Integrating Problem-Based Learning and Artificial Intelligence in Neurology Education

Integrating Problem-Based Learning and Artificial Intelligence in Neurology Education | 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 Integrating Problem-Based Learning and Artificial Intelligence in Neurology Education Jin-ru Zhang, Xiao-yu Cheng, Yu-lan Cao, Hong Jin, Chun-feng Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6520952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The integration of Artificial Intelligence (AI) in medical education, particularly in neurology, has shown potential for enhancing student learning. Methods This study investigates the effectiveness of AI-assisted Problem-Based Learning (PBL) compared to traditional teaching method in neurology education. Two cohorts of students, one utilizing traditional teaching method and the other integrating AI tools with PBL class, were assessed based on their academic performance. Results The results indicate a significant improvement in problem-solving skills and overall academic performance in the AI-integrated PBL group. The traditional group achieved a score of 92.93 ± 1.731 in clinical practice score, whereas the AI-assisted PBL group obtained a score of 94.74 ± 1.792, showing a statistically significant difference (P < 0.001). Similarly, in the examination results, the traditional group scored 80.74 ± 5.075, while the AI-assisted PBL group attained 82.92 ± 7.11, with the difference also reaching statistical significance (P = 0.038). On the other hand, AI-assisted teaching also enhanced students' interest, and proficiency in lumbar puncture. Conclusions These findings suggest that AI-enhanced PBL can offer an effective pedagogical approach for neurology education. Artificial Intelligence Problem-Based Learning Neurology Education 1. Introduction Medical education has continuously evolved to incorporate innovative pedagogical strategies that enhance student learning, clinical decision-making, and procedural competence[1, 2]. Traditional instructional methodologies, primarily lecture-based approaches, often fall short in engaging students and fostering critical thinking skills necessary for complex medical fields such as neurology[3, 4]. Problem-Based Learning (PBL) has emerged as a viable alternative, promoting active learning through case-based discussions and problem-solving exercises[5]. However, the increasing complexity of medical knowledge necessitates the incorporation of advanced technological tools to support and enhance PBL methodologies. Artificial Intelligence (AI) has shown tremendous promise in medical education, offering adaptive learning experiences, personalized feedback, and immersive simulation-based training[3]. AI-driven learning platforms can assist students in understanding intricate neurological conditions by simulating real-world clinical scenarios, and offering guided procedural training such as lumbar puncture (LP) simulations. The integration of AI into PBL has the potential to bridge gaps in medical education, enabling students to develop deeper analytical skills and improve their diagnostic accuracy[6, 7]. This study aims to compare the effectiveness of AI-assisted PBL versus traditional neurology education by analyzing the academic performance of two distinct student cohorts. The first cohort was taught using conventional lecture-based and self-study methods without PBL or AI assistance, while the second cohort engaged in a PBL with AI curriculum. This research examines the impact of these different educational approaches on students' theoretical knowledge, and engagement levels. By conducting a comparative analysis of these cohorts, this study seeks to highlight the advantages and challenges of integrating AI-driven PBL in neurology education and provide evidence-based recommendations for future medical curricula. 2. Methods 2.1 Study Design This study involved two cohorts of undergraduate students over two academic years for Neurology. The first cohort (Year 1) underwent traditional PBL sessions, while the second cohort (Year 2) participated in AI-assisted PBL sessions. 2.2 The Application of PBL in the Undergraduate Neurology Curriculum In this study, we designed a Problem-Based Learning (PBL) curriculum focused on neurological diseases, including cerebrovascular diseases, Parkinson's disease, Alzheimer's disease, motor neuron diseases, and myasthenia gravis. The PBL curriculum was structured over two weeks. During the first week, students were introduced to the fundamental concepts of epidemiology, pathophysiology, clinical manifestations, imaging findings, diagnosis, and treatment options for each disease. Students were then assigned to review a relevant case study and divided into small groups to discuss and answer questions regarding the patient's diagnosis, disease progression, and treatment strategies[8]. This group activity aimed to encourage critical thinking and collaborative problem-solving. In the second week, each group presented their findings and engaged in a discussion of the case, with particular focus on the diagnostic and therapeutic decisions made throughout the disease course. Teachers provided feedback and evaluations of the group discussions, highlighting key areas of learning and offering insights into alternative approaches. This approach aimed to enhance students' understanding of neurological diseases through active engagement and peer-driven learning, while fostering a deeper appreciation for the clinical decision-making process in these complex conditions. 