Table 5 Perceived possibility of ai integration with Korean medicine. Question N Mean ± SD Q17. AI can support KM diagnosis (e.g. tongue, face image) 304 4.18 ± 0.59 Q18. AI can be applied to KM treatment (e.g. prescriptions, acupoints) 304 4.17 ± 0.76 Q19. AI use by KM doctors will increase in future clinical practice 304 4.30 ± 0.69 Q20. I am prepared to accept AI-assisted KM practice 304 4.38 ± 0.68 SD: standard deviation, AI: artificial intelligence, KM: Korean medicine 3.4. Cross-Tabulation by Academic Year, Gender, AI Literacy Level, and Degree of AI Education 3.4.1. Comparison by Academic Year As shown in Table 6 , technical understanding increased significantly from 2.48 among first-year premedical students to 3.21 among fourth-year medical students (F = 5.23, p = 0.001). Practical ability was highest among first-year medical students (3.78) and lowest among second-year premedical students (3.23), but the difference was not significant (F = 2.11, p = 0.064). Critical thinking and ethical awareness remained consistently high (≥ 3.5 and ≥ 4.5, respectively), with no significant differences observed (F = 1.37, p = 0.242; F = 0.88, p = 0.493). Perceived applicability of AI increased from 4.01 in first-year premedical students to 4.35 in fourth-year medical students (F = 3.56, p = 0.004), while perceived need for AI education remained high across all academic years (approximately 4.0) with no significant difference (F = 0.94, p = 0.452). Table 6 AI literacy and related perceptions by grade. Pre-med 1 Pre-med 2 Med 1 Med 2 Med 3 Med 4 p-value Technical understanding 2.48 2.59 3.09 3.04 3.11 3.21 0.001 *** Practical ability 3.38 3.23 3.78 3.58 3.6 3.55 0.064 Critical thinking 3.55 3.88 3.65 3.74 3.7 3.76 0.242 Ethical/Social awareness 4.5 4.62 4.65 4.59 4.55 4.61 0.493 AI-KM integration 4.01 4.14 4.33 4.23 4.28 4.35 0.004 * AI education need 4.1 3.88 4.16 4.1 4 4.12 0.452 Pre-med: premedical course, Med: medical course, AI: artificial intelligence, KM: Korean medicine, *: p < .05, ***: p < .001 3.4.2. Comparison by AI Literacy Level Overall, AI literacy scores were calculated as the mean of the four domains: technical understanding (Q6, Q7, Q10, and Q11), practical ability (Q4 and Q5), critical thinking (Q15, Q16, Q21, and Q22), and ethical awareness (Q24 and Q25). Based on the composite scores, respondents were categorized into low (≤ 3.44), medium (3.45–3.77), and high (≥ 3.78) literacy groups according to tertile distribution. As shown in Table 7 , perceived AI applicability in Korean medicine increased with literacy level, rising from a mean score of 4.14 in the low-literacy group to 4.46 in the high-literacy group (F = 7.73, p < 0.001). Perceived educational need also increased from 3.99 in the low-literacy group to 4.27 in the high-literacy group (F = 2.68, p = 0.047). Domain-specific analyses revealed significant differences across literacy levels in technical understanding (F = 11.80, p < 0.001), practical ability (F = 3.11, p = 0.027), and critical thinking (F = 4.18, p = 0.006), but not in ethical awareness (F = 1.10, p = 0.35). Table 7 Perceptions by literacy level. Category Low(≤ 3.44) Medium(3.45–3.77) High(≥ 3.78) p-value AI-KM integration 4.14 4.21 4.46 < 0.001 *** Need for AI education 3.99 4.16 4.27 0.047 * AI: artificial intelligence, KM: Korean medicine; *: p < .05, ***: p < .001 3.4.3. Comparison by Gender As shown in Table 8 , independent sample t-tests were conducted to examine gender differences in AI literacy and related perceptions. The findings indicated no significant differences between male and female students in technical understanding (male = 2.91, female = 2.94, t = − 0.31, p = 0.753), practical ability (male = 3.45, female = 3.43, t = 0.19, p = 0.849), critical thinking (male = 3.67, female = 3.68, t = − 0.20, p = 0.844), or ethical awareness (male = 4.57, female = 4.58, t = − 0.22, p = 0.827). Table 8 AI literacy and perceptions by gender. Domain Male (Mean) Female (Mean) p-value Technical understanding 2.91 2.94 0.753 Practical ability 3.45 3.43 0.849 Critical thinking 3.67 3.68 0.844 Ethical/Social awareness 4.57 4.58 0.827 AI: artificial intelligence 3.4.4. Comparison by Experience with AI Education As shown in Table 9 , the overall AI literacy score was significantly higher among students who had prior exposure to AI education (3.81 ± 0.47) compared with those without such experience (3.54 ± 0.39) (t = − 5.21, p < 0.001). This finding indicates that prior exposure to AI education has a positive effect on students’ literacy levels. By contrast, both groups demonstrated very high levels of perceived need for AI education (Q26). The mean score was 4.07 ± 0.74 among students without prior AI education and slightly higher at 4.22 ± 0.78 among those with prior experience; however, the difference was not significant (t = − 1.70, p = 0.091). Table 9 AI education experience and literacy or educational needs. Group N Literacy score (Mean ± SD) p-value Need for AI education (Mean ± SD) p-value No experience 179 3.54 ± 0.39 4.07 ± 0.74 With experience 125 3.81 ± 0.47 < 0.001 *** 4.22 ± 0.78 0.091 ** SD: standard deviation, **: p < .01, ***: p < .001. 3.5. Educational Content and Instructional Methods 3.5.1. Preferred Educational Content As illustrated in Fig. 1 , most respondents (75.1%) selected “practical training using medical AI tools” as their preferred direction for AI education (Q28). This was followed by the “analysis of clinical cases in Korean medicine utilizing AI” (55.7%) as well as the “issues and ethical challenges related to the use of medical AI” (36.9%). Additionally, some respondents expressed a need for basic-level education, including “fundamentals of medical data structures and interpretation” (31.1%) and “basic concepts and principles of AI” (24.9%). Only a very small proportion (0.3%) mentioned “applications of AI in Korean medicine prescriptions.” 3.5.2. Expected Applications of AI in Korean Medicine As illustrated in Fig. 2 , when asked about the expected applications of AI in Korean medicine (Q30), the largest proportion of respondents (51.1%) selected “diagnostic assistance” as the most promising area. This was followed by the “systematization and standardization of Korean medicine through data organization” (20.5%), “analysis of patient conditions and prognosis” (17.0%), and “support for treatment and prescription decisions” (11.4%). Additionally, 9.1% of the respondents provided other opinions, suggesting diverse perspectives on how AI could be integrated into future Korean medical practices. 3.5.3. Educational Operation Methods As illustrated in Fig. 3 , 78 responses were collected regarding preferred educational delivery methods (Q31). Among them, 48.7% suggested introducing AI education at the premedical stage through a stepwise curriculum, beginning with AI foundational concepts and progressing to clinical applications. Conversely, 38.5% preferred focusing on AI education during the medical stage, stating that meaningful learning requires prior knowledge of Korean medical principles. Additionally, 12.8% of the respondents proposed alternatives, such as offering AI as an elective course or integrating it into interdisciplinary curricula. While opinions differed regarding timing and structure, both groups emphasized the importance of linking AI to clinical Korean medicine education. While opinions were divided on the need for introductory AI education at the premedical level, there was consensus that the ability to apply AI clinically should be taught during medical programs. Several participants suggested that AI courses should be offered as electives during the premedical stage and as mandatory components during the medical stage. 3.6. Qualitative Analysis (In-Depth Interviews) 3.6.1. Perceptions and Experiences of AI Most students reported using AI actively in academic and daily contexts. Medical students cited academic purposes, such as preparing assignments, presentations, and exam materials. For instance, one first-year medical student stated, “I used AI for literature searches, summarizing papers, and writing presentation scripts when preparing assignments.” Another student remarked, “When studying herbal pharmacology, I had AI learn my organized notes and then generate quiz questions for practice.” Conversely, the premedical students described simpler and more exploratory uses of AI. They primarily used it for writing assignments, searching for unfamiliar concepts, or asking questions daily (e.g., diet or exercise routines). One first-year premedical student commented, “I use AI when I need to look up things I don’t understand while preparing school assignments.” Another student noted, “I once asked AI about the appropriate amount of protein intake for my workouts.” Overall, as students progressed through their academic years, they tended to use AI in more scholarly and professional contexts, while premedical students primarily used AI as a supplementary search or support tool. 3.6.2. AI Literacy Competencies and Attitudes Students acknowledged AI’s value as an auxiliary tool but emphasized its limitations and the professional responsibility of healthcare providers. Most students agreed that the final decision should be made by a clinician. A second-year medical student stated, “If the diagnosis from AI differs from that of a medical professional, the clinician’s judgment should take precedence because AI cannot take responsibility.” Another student remarked, “The process of identifying why AI made a different decision and comparing it with the clinician’s reasoning is part of a medical professional’s competence.” Concerns regarding the overreliance on AI were also expressed. A third-year medical student commented, “If AI provides incorrect information, I worry that medical professionals might study less,” while a second year student stated, “There’s a risk of believing AI’s hallucinations”. Conversely, premedical students expressed simpler perspectives, such as “It’s right to follow the clinician’s judgment rather than AI,” and “If a clinician can refute AI, they should follow their own reasoning.” However, a few premedical students acknowledged the need for critical use, stating, “We should review AI’s reasoning and compare it with the clinician’s decision to choose the more valid one.” In summary, students attitudes matured with academic progression, shifting toward the perspective that AI should be used as a supportive tool while engaging in evidence comparison and cross-validation during decision-making processes. 