2.2 AI The AI driven 3D technology assisted lumbar puncture (LP) teaching system is a cutting-edge educational tool aimed at improving students' skills and success rates in LP. This system integrates advanced artificial intelligence and immersive 3D technology to provide medical students, learners, and professionals with a realistic and interactive learning experience. In the traditional LP training, students practiced on mannequins or with instructors giving feedback, but these methods often fall short in providing the immersive, dynamic feedback that mimics real-life scenarios. This system created a more engaging and effective learning environment. The 3D technology provided a detailed, three-dimensional view of the patient's anatomy, allowing the learner to better understand the critical structures they need to avoid during the procedure, such as nerves, and the spinal cord. 2.3 Data Collection and Analysis Student performance was evaluated through: There were two student groups in this study: the first group (n = 72) received traditional teaching methods, while the second group (n = 70) underwent an optimized curriculum incorporating Problem-Based Learning (PBL) and AI-assisted teaching in the following academic year. Student performance was evaluated in two main components: the first component comprised clinical practice and PBL classroom discussion scores or class participation score, while the second component was based on examination scores. In addition to performance assessments, a survey was conducted to gauge students' interest in the neurology course. The survey aimed to explore how the incorporation of PBL and AI-assisted learning influenced students' engagement and interest in the subject. The traditional group followed conventional lectures and practical training, whereas the experimental group participated in PBL sessions and utilized AI tools to enhance their understanding of neurological disorders. This comparison allowed us to assess the effectiveness of integrating PBL and AI in improving both academic performance and student interest in neurology. 2.4 Statistical analysis SPSS software version 20.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analyses. Data are presented as mean±standard deviation. The clinical practice scores and examination scores of the two groups were listed in Table 1 and comparisons were performed using independent Student’s t-test. Table 2 presented the improvement in students' interest and proficiency after using AI-assisted LP. P value <0.05 was considered to be statistically significant. 3. Results 3.1 AI-assisted PBL group showed an improvement in scores compared to the traditional group. The traditional group scored 92.93 ± 1.731, while the AI-assisted PBL group scored 94.74 ± 1.792 for the clinical practice component. The difference was statistically significant ( P < 0.001). Meanwhile for the examination score: the classical group achieved a score of 80.74 ± 5.075, while the AI-assisted PBL group scored 82.92 ± 7.11, with a statistically significant difference observed ( P = 0.038). The total score was composed of 40% clinical practice score and 60% examination score score ( P = 0.002). Table 1 Comparison of scores between Traditional group and AI-assisted PBL group. Traditional group N = 70 AI-assisted PBL group N = 72 P Clinical practice score 92.93 ± 1.731 94.74 ± 1.792 <0.001 Examination score 80.74 ± 5.075 82.92 ± 7.11 0.038 Total score 85.62 ± 3.022 87.64 ± 4.504 0.002 Analysed by independent Student’s t-test. 3.2 Student feedback indicated higher engagement in LP with AI-assisted learning. AI-supported LP training led to improved procedural competency, with students demonstrating greater accuracy and confidence in performing the procedure. We conducted a questionnaire survey which revealed that after implementing AI-assisted LP training, students demonstrated a significant improvement in learning interest (70.33% ±14.55) and procedural proficiency (68.31% ±18.64). Table 2 AI-assisted LP learning Increase in Student Interest (%) 70.33 ± 14.55 Increase in Proficiency (%) 68.31 ± 18.64 Discussion The findings suggest that AI-assisted PBL significantly improves neurology students' learning outcomes. AI tools provide personalized feedback, enhance engagement, and facilitate deeper understanding of complex neurological conditions[ 9 ]. The integration of AI-driven procedural training, such as lumbar puncture simulations, further enhances clinical skill acquisition. PBL has been widely applied in medical education, including neurology[ 10 ]. Actually, we employed the combination of PBL and Team-Based Learning (TBL), where patient cases and clinical issues were assigned to small group discussions[ 11 ]. Studies have demonstrated that PBL enhances students' engagement, promotes deeper understanding, and improves clinical reasoning[ 12 ]. Specifically, in neurology, where complex diagnostic reasoning is essential, PBL fosters the integration of theoretical knowledge with clinical practice, leading to improved retention and application of knowledge. Meta-analyses have confirmed that problem-based learning (PBL) demonstrates significant positive impacts across multiple domains, including theoretical knowledge acquisition, clinical diagnostic reasoning, collaborative teamwork competencies, analytical and problem-solving skills development, literature retrieval proficiency, as well as improvements in learning motivation and educational efficiency[ 13 ]. However, The application of PBL in China has some limitations[ 14 ]. Students in China have developed deeply ingrained learning habits shaped by traditional lecture-based instruction, and the transition to PBL pedagogy poses significant challenges in adapting cognitive patterns and academic performance metrics. With the advancement of medical education, Chinese medical students have increasingly embraced PBL, actively engaging in discussions, answering questions, and critically analyzing disease diagnosis, differential diagnosis, and treatment strategies[ 14 , 15 ]. Studies have shown that the implementation of PBL in China has enhanced students' problem-solving abilities and clinical reasoning skills, making them more confident and proactive learners. Additionally, research indicates that Chinese medical students appreciate the interactive and student-centered nature of PBL, which fosters deeper understanding and knowledge retention in neurology education. Despite initial challenges such as adapting to a more independent learning style, students have demonstrated strong adaptability and enthusiasm, contributing to the growing success of PBL in Chinese medical curricula. AI is increasingly being integrated into neurology education, enhancing various aspects of training, including LP simulation, neuroanatomy visualization, and localization diagnosis. In our study, AI-powered virtual reality and augmented reality tools provided immersive and interactive simulations for LP training, allowing students to practice in a risk-free environment and improve their technical proficiency. Additionally, AI-based neuroanatomy platforms use 3D modeling and machine learning algorithms to facilitate a deeper understanding of complex brain structures, offering personalized learning experiences. In clinical reasoning, AI-driven decision support systems can assist students in localization diagnosis by analyzing patient data and providing differential diagnoses, thus enhancing their diagnostic accuracy[ 16 , 17 ]. Within the past two decades, an exponential increase has been seen in the medical applications of AI devices and software. Many AI technologies are being utilized in medicine across radiology[ 18 ], healthcare[ 19 ], and even diagnosis[ 20 ], and are now also being applied in undergraduate education[ 6 ]. We have integrated AI into undergraduate medical education for teaching lumbar puncture, cranial nerve pathways, and localization of cerebrovascular diseases, and these applications have yielded promising educational outcomes. In the future, we can focus on cultivating specialized professionals proficient in integrating AI technology with medical education. They will play pivotal roles in intelligent curriculum development, medical knowledge graph construction, and virtual simulation training platform design, thereby providing more precise and efficient technological support for medical education. By establishing interdisciplinary training systems, we will cultivate versatile talents who understand both healthcare education principles and AI algorithm applications. This integration of medicine, computer science, and pedagogy will significantly enhance the personalization and intelligence level of medical education. Conclusion Integrating AI into PBL in neurology education enhances student performance and engagement. Future studies should explore long-term retention of knowledge and the impact of AI on clinical decision-making. ‌Emerging pedagogical innovations, intelligent technologies, and web-enhanced platforms are being progressively integrated into neurology education. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University (JD-LK2025061-I01). This study was conducted in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. Informed written consent was obtained from all the participants. Acknowledgements We thank the technicians who developed the Lumbarpuncture AI. Consent for publication Not applicable. Competing Interest The authors state that there is no conflict of interest. Data availability statement Data available on request from the authors. Authors’ contributions Jinru Zhang and Xiaoyu Cheng wrote the main manuscript text and prepared the Tables. All authors reviewed the manuscript. The authors read and approved the final manuscript. Funding This work was supported by the Resident Standardization Training Capacity Building Support Project‌ (ZPTJ-RC202407); the National Natural Science Fund of China (82401492); the Natural Science Foundation of Jiangsu Province (BK2024047621) and the Project of Second Affiliated Hospital of Soochow University Neurological Disorders Research Center‌（ND2024B07). References Triola MM, Rodman A. Integrating Generative Artificial Intelligence Into Medical Education: Curriculum, Policy, and Governance Strategies. Acad Med. 2025;100(4):413–8. Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR Med Educ. 2024;10:e54793. Jones DT, Kerber KA. Artificial Intelligence and the Practice of Neurology in 2035: The Neurology Future Forecasting Series. Neurology. 2022;98(6):238–45. Prosser M, Sze D. Problem-based learning: student learning experiences and outcomes. Clin Linguist Phon. 2014;28(1–2):131–42. Li X, Zhang L, Sun W, Lei M, Li Y, Zhang J, Huang X. Comparison of the effects of different teaching methods on the effectiveness of teaching neurology in China: a bayesian network meta-analysis and systematic review. BMC Med Educ. 2024;24(1):1560. Kedar S, Khazanchi D. Neurology education in the era of artificial intelligence. Curr Opin Neurol. 