3.6.3. Perceived Applicability of AI in Korean Medicine Students provided diverse perspectives on AI’s role in Korean medicine. Medical students emphasized clinical and professional applications such as diagnostic assistance, personalized prescriptions, and identification of herbal drug interactions. For instance, one first-year medical student stated, “AI can analyze extensive herbal and prescription data to suggest the most suitable formula,” while another noted, “AI could help detect adverse interactions among herbal medicines or identify diagnostic errors.” The Premedical students mentioned relatively simple applications. One first-year premedical student said, “Since it’s difficult to remember all the ingredients and effects of herbs, AI could be useful for personalized prescriptions,” and another commented, “AI could help predict treatment outcomes based on learned clinical cases.” Overall, it was observed that medical-course students tended to focus on diagnostic and prescription assistance that considers complex clinical contexts, whereas premedical students primarily focused on simpler aspects such as data processing and prediction. 3.6.4. Educational Content and Instructional Methods Across all academic years, students agreed on the necessity of AI-related education. However, medical students indicated that the current curriculum was insufficient. For instance, some second-year medical students who had taken a newly introduced AI course stated, “There is a need for additional AI education in the curriculum,” while first-year medical students commented, “The current AI education focuses too much on research paper analysis and feels less relevant to clinical applications.” Premedical students with limited prior exposure to AI education generally responded that practice-oriented learning would be the most valuable. One first-year premedical student mentioned, “It would be effective to have practical sessions where we diagnose cases together with AI,” while another noted, “We should study AI first so that we can respond appropriately, whether positively or negatively.” Collectively, the students called for the systematic integration of AI education into the formal curriculum, emphasizing that critical engagement through practice and discussion is more valuable than theoretical instruction alone. 4. Discussion With the growing integration of AI into clinical practice, the need for AI literacy education in medical training has become increasingly evident. However, research on AI literacy in Korean medicine education remains in its infancy, and few studies have examined students’ actual literacy levels and educational needs. To address this gap, this study employed a mixed-methods approach combining quantitative surveys and qualitative interviews with students from a Korean medicine college. Three key findings emerged. First, students with prior AI education demonstrated higher literacy levels and a stronger perceived need for further AI education. In other words, those who received AI training or exhibited higher literacy were more aware of the necessity for AI education in Korean medicine. This finding aligns with prior studies suggesting that enhanced literacy stimulates the demand for more advanced learning [ 12 , 13 ]. Second, clinical experience was identified as an important factor influencing student’s perceptions of AI. As academic levels increased, students showed greater technical understanding and a stronger recognition of AI’s applicability. While premedical students primarily used AI in exploratory ways, medical-course students displayed a more comprehensive understanding that included both clinical and ethical considerations. This suggests that deeper engagement with Korean medical knowledge rather than exposure to AI contributes to the recognition of its applicability. Third, some students perceived AI as a potential tool for integrating and standardizing the dispersed knowledge of Korean medicine. This perspective extends beyond the conventional understanding of AI as merely a clinical assistive tool, commonly emphasized in Western medicine, and highlights its potential contribution to the academic advancement of Korean medicine. This perspective is consistent with prior studies emphasizing AI’s role in the structural organization of medical knowledge. The findings revealed an imbalance across AI literacy domains. While students demonstrated lower levels of technical understanding and practical ability, they demonstrated relatively high levels of critical thinking as well as ethical and social awareness. Qualitative interviews further indicated that students regarded AI as a supportive academic tool but maintained that final clinical decisions should remain under the clinicians authority. Although they acknowledged AI’s potential, they expressed concerns about overreliance and uncritical trust, emphasizing that AI education should strengthen both technical understanding and practical competence while maintaining a balanced awareness of its potential and limitations. Despite their limited exposure to formal AI education, students generally expressed positive attitudes toward integrating AI into Korean medicine, reflecting an awareness of its clinical potential. Medical-course students emphasized clinical applications, whereas premedical students focused on basic functions. Nevertheless, both groups shared the expectation that AI could contribute to the advancement of Korean medicine. Cross-tabulation results showed that technical understanding and perceived applicability increased significantly with academic year. This finding suggests that the deepening of Korean medical knowledge, rather than AI exposure alone, enhances awareness of AI’s relevance. Ethical and social awareness remained consistently high across academic levels, indicating that ethical sensitivity was internalized regardless of academic level. No significant gender differences were observed in any domain, indicating that gender is not a determining factor influencing AI literacy or related perceptions. Students with AI education experience exhibited higher literacy scores and a stronger recognition of both the applicability of AI and the need for AI education. This indicates that even those with prior exposure perceive the current curriculum as insufficient and desire more advanced instruction. The cross-tabulation analysis further highlighted that clinical experience and educational exposure are key factors shaping perceptions of AI. Clinical experience, in particular, appeared to broaden student’s understanding of AI’s potential applications, while those with prior AI training or higher literacy levels expressed a greater demand for additional educational opportunities. Students preferred practice-based and clinically integrated AI education over purely theoretical learning. Diagnostic assistance was identified as the most anticipated area of AI application in Korean medicine, suggesting that AI could serve not only as a clinical support tool, but also as a catalyst for the academic advancement of Korean medicine. Although opinions differed regarding the appropriate timing of AI education, there was a general consensus that clinically focused training should be implemented during the medical course. In summary, Korean medical students strongly expressed the need for practice-oriented and clinically applicable AI education while recognizing AI’s potential role in the systematization and academic development of Korean medicine. Therefore, a stepwise curriculum is recommended, emphasizing foundational concepts, ethics, and critical thinking at the premedical stage, followed by practice-based training during the medical stage. This study makes several contributions to existing literature. First, unlike prior international studies that primarily examined students in Western medical programs, this study is the to target students of Korean medicine, thereby extending the discussion of AI literacy to a new educational context. Second, whereas prior studies often treated literacy as a single construct, this study distinguished between the premedical and medical stages, identifying differences by academic level and clinical experience. Third, while most Western studies perceive AI primarily as a clinically assistive tool, the students of Korean medicine in this study perceived AI as a means of knowledge integration, standardization, and academic advancement, offering a unique perspective. Finally, by combining quantitative surveys with qualitative interviews, this study contextualizes students’ perceptions using a mixed-methods approach, providing foundational data for the future development of AI curricula in Korean medical education. This study had several limitations. The sample was restricted to students from a single college of Korean medicine, which limits the generalizability of the findings. Self-reported data and non-standardized questionnaire items may also constrain the validity of the measured competencies. Moreover, the cross-sectional design prevents a clear determination of causal relationships between AI education experience and literacy levels, and the limited number of participants may not fully capture diverse perspectives across academic years and groups. Future studies should expand to multi-institutional and national levels, as well as adopt a longitudinal design to clarify the causal effects of AI education on literacy development. Given the ongoing absence of internationally standardized curricula and guidelines, future research should employ validated instruments or develop new tools that reflect the unique characteristics of Korean medicine to ensure more objective and reliable assessments [ 14 ]. In educational practice, enhancing instructors’ AI competencies is essential for meeting students’ needs and shifting AI education from a simple knowledge transfer to a process of mutual learning and professional growth. Furthermore, medical education should be designed to encompass not only technical understanding, but also ethical sensitivity, empathy, and patient-centered values. 5. Conclusions This study represents the first mixed-methods investigation to analyze AI literacy levels and educational needs among Korean medical students. The findings revealed an imbalance across literacy domains; while students demonstrated relatively low levels of technical understanding and practical ability, their ethical and social awareness levels were comparatively high. Differences in perceptions were observed across academic year and according to prior AI education experience, indicating that both clinical exposure and educational experience play critical roles in deepening students’ understanding of AI. Students preferred practice-based and clinically integrated education over concept-oriented learning. Some also expressed expectations that AI could contribute to the integration and standardization of Korean medical knowledge, reflecting an expanded perception of AI beyond its technological utility. These findings suggest that AI education in Korean medicine should not only emphasize technical proficiency, but also aim to enhance clinical competence and foster academic advancement. Accordingly, a stepwise curriculum model is recommended: the premedical stage should emphasize foundational concepts, ethics, and critical thinking, whereas the medical stage should focus on clinical practice and application. Additionally, faculty capacity building and the cultivation of humanistic competencies should be pursued in concurrently. This study provides foundational evidence for the effective integration of AI into Korean medical education. Future research should validate the proposed educational model and establish institutional frameworks to support the systematic implementation of AI literacy education in Korean medicine. Abbreviations The following abbreviations are used in this manuscript AI Aretificial Intelligence IRB Institutional Review Board WKUJIM Wonkwang University Jangheung Integrative Medical Hospital Declarations Ethical approval and consent to participate : This study was conducted in accordance with the Declaration of Helsinki. Approval was obtained from the Institutional Review Board of Wonkwang University Jangheung Integrative Medical Hospital (WKUJIM-IRB-202507-003; July 25, 2025). Written informed consent was obtained from all participants prior to participation. Consent for publication: Not applicable. Availability of data and materials : The data presented in this study are available upon reasonable request from the corresponding author. Competing Interests: The authors declare no conflicts of interest. Funding : This study received no external funding. Authors’ Contributions : Conceptualization, S.B., J.L.; methodology, W.H., S.J., J.L.; software, S.J., W.H; validation, S.J., H.W. and J.L.; formal analysis, S.B.; investigation, S.B., H.W. and J.L.; resources, J.L.; data curation, S.B., S.J.; writing—original draft preparation, S.B.; writing—review and editing, S.J., W.H. and J.L.; visualization, S.B., H.W.; supervision, S.J., W.H. and J.L.; project administration, S.J., W.H. and J.L.; All authors have read and agreed to the published version of the manuscript. Acknowledgements : Not applicable. References Cox A. Algorithmic literacy, AI literacy and responsible generative AI literacy. J Web Librariansh. 2024;18:93–110. Koch M, Wienrich C, Straka S, Latoschik M, Carolus A. Overview and confirmatory and exploratory factor analysis of AI literacy scale. Comput Educ Artif Intell. 2024;7:100310. Kim S, Kim T, Kim K. Development and effectiveness verification of AI education data sets based on constructivist learning principles for enhancing AI literacy. Sci Rep. 2025;15:10725. Yang J, Woo J, Shin D, Park S, Kwon Y. Study on the Perception and Application of AI in Korean Medicine through Practice and Questionnaire of Korean Medicine Using a Diagnostic Expert System. Korean J Orient Physiol Pathol. 2021;35:22–7. Bae H, Park S, Kim C. A practical guide to implementing artificial intelligence in traditional East Asian medicine research. Integr Med Res. 2024;13:101067. Kim J, Kim S, Kim E, Sim J, Lee Y, Kim H. Developing a framework for self-regulatory governance in healthcare AI research: insights from South Korea. Asian Bioeth Rev. 2024;16:391–406. Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5:e16048. Kim S, Kim S, Kim H, Lee Y. Integrating artificial intelligence into medical curricula: perspectives of faculty and students in South Korea. Korean J Med Educ. 2025;37:65–70. Park Y, Lee K, Jeong H, Kim K. The necessity of education in response to technological advancements and future environmental changes: a comparison of Korean medicine doctors and students. J Korean Med. 2023;44:59–67. Varma J, Fernando S, Ting B, Aamir S, Sivaprakasam R. The global use of artificial intelligence in the undergraduate medical curriculum: a systematic review. Cureus. 2023;15:e39701. Laupichler M, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Med Educ. 2024;24:401. Lee J, Wu A, Li D, Kulasegaram K. Artificial intelligence in undergraduate medical education: a scoping review. Acad Med. 2021;96(Suppl 1):S62–70. Ma Y, Song Y, Balch J, Ren Y, Vellanki D, Hu Z et al. Promoting AI competencies for medical students: a scoping review on frameworks, programs, and tools; 2024. arXiv [Preprint]. https://arxiv.org/abs/2407.18939 . Accessed 13 October 2025. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu N, Bartlett R, et al. A scoping review of artificial intelligence in medical education: BEME Guide 84. Med Teach. 2024;46:446–70. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx AppendixB.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8525533","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593594800,"identity":"cb7f0a3e-6185-4d4a-b239-c250143a86da","order_by":0,"name":"Seonghoon Bae","email":"","orcid":"","institution":"Chuna Manual Medicine Research Group","correspondingAuthor":false,"prefix":"","firstName":"Seonghoon","middleName":"","lastName":"Bae","suffix":""},{"id":593594801,"identity":"bf8b8cfa-4082-4bd5-8f3e-f65709fde08d","order_by":1,"name":"Seojae Jeon","email":"","orcid":"","institution":"Jeonbuk Advanced Bio-Convergence Academy, Wonkwang University","correspondingAuthor":false,"prefix":"","firstName":"Seojae","middleName":"","lastName":"Jeon","suffix":""},{"id":593594802,"identity":"6a49928f-36fe-407f-b47e-0b0e8a0cedd4","order_by":2,"name":"Wonbae Ha","email":"","orcid":"","institution":"Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang Univers","correspondingAuthor":false,"prefix":"","firstName":"Wonbae","middleName":"","lastName":"Ha","suffix":""},{"id":593594803,"identity":"3cfd2d32-f986-435a-94fc-457514829292","order_by":3,"name":"Junghan Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACZuYGECXHwAPhGxChhRGsxZgELQwQLYkNRGvRbWdse/Djj036hjNnDBh+1DAYmzcQ0GJ2mLHdsLctLXfD2R4Dxp5jDGYyBwhraZPgbTicu+08jwEDbwODjQQhh4G0SP75czjdDKiF8S+xWqR52A4nmAEdxgy0xYwYLe3Gsm1phvvPHCs4LHNMwpiwlvOHjz1888dGXrIneePDNzU2hjMIaQECNjjrAAMDQTvQtIyCUTAKRsEowAoAkhU7tiwNi9kAAAAASUVORK5CYII=","orcid":"","institution":"Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang Univers","correspondingAuthor":true,"prefix":"","firstName":"Junghan","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-01-06 01:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8525533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8525533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103217709,"identity":"f28daf44-7028-487d-a7ec-cf3a16e64943","added_by":"auto","created_at":"2026-02-23 09:48:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18565,"visible":true,"origin":"","legend":"\u003cp\u003ePreferred directions and expectations for AI education.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/53f92566ab881a5d6e454132.png"},{"id":103505648,"identity":"422d7eaa-661f-4f88-812b-68a5c3e88daf","added_by":"auto","created_at":"2026-02-26 13:32:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15009,"visible":true,"origin":"","legend":"\u003cp\u003eExpected applications of AI.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/6768671970d89767729577fb.png"},{"id":103217711,"identity":"18702827-076b-40b1-b3c8-26b7423fb749","added_by":"auto","created_at":"2026-02-23 09:48:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14692,"visible":true,"origin":"","legend":"\u003cp\u003eSuggested timing for AI education implementation\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/dc8f60d5c7c6887b22d98d74.png"},{"id":103509424,"identity":"f1664f98-2e32-4649-9097-52368831ac25","added_by":"auto","created_at":"2026-02-26 13:58:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304831,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/38d4de95-3493-44bb-b7d1-8538816e9d5e.pdf"},{"id":103217710,"identity":"4daf8b24-2017-4894-bdc2-e7e535c039e8","added_by":"auto","created_at":"2026-02-23 09:48:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28892,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/22b6cae7892eb57ea184b856.docx"},{"id":103217713,"identity":"d74de508-87a8-47e9-bcc1-6b39e1e6d5dd","added_by":"auto","created_at":"2026-02-23 09:48:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27031,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8525533/v1/b303011f74e0bd57ac06d928.