2023;36(1):51–8. Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri M. Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives. J Adv Med Educ Prof. 2023;11(3):133–40. Zhao W, He L, Deng W, Zhu J, Su A, Zhang Y. The effectiveness of the combined problem-based learning (PBL) and case-based learning (CBL) teaching method in the clinical practical teaching of thyroid disease. BMC Med Educ. 2020;20(1):381. Han ER, Yeo S, Kim MJ, Lee YH, Park KH, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Med Educ. 2019;19(1):460. Dolmans D, Michaelsen L, van Merriënboer J, van der Vleuten C. Should we choose between problem-based learning and team-based learning? No, combine the best of both worlds. Med Teach. 2015;37(4):354–9. Morton CE, Saleh SN, Smith SF, Hemani A, Ameen A, Bennie TD, Toro-Troconis M. Blended learning: how can we optimise undergraduate student engagement. BMC Med Educ. 2016;16:195. Bodagh N, Bloomfield J, Birch P, Ricketts W. Problem-based learning: a review. Br J Hosp Med (Lond). 2017;78(11):C167–167170. Li T, Wang W, Li Z, Wang H, Liu X. Problem-based or lecture-based learning, old topic in the new field: a meta-analysis on the effects of PBL teaching method in Chinese standardized residency training. BMC Med Educ. 2022;22(1):221. Hu X, Li J, Wang X, Guo K, Liu H, Yu Q, Kuang G, Zhang S, Liu L, Lin Z, et al. Medical education challenges in Mainland China: An analysis of the application of problem-based learning. Med Teach. 2025;47(4):713–28. Kim YJ. The PBL teaching method in neurology education in the traditional Chinese medicine undergraduate students: An observational study. Med (Baltim). 2023;102(39):e35143. Orzan F, Iancu ŞD, Dioşan L, Bálint Z. Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review. Front Neurosci. 2024;18:1457420. Grunhut J, Wyatt AT, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. J Med Educ Curric Dev. 2021;8:23821205211036836. Fiani B, Pasko K, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci. 2022;33(4):383–95. Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, Seshadri A, Yousufuddin M, Arumaithurai K. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268(5):1623–42. Merkin A, Krishnamurthi R, Medvedev ON. Machine learning, artificial intelligence and the prediction of dementia. Curr Opin Psychiatry. 2022;35(2):123–9. Additional Declarations No competing interests reported. Supplementary Files AILumbarPunctureSystemApplicationEvaluationForm.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6520952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464717349,"identity":"b90744fc-6444-40ca-920c-2d40d778fdd1","order_by":0,"name":"Jin-ru Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jin-ru","middleName":"","lastName":"Zhang","suffix":""},{"id":464717350,"identity":"140b2093-1d93-4725-8e6d-e520140caf1a","order_by":1,"name":"Xiao-yu Cheng","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-yu","middleName":"","lastName":"Cheng","suffix":""},{"id":464717351,"identity":"f7edb319-242f-4fc0-b987-d247fa005ef6","order_by":2,"name":"Yu-lan Cao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yu-lan","middleName":"","lastName":"Cao","suffix":""},{"id":464717352,"identity":"47f74a2f-234d-4905-8ecb-4153f85c283f","order_by":3,"name":"Hong Jin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Jin","suffix":""},{"id":464717353,"identity":"2a4feb11-4c5b-40de-b307-bfb992ef9261","order_by":4,"name":"Chun-feng Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Chun-feng","middleName":"","lastName":"Liu","suffix":""},{"id":464717354,"identity":"bf338ee7-bac7-49d1-af33-704f9a88c1d0","order_by":5,"name":"Cheng-jie Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDCCAwxsQJKNB8z5YGBjR5oWxhkFacnEaoEAZp4PhxgbCOngu3342YOPO/hk+MUOP3xsY3CAmYH98NEN+LRInkszN5x5ho1HcnaasXGOwR0+Bp60tBv4tBic4WGT5m1j4zG4nWAmnWPwjJlBgseMsJa/QC32t9O/SVsYHGZsIEoLI8gW6RwzaQZitEieYTOT7AVqkbidU2zYY5CWzEbIL3xnmJ9J/Gw7Zs8/O33jgx9/bOz42Q8fw6sFCo4hmGy4VaGAGiLVjYJRMApGwYgEAMb8RBDJZ6E6AAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Cheng-jie","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2025-04-24 12:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6520952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6520952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93496124,"identity":"01bbfa13-9a04-4c25-8cf0-471a1ece985d","added_by":"auto","created_at":"2025-10-14 13:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":484986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6520952/v1/906676e2-7a2b-4da4-840f-53ac53e835a4.pdf"},{"id":83863490,"identity":"d7d46665-2980-4494-a4df-414965c6682d","added_by":"auto","created_at":"2025-06-03 20:29:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11889,"visible":true,"origin":"","legend":"","description":"","filename":"AILumbarPunctureSystemApplicationEvaluationForm.docx","url":"https://assets-eu.researchsquare.com/files/rs-6520952/v1/57c20b36693d10c9096b5164.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Problem-Based Learning and Artificial Intelligence in Neurology Education","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMedical education has continuously evolved to incorporate innovative pedagogical strategies that enhance student learning, clinical decision-making, and procedural competence[1, 2].\u0026nbsp;Traditional instructional methodologies, primarily lecture-based approaches, often fall short in engaging students and fostering critical thinking skills necessary for complex medical fields such as neurology[3, 4]. Problem-Based Learning (PBL) has emerged as a viable alternative, promoting active learning through case-based discussions and problem-solving exercises[5]. However, the increasing complexity of medical knowledge necessitates the incorporation of advanced technological tools to support and enhance PBL methodologies.\u003c/p\u003e\n\u003cp\u003eArtificial Intelligence (AI) has shown tremendous promise in medical education, offering adaptive learning experiences, personalized feedback, and immersive simulation-based training[3]. AI-driven learning platforms can assist students in understanding intricate neurological conditions by simulating real-world clinical scenarios, and offering guided procedural training such as lumbar puncture (LP) simulations. The integration of AI into PBL has the potential to bridge gaps in medical education, enabling students to develop deeper analytical skills and improve their diagnostic accuracy[6, 7].\u003c/p\u003e\n\u003cp\u003eThis study aims to compare the effectiveness of AI-assisted PBL versus traditional neurology education by analyzing the academic performance of two distinct student cohorts. The first cohort was taught using conventional lecture-based and self-study methods without PBL or AI assistance, while the second cohort engaged in a PBL with AI curriculum. This research examines the impact of these different educational approaches on students' theoretical knowledge, and engagement levels. By conducting a comparative analysis of these cohorts, this study seeks to highlight the advantages and challenges of integrating AI-driven PBL in neurology education and provide evidence-based recommendations for future medical curricula.\u003c/p\u003e"},{"header":"2. Methods ","content":"\u003cp\u003e2.1 Study Design This study involved two cohorts of undergraduate students over two academic years for Neurology. The first cohort (Year 1) underwent traditional PBL sessions, while the second cohort (Year 2) participated in AI-assisted PBL sessions.\u003c/p\u003e\n\u003cp\u003e2.2 The Application of PBL in the Undergraduate Neurology Curriculum\u003c/p\u003e\n\u003cp\u003eIn this study, we designed a Problem-Based Learning (PBL) curriculum focused on neurological diseases, including cerebrovascular diseases, Parkinson's disease, Alzheimer's disease, motor neuron diseases, and myasthenia gravis. The PBL curriculum was structured over two weeks. During the first week, students were introduced to the fundamental concepts of epidemiology, pathophysiology, clinical manifestations, imaging findings, diagnosis, and treatment options for each disease. Students were then assigned to review a relevant case study and divided into small groups to discuss and answer questions regarding the patient's diagnosis, disease progression, and treatment strategies[8]. This group activity aimed to encourage critical thinking and collaborative problem-solving. In the second week, each group presented their findings and engaged in a discussion of the case, with particular focus on the diagnostic and therapeutic decisions made throughout the disease course. Teachers provided feedback and evaluations of the group discussions, highlighting key areas of learning and offering insights into alternative approaches. This approach aimed to enhance students' understanding of neurological diseases through active engagement and peer-driven learning, while fostering a deeper appreciation for the clinical decision-making process in these complex conditions.\u003c/p\u003e\n\u003cp\u003e2.2 AI\u003c/p\u003e\n\u003cp\u003eThe AI driven 3D technology assisted lumbar puncture (LP) teaching system is a cutting-edge educational tool aimed at improving students' skills and success rates in LP. This system integrates advanced artificial intelligence and immersive 3D technology to provide medical students, learners, and professionals with a realistic and interactive learning experience. In the traditional LP training, students practiced on mannequins or with instructors giving feedback, but these methods often fall short in providing the immersive, dynamic feedback that mimics real-life scenarios. This system created a more engaging and effective learning environment. The 3D technology provided a detailed, three-dimensional view of the patient's anatomy, allowing the learner to better understand the critical structures they need to avoid during the procedure, such as nerves, and the spinal cord.\u003c/p\u003e\n\u003cp\u003e2.3 Data Collection and Analysis Student performance was evaluated through:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were two student groups in this study: the first group (n = 72) received traditional teaching methods, while the second group (n = 70) underwent an optimized curriculum incorporating Problem-Based Learning (PBL) and AI-assisted teaching in the following academic year. Student performance was evaluated in two main components: the first component comprised clinical practice and PBL classroom discussion scores or class participation score, while the second component was based on examination scores. In addition to performance assessments, a survey was conducted to gauge students' interest in the neurology course. The survey aimed to explore how the incorporation of PBL and AI-assisted learning influenced students' engagement and interest in the subject. The traditional group followed conventional lectures and practical training, whereas the experimental group participated in PBL sessions and utilized AI tools to enhance their understanding of neurological disorders. This comparison allowed us to assess the effectiveness of integrating PBL and AI in improving both academic performance and student interest in neurology.