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence Literacy and Educational Needs among Students at a Korean Medicine College: A Mixed-Methods Study","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eArtificial intelligence (AI) holds substantial potential for application across a wide range of medical domains, including image interpretation, data analysis, treatment planning, and prognosis prediction. Amidst these transformations, AI literacy has emerged as a core competency for future healthcare professionals. As the use of AI in clinical practice continues to expand, the importance of AI literacy in medical education has become increasingly evident. This emphasis extends beyond the acquisition of technological skills; it represents an essential prerequisite for healthcare professionals to safely and responsibly utilize AI.\u003c/p\u003e\u003cp\u003eAI literacy encompasses a comprehensive set of competencies that extend beyond operational skills. It involves understanding the fundamental concepts and mechanisms of AI, developing practical application abilities, cultivating critical evaluation skills, and recognizing the ethical and social implications of AI [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Internationally, four key dimensions have been proposed as frameworks for AI literacy: (1) technical understanding, (2) practical ability, (3) critical thinking, and (4) ethical and social awareness [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In this study, a fifth domain, educational needs, was included as an independent analytical component. Although not a direct indicator of literacy competence, educational needs have been consistently emphasized in national healthcare education policies and serve as important indicators of students\u0026rsquo; perceived learning requirements [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the field of Korean medicine, AI applications have gradually begun to emerge. Recent domestic and international studies have reported various initiatives, including the development of predictive models for acupuncture treatment response [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the introduction of clinical education programs using large language models such as ChatGPT [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the construction of diagnostic support algorithms based on electronic medical records (EMR) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These studies demonstrate the feasibility and potential of AI implementation in Korean medicine.\u003c/p\u003e\u003cp\u003eAs the need for AI education in healthcare becomes widely recognized, diverse efforts have been made to integrate AI-related content into medical curricula. However, the content and depth of such education vary significantly across countries as well as institutions, and standardized curricula and guidelines remain limited [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although discussions on AI education have begun in the context of Korean medicine, practical implementation remains limited. Most prior studies were conducted among Western medical students, and research involving Korean medical students remains scarce [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to evaluate the level of AI literacy among students enrolled in colleges of Korean medicine and to analyze their educational needs and preferred instructional formats. The objective was to provide foundational data to support the effective integration of AI literacy into Korean medical curricula. By examining students\u0026rsquo; perceptions of necessary educational content and appropriate curriculum structures, this study seeks to contribute to establishing clear directions and institutional foundations for implementing AI literacy education, specifically regarding timing, content design, and priority setting, within Korean medical education.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study was approved by the Institutional Review Board of Jangheung Integrative Medical Hospital, Wonkwang University (IRB No. WKUJIM-202507-003). The target population comprised 562 students enrolled in the College of Korean Medicine at [University Name]. A mixed-methods design was employed, integrating both quantitative surveys and qualitative interviews to comprehensively explore participants\u0026rsquo; AI literacy levels and educational needs.\u003c/p\u003e \u003cp\u003eA structured questionnaire was developed using an online survey platform (Google Forms). The survey link was distributed to student groups by academic year and participation was voluntary. Quantitative data were collected between August 4 to August 25, 2025.\u003c/p\u003e \u003cp\u003eFor the qualitative component, semi-structured, and in-depth interviews were conducted using an online videoconferencing platform (Zoom). Thirty participants were randomly selected, comprising five students from each academic year. Participants were selected without considering specific demographic factors such as gender, academic performance, or interest in AI to ensure a balanced representation of experiences and perceptions across academic levels.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Survey Contents\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe questionnaire items were developed by the researchers based on prior studies, with modifications to reflect the unique characteristics and educational needs of Korean medical education [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The final questionnaire included 31 items assessing AI literacy competencies, attitudes, general characteristics, perceptions of AI applicability in Korean medicine, and educational needs as well as preferences. All items were rated on a five-point Likert scale, with some questions designed as open-ended questions to obtain more detailed insights.\u003c/p\u003e \u003cp\u003eAI literacy was measured across four internationally recognized domains: (1) technical understanding, (2) practical ability, (3) critical thinking, and (4) ethical and social awareness. An additional domain, educational needs, was included to assess students\u0026rsquo; perceived demands for AI-related learning opportunities. For the qualitative component, thematic analysis was conducted on semi-structured interview data to identify key categories and themes. Interview questions focused on participants\u0026rsquo; experiences with AI, understanding of AI literacy competencies, perceptions of AI\u0026ndash;Korean medicine integration, and opinions on the appropriate timing and methods for AI education.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll survey data were analyzed using SPSS Statistics version 28.0 (IBM Corp., Armonk, NY, USA). Participants\u0026rsquo; general characteristics were summarized using frequencies and percentages, and AI literacy scores for each domain were presented as means and standard deviations.\u003c/p\u003e \u003cp\u003eDifferences in AI literacy scores by academic year and sex were examined using one-way analysis of variance (ANOVA) and independent-sample t-tests, respectively. Differences in AI literacy levels and educational needs according to prior AI education experience were also analyzed using independent sample t-tests.\u003c/p\u003e \u003cp\u003eTo further examine whether the participants\u0026rsquo; background characteristics influenced their perceptions of AI applicability to Korean medicine and the necessity of AI education, cross-tabulation analyses (chi-square tests) were conducted. The following comparisons were made:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferences in AI literacy levels and perceptions across academic years;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferences in perceived applicability and educational needs between high- and low-literacy groups; and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferences in literacy levels and perceptions by sex.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe threshold for statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. General Characteristics of the Participants\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e A total of 304 students from the College of Korean Medicine at Wonkwang University participated in the survey. The sex distribution was nearly balanced, with 154 male (50.7%) and 150 female students (49.3%).\u003c/p\u003e\u003cp\u003eThe participants included 50 first-year premedical students (16.4%), 52 second-year premedical students (17.1%), 51 first-year medical students (16.8%), 53 second-year medical students (17.4%), 47 third-year medical students (15.5%), and 51 fourth-year medical students (16.8%), indicating a relatively even distribution across academic levels.\u003c/p\u003e\u003cp\u003eThe mean age was 22.75 years, ranging from 18 to 50 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-med 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-med 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (16.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (16.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePre-med: premedical course, Med: medical course\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AI Literacy Competencies and Attitudes\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Analysis by the Four Domains of AI Literacy\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eItem-level analysis revealed that scores in the technical understanding domain were generally low, ranging from 2.8 to 3.2 on a five-point scale (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although the students showed adequate understanding of AI\u0026rsquo;s basic concepts (Q6, mean\u0026thinsp;=\u0026thinsp;3.17), their comprehension of AI mechanisms (Q7, mean\u0026thinsp;=\u0026thinsp;2.90), real-world application (Q10, mean\u0026thinsp;=\u0026thinsp;2.82), and clinical implementation principles (Q11, mean\u0026thinsp;=\u0026thinsp;2.80) were limited.\u003c/p\u003e\u003cp\u003eIn the practical ability domain, digital tool usage (Q4) exhibited a moderate mean score of 3.44. Regarding experience with AI-based services (Q5), 64.8% reported frequent use, 34.5% occasional use, and only 0.7% no experience, suggesting that most participants had some degree of practical exposure to AI applications.