\u003c/p\u003e\n\u003cp\u003e2.4 Statistical analysis\u003c/p\u003e\n\u003cp\u003eSPSS software version 20.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analyses. Data are presented as mean±standard deviation. The clinical practice scores and examination scores of the two groups were listed in Table 1 and comparisons were performed using independent Student’s t-test. Table 2 presented the improvement in students' interest and proficiency after using AI-assisted LP. \u003cem\u003eP\u003c/em\u003e value \u0026lt;0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 AI-assisted PBL group showed an improvement in scores compared to the traditional group.\u003c/h2\u003e \u003cp\u003eThe traditional group scored 92.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.731, while the AI-assisted PBL group scored 94.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.792 for the clinical practice component. The difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meanwhile for the examination score: the classical group achieved a score of 80.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.075, while the AI-assisted PBL group scored 82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11, with a statistically significant difference observed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). The total score was composed of 40% clinical practice score and 60% examination score score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\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\u003eComparison of scores between Traditional group and AI-assisted PBL group.\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=\"\u0026plusmn;\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eTraditional group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;70\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-assisted PBL group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\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\u003eClinical practice score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e92.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e94.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExamination score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e80.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e85.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e87.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysed by independent Student\u0026rsquo;s t-test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Student feedback indicated higher engagement in LP with AI-assisted learning.\u003c/h2\u003e \u003cp\u003eAI-supported LP training led to improved procedural competency, with students demonstrating greater accuracy and confidence in performing the procedure. We conducted a questionnaire survey which revealed that after implementing AI-assisted LP training, students demonstrated a significant improvement in learning interest (70.33% \u0026plusmn;14.55) and procedural proficiency (68.31% \u0026plusmn;18.64).\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\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\u003eAI-assisted LP learning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncrease in Student Interest (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncrease in Proficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.31\u0026thinsp;\u0026plusmn;\u0026thinsp;18.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe findings suggest that AI-assisted PBL significantly improves neurology students' learning outcomes. AI tools provide personalized feedback, enhance engagement, and facilitate deeper understanding of complex neurological conditions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The integration of AI-driven procedural training, such as lumbar puncture simulations, further enhances clinical skill acquisition.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003ePBL has been widely applied in medical education, including neurology[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Actually, we employed the combination of PBL and Team-Based Learning (TBL), where patient cases and clinical issues were assigned to small group discussions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies have demonstrated that PBL enhances students' engagement, promotes deeper understanding, and improves clinical reasoning[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Specifically, in neurology, where complex diagnostic reasoning is essential, PBL fosters the integration of theoretical knowledge with clinical practice, leading to improved retention and application of knowledge. Meta-analyses have confirmed that problem-based learning (PBL) demonstrates significant positive impacts across multiple domains, including theoretical knowledge acquisition, clinical diagnostic reasoning, collaborative teamwork competencies, analytical and problem-solving skills development, literature retrieval proficiency, as well as improvements in learning motivation and educational efficiency[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, The application of PBL in China has some limitations[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Students in China have developed deeply ingrained learning habits shaped by traditional lecture-based instruction, and the transition to PBL pedagogy poses significant challenges in adapting cognitive patterns and academic performance metrics.