\u003c/p\u003e\u003cp\u003eThe critical thinking domain exhibited relatively high scores, ranging from 3.7 to 4.5. Students expressed strong awareness of potential AI errors (Q15, mean\u0026thinsp;=\u0026thinsp;4.22) and concerns regarding excessive dependence on AI systems (Q16, 4.46). They also recognized potential inconsistencies between AI judgments and physicians\u0026rsquo; clinical decisions (Q21, mean\u0026thinsp;=\u0026thinsp;3.96). However, trust in AI-based diagnostic results (Q22) was comparatively low (mean\u0026thinsp;=\u0026thinsp;2.70), indicating a generally cautious attitude toward AI-driven diagnoses.\u003c/p\u003e\u003cp\u003e The ethical and social awareness domain yielded the highest scores. Students demonstrated a strong recognition of the importance of data protection (Q24, mean\u0026thinsp;=\u0026thinsp;4.47) and accountability in AI decision-making (Q25, mean\u0026thinsp;=\u0026thinsp;4.67), reflecting a high level of ethical sensitivity toward AI utilization in healthcare.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Analysis by the Four Domains of AI Literacy\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe mean scores across the four AI literacy domains are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Technical understanding had the lowest mean score of 2.92, indicating limited comprehension of AI-related principles. Practical ability demonstrated a moderate level, with a mean score of 3.44, while critical thinking exhibited a relatively higher mean score of 3.68. The ethical and social awareness domains had the highest score of 4.57, reflecting the students\u0026rsquo; strong sensitivity to ethical considerations and social implications associated with AI use.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem level results of AI literacy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical\u003c/p\u003e \u003cp\u003eunderstanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ6. Understanding of AI concepts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ7. Understanding of AI principles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ10. Recognition of AI applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ11. Understanding of AI in medical context\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4. Digital tool usage ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ5. Experience with AI services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone 0.7%,\u003c/p\u003e \u003cp\u003eLight use 34.5%, Frequent use 64.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ15. Awareness of AI error possibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ16. Concern about overreliance on AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ21. Recognition of conflict with physician\u0026rsquo;s judgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ22. Trust in AI diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical/Social awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ24. Importance of privacy protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ25. Responsibility in case of AI misdiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD: standard deviation, AI: artificial intelligence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDomain level results of AI literacy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical/Social awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSD: standard deviation, AI: artificial intelligence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Analysis of Educational Needs and Experience with AI-Related Courses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, students expressed a strong perceived need for AI education, with a mean score of 4.16 for the item \u0026ldquo;The need for AI education\u0026rdquo; (Q26). Agreement was particularly high for the statement \u0026ldquo;Korean medicine doctors should possess the competency to utilize AI technologies\u0026rdquo; (Q13), which received a mean score of 4.51, with over 96% agreeing or strongly agreeing.\u003c/p\u003e \u003cp\u003eDespite this recognition, only 37.2% reported prior experience with AI-related courses (Q27), indicating a substantial gap between perceived necessity and actual exposure.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEducational needs and related items.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResponse distribution (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ26. Perceived need for AI education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ13. Physicians should acquire competency in AI usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ27. Experience with AI-related courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone 56.3; Yes 37.2; No response 6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSD: standard deviation, AI: artificial intelligence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Perceived Applicability of AI in Korean Medicine\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, participants demonstrated highly positive perceptions of AI applicability in Korean medicine, with mean scores exceeding 4.0 for all related items and over 85% agreeing or strongly agreeing.\u003c/p\u003e \u003cp\u003eRespondents expressed particularly favorable perceptions regarding AI integration in diagnostic (Q17, mean\u0026thinsp;=\u0026thinsp;4.18) and treatment domains (Q18, mean\u0026thinsp;=\u0026thinsp;4.17). They also expressed strong expectations that Korean medicine doctors will utilize AI technologies in future clinical practice (Q19, mean\u0026thinsp;=\u0026thinsp;4.30) and exhibited high readiness to adopt AI-based approaches (Q20, mean\u0026thinsp;=\u0026thinsp;4.38).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceived possibility of ai integration with Korean medicine.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ17. AI can support KM diagnosis (e.g. tongue, face image)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ18. AI can be applied to KM treatment (e.g. prescriptions, acupoints)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ19. AI use by KM doctors will increase in future clinical practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ20. I am prepared to accept AI-assisted KM practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSD: standard deviation, AI: artificial intelligence, KM: Korean medicine\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Cross-Tabulation by Academic Year, Gender, AI Literacy Level, and Degree of AI Education\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Comparison by Academic Year\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, technical understanding increased significantly from 2.48 among first-year premedical students to 3.21 among fourth-year medical students (F\u0026thinsp;=\u0026thinsp;5.23, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003ePractical ability was highest among first-year medical students (3.78) and lowest among second-year premedical students (3.23), but the difference was not significant (F\u0026thinsp;=\u0026thinsp;2.11, p\u0026thinsp;=\u0026thinsp;0.064). Critical thinking and ethical awareness remained consistently high (\u0026ge;\u0026thinsp;3.5 and \u0026ge;\u0026thinsp;4.5, respectively), with no significant differences observed (F\u0026thinsp;=\u0026thinsp;1.37, p\u0026thinsp;=\u0026thinsp;0.242; F\u0026thinsp;=\u0026thinsp;0.88, p\u0026thinsp;=\u0026thinsp;0.493).\u003c/p\u003e \u003cp\u003ePerceived applicability of AI increased from 4.01 in first-year premedical students to 4.35 in fourth-year medical students (F\u0026thinsp;=\u0026thinsp;3.56, p\u0026thinsp;=\u0026thinsp;0.004), while perceived need for AI education remained high across all academic years (approximately 4.0) with no significant difference (F\u0026thinsp;=\u0026thinsp;0.94, p\u0026thinsp;=\u0026thinsp;0.452).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI literacy and related perceptions by grade.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-med 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-med 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMed 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMed 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMed 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical/Social awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI-KM integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI education need\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ePre-med: premedical course, Med: medical course, AI: artificial intelligence, KM: Korean medicine, *: p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Comparison by AI Literacy Level\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, AI literacy scores were calculated as the mean of the four domains: technical understanding (Q6, Q7, Q10, and Q11), practical ability (Q4 and Q5), critical thinking (Q15, Q16, Q21, and Q22), and ethical awareness (Q24 and Q25). Based on the composite scores, respondents were categorized into low (\u0026le;\u0026thinsp;3.44), medium (3.45\u0026ndash;3.77), and high (\u0026ge;\u0026thinsp;3.78) literacy groups according to tertile distribution.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, perceived AI applicability in Korean medicine increased with literacy level, rising from a mean score of 4.14 in the low-literacy group to 4.46 in the high-literacy group (F\u0026thinsp;=\u0026thinsp;7.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Perceived educational need also increased from 3.99 in the low-literacy group to 4.27 in the high-literacy group (F\u0026thinsp;=\u0026thinsp;2.68, p\u0026thinsp;=\u0026thinsp;0.047).\u003c/p\u003e \u003cp\u003eDomain-specific analyses revealed significant differences across literacy levels in technical understanding (F\u0026thinsp;=\u0026thinsp;11.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), practical ability (F\u0026thinsp;=\u0026thinsp;3.11, p\u0026thinsp;=\u0026thinsp;0.027), and critical thinking (F\u0026thinsp;=\u0026thinsp;4.18, p\u0026thinsp;=\u0026thinsp;0.006), but not in ethical awareness (F\u0026thinsp;=\u0026thinsp;1.10, p\u0026thinsp;=\u0026thinsp;0.35).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceptions by literacy level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow(\u0026le;\u0026thinsp;3.44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium(3.45\u0026ndash;3.77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh(\u0026ge;\u0026thinsp;3.78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI-KM integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeed for AI education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAI: artificial intelligence, KM: Korean medicine;\u003c/p\u003e \u003cp\u003e*: p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Comparison by Gender\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, independent sample t-tests were conducted to examine gender differences in AI literacy and related perceptions. The findings indicated no significant differences between male and female students in technical understanding (male\u0026thinsp;=\u0026thinsp;2.91, female\u0026thinsp;=\u0026thinsp;2.94, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.753), practical ability (male\u0026thinsp;=\u0026thinsp;3.45, female\u0026thinsp;=\u0026thinsp;3.43, t\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.849), critical thinking (male\u0026thinsp;=\u0026thinsp;3.67, female\u0026thinsp;=\u0026thinsp;3.68, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.844), or ethical awareness (male\u0026thinsp;=\u0026thinsp;4.57, female\u0026thinsp;=\u0026thinsp;4.58, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.827).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI literacy and perceptions by gender.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (Mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (Mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical/Social awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAI: artificial intelligence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4. Comparison by Experience with AI Education\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the overall AI literacy score was significantly higher among students who had prior exposure to AI education (3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47) compared with those without such experience (3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39) (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding indicates that prior exposure to AI education has a positive effect on students\u0026rsquo; literacy levels.\u003c/p\u003e \u003cp\u003eBy contrast, both groups demonstrated very high levels of perceived need for AI education (Q26). The mean score was 4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 among students without prior AI education and slightly higher at 4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 among those with prior experience; however, the difference was not significant (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.70, p\u0026thinsp;=\u0026thinsp;0.091).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI education experience and literacy or educational needs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiteracy score (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeed for AI education (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD: standard deviation, **: p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Educational Content and Instructional Methods\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1. Preferred Educational Content\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, most respondents (75.1%) selected \u0026ldquo;practical training using medical AI tools\u0026rdquo; as their preferred direction for AI education (Q28). This was followed by the \u0026ldquo;analysis of clinical cases in Korean medicine utilizing AI\u0026rdquo; (55.7%) as well as the \u0026ldquo;issues and ethical challenges related to the use of medical AI\u0026rdquo; (36.9%).\u003c/p\u003e\u003cp\u003eAdditionally, some respondents expressed a need for basic-level education, including \u0026ldquo;fundamentals of medical data structures and interpretation\u0026rdquo; (31.1%) and \u0026ldquo;basic concepts and principles of AI\u0026rdquo; (24.9%). Only a very small proportion (0.3%) mentioned \u0026ldquo;applications of AI in Korean medicine prescriptions.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2. Expected Applications of AI in Korean Medicine\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, when asked about the expected applications of AI in Korean medicine (Q30), the largest proportion of respondents (51.1%) selected \u0026ldquo;diagnostic assistance\u0026rdquo; as the most promising area. This was followed by the \u0026ldquo;systematization and standardization of Korean medicine through data organization\u0026rdquo; (20.5%), \u0026ldquo;analysis of patient conditions and prognosis\u0026rdquo; (17.0%), and \u0026ldquo;support for treatment and prescription decisions\u0026rdquo; (11.4%). Additionally, 9.1% of the respondents provided other opinions, suggesting diverse perspectives on how AI could be integrated into future Korean medical practices.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3. Educational Operation Methods\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 78 responses were collected regarding preferred educational delivery methods (Q31). Among them, 48.7% suggested introducing AI education at the premedical stage through a stepwise curriculum, beginning with AI foundational concepts and progressing to clinical applications.\u003c/p\u003e \u003cp\u003eConversely, 38.5% preferred focusing on AI education during the medical stage, stating that meaningful learning requires prior knowledge of Korean medical principles.\u003c/p\u003e \u003cp\u003eAdditionally, 12.8% of the respondents proposed alternatives, such as offering AI as an elective course or integrating it into interdisciplinary curricula. While opinions differed regarding timing and structure, both groups emphasized the importance of linking AI to clinical Korean medicine education. While opinions were divided on the need for introductory AI education at the premedical level, there was consensus that the ability to apply AI clinically should be taught during medical programs. Several participants suggested that AI courses should be offered as electives during the premedical stage and as mandatory components during the medical stage.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Qualitative Analysis (In-Depth Interviews)\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. Perceptions and Experiences of AI\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMost students reported using AI actively in academic and daily contexts. Medical students cited academic purposes, such as preparing assignments, presentations, and exam materials. For instance, one first-year medical student stated, \u0026ldquo;I used AI for literature searches, summarizing papers, and writing presentation scripts when preparing assignments.\u0026rdquo; Another student remarked, \u0026ldquo;When studying herbal pharmacology, I had AI learn my organized notes and then generate quiz questions for practice.\u0026rdquo;\u003c/p\u003e \u003cp\u003eConversely, the premedical students described simpler and more exploratory uses of AI. They primarily used it for writing assignments, searching for unfamiliar concepts, or asking questions daily (e.g., diet or exercise routines). One first-year premedical student commented, \u0026ldquo;I use AI when I need to look up things I don\u0026rsquo;t understand while preparing school assignments.\u0026rdquo; Another student noted, \u0026ldquo;I once asked AI about the appropriate amount of protein intake for my workouts.\u0026rdquo;\u003c/p\u003e \u003cp\u003eOverall, as students progressed through their academic years, they tended to use AI in more scholarly and professional contexts, while premedical students primarily used AI as a supplementary search or support tool.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. AI Literacy Competencies and Attitudes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStudents acknowledged AI\u0026rsquo;s value as an auxiliary tool but emphasized its limitations and the professional responsibility of healthcare providers. Most students agreed that the final decision should be made by a clinician. A second-year medical student stated, \u0026ldquo;If the diagnosis from AI differs from that of a medical professional, the clinician\u0026rsquo;s judgment should take precedence because AI cannot take responsibility.\u0026rdquo; Another student remarked, \u0026ldquo;The process of identifying why AI made a different decision and comparing it with the clinician\u0026rsquo;s reasoning is part of a medical professional\u0026rsquo;s competence.\u0026rdquo;\u003c/p\u003e \u003cp\u003eConcerns regarding the overreliance on AI were also expressed. A third-year medical student commented, \u0026ldquo;If AI provides incorrect information, I worry that medical professionals might study less,\u0026rdquo; while a second year student stated, \u0026ldquo;There\u0026rsquo;s a risk of believing AI\u0026rsquo;s hallucinations\u0026rdquo;.\u003c/p\u003e \u003cp\u003eConversely, premedical students expressed simpler perspectives, such as \u0026ldquo;It\u0026rsquo;s right to follow the clinician\u0026rsquo;s judgment rather than AI,\u0026rdquo; and \u0026ldquo;If a clinician can refute AI, they should follow their own reasoning.\u0026rdquo; However, a few premedical students acknowledged the need for critical use, stating, \u0026ldquo;We should review AI\u0026rsquo;s reasoning and compare it with the clinician\u0026rsquo;s decision to choose the more valid one.