\u003c/p\u003e \u003cp\u003eWith the advancement of medical education, Chinese medical students have increasingly embraced PBL, actively engaging in discussions, answering questions, and critically analyzing disease diagnosis, differential diagnosis, and treatment strategies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have shown that the implementation of PBL in China has enhanced students' problem-solving abilities and clinical reasoning skills, making them more confident and proactive learners. Additionally, research indicates that Chinese medical students appreciate the interactive and student-centered nature of PBL, which fosters deeper understanding and knowledge retention in neurology education. Despite initial challenges such as adapting to a more independent learning style, students have demonstrated strong adaptability and enthusiasm, contributing to the growing success of PBL in Chinese medical curricula.\u003c/p\u003e \u003cp\u003eAI is increasingly being integrated into neurology education, enhancing various aspects of training, including LP simulation, neuroanatomy visualization, and localization diagnosis. In our study, AI-powered virtual reality and augmented reality tools provided immersive and interactive simulations for LP training, allowing students to practice in a risk-free environment and improve their technical proficiency. Additionally, AI-based neuroanatomy platforms use 3D modeling and machine learning algorithms to facilitate a deeper understanding of complex brain structures, offering personalized learning experiences. In clinical reasoning, AI-driven decision support systems can assist students in localization diagnosis by analyzing patient data and providing differential diagnoses, thus enhancing their diagnostic accuracy[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin the past two decades, an exponential increase has been seen in the medical applications of AI devices and software. Many AI technologies are being utilized in medicine across radiology[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], healthcare[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and even diagnosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and are now also being applied in undergraduate education[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We have integrated AI into undergraduate medical education for teaching lumbar puncture, cranial nerve pathways, and localization of cerebrovascular diseases, and these applications have yielded promising educational outcomes. In the future, we can focus on cultivating specialized professionals proficient in integrating AI technology with medical education. They will play pivotal roles in intelligent curriculum development, medical knowledge graph construction, and virtual simulation training platform design, thereby providing more precise and efficient technological support for medical education. By establishing interdisciplinary training systems, we will cultivate versatile talents who understand both healthcare education principles and AI algorithm applications. This integration of medicine, computer science, and pedagogy will significantly enhance the personalization and intelligence level of medical education.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003e Integrating AI into PBL in neurology education enhances student performance and engagement. Future studies should explore long-term retention of knowledge and the impact of AI on clinical decision-making. \u0026zwnj;Emerging pedagogical innovations, intelligent technologies, and web-enhanced platforms are being progressively integrated into neurology education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University (JD-LK2025061-I01).\u0026nbsp;This study was conducted in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. Informed written consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the technicians who developed the Lumbarpuncture AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinru Zhang and Xiaoyu Cheng wrote the main manuscript text and prepared the Tables. All authors reviewed the manuscript. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Resident Standardization Training Capacity Building Support Project‌ (ZPTJ-RC202407); the National Natural Science Fund of China (82401492); the Natural Science Foundation of Jiangsu Province (BK2024047621) and the Project of Second Affiliated Hospital of Soochow University Neurological Disorders Research Center‌（ND2024B07).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTriola MM, Rodman A. Integrating Generative Artificial Intelligence Into Medical Education: Curriculum, Policy, and Governance Strategies. Acad Med. 2025;100(4):413\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR Med Educ. 2024;10:e54793.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones DT, Kerber KA. Artificial Intelligence and the Practice of Neurology in 2035: The Neurology Future Forecasting Series. Neurology. 2022;98(6):238\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProsser M, Sze D. Problem-based learning: student learning experiences and outcomes. Clin Linguist Phon. 2014;28(1\u0026ndash;2):131\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Zhang L, Sun W, Lei M, Li Y, Zhang J, Huang X. Comparison of the effects of different teaching methods on the effectiveness of teaching neurology in China: a bayesian network meta-analysis and systematic review. BMC Med Educ. 2024;24(1):1560.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKedar S, Khazanchi D. Neurology education in the era of artificial intelligence. Curr Opin Neurol. 2023;36(1):51\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri M. Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives. J Adv Med Educ Prof. 2023;11(3):133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao W, He L, Deng W, Zhu J, Su A, Zhang Y. The effectiveness of the combined problem-based learning (PBL) and case-based learning (CBL) teaching method in the clinical practical teaching of thyroid disease. BMC Med Educ. 2020;20(1):381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan ER, Yeo S, Kim MJ, Lee YH, Park KH, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Med Educ. 2019;19(1):460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolmans D, Michaelsen L, van Merri\u0026euml;nboer J, van der Vleuten C. Should we choose between problem-based learning and team-based learning? No, combine the best of both worlds. Med Teach. 2015;37(4):354\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorton CE, Saleh SN, Smith SF, Hemani A, Ameen A, Bennie TD, Toro-Troconis M. Blended learning: how can we optimise undergraduate student engagement. BMC Med Educ. 2016;16:195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodagh N, Bloomfield J, Birch P, Ricketts W. Problem-based learning: a review. Br J Hosp Med (Lond). 2017;78(11):C167\u0026ndash;167170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Wang W, Li Z, Wang H, Liu X. Problem-based or lecture-based learning, old topic in the new field: a meta-analysis on the effects of PBL teaching method in Chinese standardized residency training. BMC Med Educ. 2022;22(1):221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Li J, Wang X, Guo K, Liu H, Yu Q, Kuang G, Zhang S, Liu L, Lin Z, et al. Medical education challenges in Mainland China: An analysis of the application of problem-based learning. Med Teach. 2025;47(4):713\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YJ. The PBL teaching method in neurology education in the traditional Chinese medicine undergraduate students: An observational study. Med (Baltim). 2023;102(39):e35143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrzan F, Iancu ŞD, Dioşan L, B\u0026aacute;lint Z. Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review. Front Neurosci. 2024;18:1457420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrunhut J, Wyatt AT, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. J Med Educ Curric Dev. 2021;8:23821205211036836.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiani B, Pasko K, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci. 2022;33(4):383\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, Seshadri A, Yousufuddin M, Arumaithurai K. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268(5):1623\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerkin A, Krishnamurthi R, Medvedev ON. Machine learning, artificial intelligence and the prediction of dementia. Curr Opin Psychiatry. 2022;35(2):123\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Problem-Based Learning, Neurology Education","lastPublishedDoi":"10.21203/rs.3.rs-6520952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6520952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe integration of Artificial Intelligence (AI) in medical education, particularly in neurology, has shown potential for enhancing student learning.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study investigates the effectiveness of AI-assisted Problem-Based Learning (PBL) compared to traditional teaching method in neurology education. Two cohorts of students, one utilizing traditional teaching method and the other integrating AI tools with PBL class, were assessed based on their academic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results indicate a significant improvement in problem-solving skills and overall academic performance in the AI-integrated PBL group. The traditional group achieved a score of 92.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.731 in clinical practice score, whereas the AI-assisted PBL group obtained a score of 94.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.792, showing a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in the examination results, the traditional group scored 80.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.075, while the AI-assisted PBL group attained 82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11, with the difference also reaching statistical significance (P\u0026thinsp;=\u0026thinsp;0.038). On the other hand, AI-assisted teaching also enhanced students' interest, and proficiency in lumbar puncture.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings suggest that AI-enhanced PBL can offer an effective pedagogical approach for neurology education.\u003c/p\u003e","manuscriptTitle":"Integrating Problem-Based Learning and Artificial Intelligence in Neurology Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 20:29:39","doi":"10.21203/rs.3.rs-6520952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cde83e2b-048c-4b47-9297-235abefd3bab","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T13:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 20:29:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6520952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6520952","identity":"rs-6520952","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}