\u0026rdquo;\u003c/p\u003e \u003cp\u003eIn summary, students attitudes matured with academic progression, shifting toward the perspective that AI should be used as a supportive tool while engaging in evidence comparison and cross-validation during decision-making processes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3. Perceived Applicability of AI in Korean Medicine\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStudents provided diverse perspectives on AI\u0026rsquo;s role in Korean medicine. Medical students emphasized clinical and professional applications such as diagnostic assistance, personalized prescriptions, and identification of herbal drug interactions. For instance, one first-year medical student stated, \u0026ldquo;AI can analyze extensive herbal and prescription data to suggest the most suitable formula,\u0026rdquo; while another noted, \u0026ldquo;AI could help detect adverse interactions among herbal medicines or identify diagnostic errors.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe Premedical students mentioned relatively simple applications. One first-year premedical student said, \u0026ldquo;Since it\u0026rsquo;s difficult to remember all the ingredients and effects of herbs, AI could be useful for personalized prescriptions,\u0026rdquo; and another commented, \u0026ldquo;AI could help predict treatment outcomes based on learned clinical cases.\u0026rdquo;\u003c/p\u003e \u003cp\u003eOverall, it was observed that medical-course students tended to focus on diagnostic and prescription assistance that considers complex clinical contexts, whereas premedical students primarily focused on simpler aspects such as data processing and prediction.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4. Educational Content and Instructional Methods\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAcross all academic years, students agreed on the necessity of AI-related education. However, medical students indicated that the current curriculum was insufficient. For instance, some second-year medical students who had taken a newly introduced AI course stated, \u0026ldquo;There is a need for additional AI education in the curriculum,\u0026rdquo; while first-year medical students commented, \u0026ldquo;The current AI education focuses too much on research paper analysis and feels less relevant to clinical applications.\u0026rdquo;\u003c/p\u003e \u003cp\u003ePremedical students with limited prior exposure to AI education generally responded that practice-oriented learning would be the most valuable. One first-year premedical student mentioned, \u0026ldquo;It would be effective to have practical sessions where we diagnose cases together with AI,\u0026rdquo; while another noted, \u0026ldquo;We should study AI first so that we can respond appropriately, whether positively or negatively.\u0026rdquo;\u003c/p\u003e \u003cp\u003eCollectively, the students called for the systematic integration of AI education into the formal curriculum, emphasizing that critical engagement through practice and discussion is more valuable than theoretical instruction alone.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWith the growing integration of AI into clinical practice, the need for AI literacy education in medical training has become increasingly evident. However, research on AI literacy in Korean medicine education remains in its infancy, and few studies have examined students\u0026rsquo; actual literacy levels and educational needs. To address this gap, this study employed a mixed-methods approach combining quantitative surveys and qualitative interviews with students from a Korean medicine college. Three key findings emerged.\u003c/p\u003e\u003cp\u003eFirst, students with prior AI education demonstrated higher literacy levels and a stronger perceived need for further AI education. In other words, those who received AI training or exhibited higher literacy were more aware of the necessity for AI education in Korean medicine. This finding aligns with prior studies suggesting that enhanced literacy stimulates the demand for more advanced learning [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, clinical experience was identified as an important factor influencing student\u0026rsquo;s perceptions of AI. As academic levels increased, students showed greater technical understanding and a stronger recognition of AI\u0026rsquo;s applicability. While premedical students primarily used AI in exploratory ways, medical-course students displayed a more comprehensive understanding that included both clinical and ethical considerations. This suggests that deeper engagement with Korean medical knowledge rather than exposure to AI contributes to the recognition of its applicability.\u003c/p\u003e\u003cp\u003eThird, some students perceived AI as a potential tool for integrating and standardizing the dispersed knowledge of Korean medicine. This perspective extends beyond the conventional understanding of AI as merely a clinical assistive tool, commonly emphasized in Western medicine, and highlights its potential contribution to the academic advancement of Korean medicine. This perspective is consistent with prior studies emphasizing AI\u0026rsquo;s role in the structural organization of medical knowledge.\u003c/p\u003e\u003cp\u003eThe findings revealed an imbalance across AI literacy domains. While students demonstrated lower levels of technical understanding and practical ability, they demonstrated relatively high levels of critical thinking as well as ethical and social awareness. Qualitative interviews further indicated that students regarded AI as a supportive academic tool but maintained that final clinical decisions should remain under the clinicians authority. Although they acknowledged AI\u0026rsquo;s potential, they expressed concerns about overreliance and uncritical trust, emphasizing that AI education should strengthen both technical understanding and practical competence while maintaining a balanced awareness of its potential and limitations.\u003c/p\u003e\u003cp\u003eDespite their limited exposure to formal AI education, students generally expressed positive attitudes toward integrating AI into Korean medicine, reflecting an awareness of its clinical potential. Medical-course students emphasized clinical applications, whereas premedical students focused on basic functions. Nevertheless, both groups shared the expectation that AI could contribute to the advancement of Korean medicine. Cross-tabulation results showed that technical understanding and perceived applicability increased significantly with academic year. This finding suggests that the deepening of Korean medical knowledge, rather than AI exposure alone, enhances awareness of AI\u0026rsquo;s relevance. Ethical and social awareness remained consistently high across academic levels, indicating that ethical sensitivity was internalized regardless of academic level.\u003c/p\u003e\u003cp\u003eNo significant gender differences were observed in any domain, indicating that gender is not a determining factor influencing AI literacy or related perceptions. Students with AI education experience exhibited higher literacy scores and a stronger recognition of both the applicability of AI and the need for AI education. This indicates that even those with prior exposure perceive the current curriculum as insufficient and desire more advanced instruction.\u003c/p\u003e\u003cp\u003eThe cross-tabulation analysis further highlighted that clinical experience and educational exposure are key factors shaping perceptions of AI. Clinical experience, in particular, appeared to broaden student\u0026rsquo;s understanding of AI\u0026rsquo;s potential applications, while those with prior AI training or higher literacy levels expressed a greater demand for additional educational opportunities.\u003c/p\u003e\u003cp\u003eStudents preferred practice-based and clinically integrated AI education over purely theoretical learning. Diagnostic assistance was identified as the most anticipated area of AI application in Korean medicine, suggesting that AI could serve not only as a clinical support tool, but also as a catalyst for the academic advancement of Korean medicine. Although opinions differed regarding the appropriate timing of AI education, there was a general consensus that clinically focused training should be implemented during the medical course.\u003c/p\u003e\u003cp\u003e In summary, Korean medical students strongly expressed the need for practice-oriented and clinically applicable AI education while recognizing AI\u0026rsquo;s potential role in the systematization and academic development of Korean medicine. Therefore, a stepwise curriculum is recommended, emphasizing foundational concepts, ethics, and critical thinking at the premedical stage, followed by practice-based training during the medical stage.\u003c/p\u003e\u003cp\u003eThis study makes several contributions to existing literature. First, unlike prior international studies that primarily examined students in Western medical programs, this study is the to target students of Korean medicine, thereby extending the discussion of AI literacy to a new educational context. Second, whereas prior studies often treated literacy as a single construct, this study distinguished between the premedical and medical stages, identifying differences by academic level and clinical experience. Third, while most Western studies perceive AI primarily as a clinically assistive tool, the students of Korean medicine in this study perceived AI as a means of knowledge integration, standardization, and academic advancement, offering a unique perspective. Finally, by combining quantitative surveys with qualitative interviews, this study contextualizes students\u0026rsquo; perceptions using a mixed-methods approach, providing foundational data for the future development of AI curricula in Korean medical education.\u003c/p\u003e\u003cp\u003eThis study had several limitations. The sample was restricted to students from a single college of Korean medicine, which limits the generalizability of the findings. Self-reported data and non-standardized questionnaire items may also constrain the validity of the measured competencies. Moreover, the cross-sectional design prevents a clear determination of causal relationships between AI education experience and literacy levels, and the limited number of participants may not fully capture diverse perspectives across academic years and groups.\u003c/p\u003e\u003cp\u003eFuture studies should expand to multi-institutional and national levels, as well as adopt a longitudinal design to clarify the causal effects of AI education on literacy development. Given the ongoing absence of internationally standardized curricula and guidelines, future research should employ validated instruments or develop new tools that reflect the unique characteristics of Korean medicine to ensure more objective and reliable assessments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In educational practice, enhancing instructors\u0026rsquo; AI competencies is essential for meeting students\u0026rsquo; needs and shifting AI education from a simple knowledge transfer to a process of mutual learning and professional growth. Furthermore, medical education should be designed to encompass not only technical understanding, but also ethical sensitivity, empathy, and patient-centered values.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study represents the first mixed-methods investigation to analyze AI literacy levels and educational needs among Korean medical students. The findings revealed an imbalance across literacy domains; while students demonstrated relatively low levels of technical understanding and practical ability, their ethical and social awareness levels were comparatively high. Differences in perceptions were observed across academic year and according to prior AI education experience, indicating that both clinical exposure and educational experience play critical roles in deepening students\u0026rsquo; understanding of AI.\u003c/p\u003e\u003cp\u003eStudents preferred practice-based and clinically integrated education over concept-oriented learning. Some also expressed expectations that AI could contribute to the integration and standardization of Korean medical knowledge, reflecting an expanded perception of AI beyond its technological utility. These findings suggest that AI education in Korean medicine should not only emphasize technical proficiency, but also aim to enhance clinical competence and foster academic advancement.\u003c/p\u003e\u003cp\u003e Accordingly, a stepwise curriculum model is recommended: the premedical stage should emphasize foundational concepts, ethics, and critical thinking, whereas the medical stage should focus on clinical practice and application. Additionally, faculty capacity building and the cultivation of humanistic competencies should be pursued in concurrently.\u003c/p\u003e\u003cp\u003eThis study provides foundational evidence for the effective integration of AI into Korean medical education. Future research should validate the proposed educational model and establish institutional frameworks to support the systematic implementation of AI literacy education in Korean medicine.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eAretificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eIRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eInstitutional Review Board\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eWKUJIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eWonkwang University Jangheung Integrative Medical Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eto participate\u003c/strong\u003e: This study was conducted in accordance with the Declaration of Helsinki. Approval was obtained from the Institutional Review Board of Wonkwang University Jangheung Integrative Medical Hospital (WKUJIM-IRB-202507-003; July 25, 2025).\u0026nbsp;Written informed consent was obtained from all participants prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The data presented in this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e: Conceptualization, S.B., J.L.; methodology, W.H., S.J., J.L.; software, S.J., W.H; validation, S.J., H.W. and J.L.; formal analysis, S.B.; investigation, S.B., H.W. and J.L.; resources, J.L.; data curation, S.B., S.J.; writing\u0026mdash;original draft preparation, S.B.; writing\u0026mdash;review and editing, S.J., W.H. and J.L.; visualization, S.B., H.W.; supervision, S.J., W.H. and J.L.; project administration, S.J., W.H. and J.L.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCox A. Algorithmic literacy, AI literacy and responsible generative AI literacy. J Web Librariansh. 2024;18:93\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch M, Wienrich C, Straka S, Latoschik M, Carolus A. Overview and confirmatory and exploratory factor analysis of AI literacy scale. Comput Educ Artif Intell. 2024;7:100310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim S, Kim T, Kim K. Development and effectiveness verification of AI education data sets based on constructivist learning principles for enhancing AI literacy. Sci Rep. 2025;15:10725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Woo J, Shin D, Park S, Kwon Y. Study on the Perception and Application of AI in Korean Medicine through Practice and Questionnaire of Korean Medicine Using a Diagnostic Expert System. Korean J Orient Physiol Pathol. 2021;35:22\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae H, Park S, Kim C. 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The necessity of education in response to technological advancements and future environmental changes: a comparison of Korean medicine doctors and students. J Korean Med. 2023;44:59\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarma J, Fernando S, Ting B, Aamir S, Sivaprakasam R. The global use of artificial intelligence in the undergraduate medical curriculum: a systematic review. Cureus. 2023;15:e39701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaupichler M, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students\u0026rsquo; AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Med Educ. 2024;24:401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Wu A, Li D, Kulasegaram K. Artificial intelligence in undergraduate medical education: a scoping review. Acad Med. 2021;96(Suppl 1):S62\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y, Song Y, Balch J, Ren Y, Vellanki D, Hu Z et al. Promoting AI competencies for medical students: a scoping review on frameworks, programs, and tools; 2024. arXiv [Preprint]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2407.18939\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2407.18939\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 13 October 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon M, Daniel M, Ajiboye A, Uraiby H, Xu N, Bartlett R, et al. A scoping review of artificial intelligence in medical education: BEME Guide 84. Med Teach. 2024;46:446\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"medical artificial intelligence, artificial intelligence literacy, Korean medicine, educational needs, mixed-methods study","lastPublishedDoi":"10.21203/rs.3.rs-8525533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8525533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed artificial intelligence literacy among students at a college of Korean Medicine and examined their educational needs and preferred curriculum formats to inform the effective integration of artificial intelligence literacy into future curricula. A mixed-methods design was employed involving 562 students from Wonkwang University College of Korean Medicine. A structured questionnaire was administered across all academic years, and semi-structured interviews were conducted annually with 30 randomly selected students. The findings revealed uneven levels of artificial intelligence literacy, with relatively low technical understanding and practical ability but high levels of critical thinking and ethical awareness. Most students recognized artificial intelligence\u0026rsquo;s potential in Korean Medicine, particularly for diagnostic support, and expressed a strong need for formal artificial intelligence education. Practice-based and clinically oriented training formats were preferred. Cross-analysis indicated that clinical and educational experiences significantly shaped students\u0026rsquo; perceptions of artificial intelligence; students with prior artificial intelligence exposure or higher levels of artificial intelligence literacy demonstrated greater interest in further training. Overall, participants acknowledged both the promise of artificial intelligence and the importance of structured, progressive education that extends beyond theoretical learning toward clinical applications. Stepwise curricula, progressing from foundational knowledge in premedical years to clinical applications in later stages, may be most effective in fostering artificial intelligence literacy in Korean Medicine education.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence Literacy and Educational Needs among Students at a Korean Medicine College: A Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 09:48:45","doi":"10.21203/rs.3.rs-8525533/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-12T11:50:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92734953439637507872899324240777825581","date":"2026-02-19T01:07:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T07:53:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T08:36:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T06:34:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-01-12T06:23:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d63212e-34c8-451a-8d1a-391ebf7dfddc","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T09:48:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 09:48:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8525533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8525533","identity":"rs-8525533","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}