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Semin Neurol
DOI: 10.1055/a-2795-8556
Review Article

Let's Talk Tech: The Role of Technology in Neurology Education

Authors

  • Catherine S. W. Albin 

    1   Division of Neurocritical Care, Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, United States

  • Jaclyn M. Martindale 

    2   Division of Pediatric Neurology, Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States

  • Rohit Marawar

    3   Division of Epilepsy, Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, United States

  • Rachel Gottlieb-Smith

    4   Division of Pediatric Neurology, Department of Pediatrics, University of Michigan School of Medicine, Ann Arbor, Michigan, United States

  • Ashley Paul

    5   Division of Movement Disorders, Department of Neurology, Johns Hopkins University, Baltimore, Maryland, United States

  • Jeffrey Strelzik

    6   Department of Pediatric Neurology, George Washington University School of Medicine and Health Sciences, Children's National Hospital, Washington, District of Columbia, United States

  • Alison Christy

    7   Pediatric Neurology, Providence Health and Services, Portland, Oregon, United States

  • Kathryn Xixis

    8   Division of Pediatric Neurology, Department of Neurology, University of Virginia School of Medicine, Charlottesville, Virginia, United States

  • Jessica Goldstein

    9   Division of Pediatric Neurology, Department of Neurology, University of Minnesota School of Medicine, Minneapolis, Minnesota, United States
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Abstract

As a field, neurology can seem complicated, overwhelming, and at times ambiguous and uncertain. However, novel technological developments—including artificial intelligence—can be used to decrease “neurophobia,” foster enthusiasm about our specialty, and increase curiosity and motivation while decreasing the educator's time in preparation. This review discusses the technology-enhanced teaching landscape and how neurology integrates into known conceptual frameworks and learning theories in medical education. We provide detailed guidance for technology-focused curriculum design in all realms of neurologic teaching: at the bedside, in small groups, and in larger presentations. Finally, we identify ways technological scholarship can be leveraged toward academic promotion for neurology education.

Keywords

artificial intelligence - technology-enhanced teaching - technology-enhanced learning - digital education


Publication History

Received: 17 November 2025

Accepted: 22 January 2026

Article published online:
20 February 2026

© 2026. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Shoirah H, Ntranos A, Brandstadter R. et al. Education research: resident education through adult learning in neurology: implementation and impact. Neurology 2018; 91 (05) 234-238
    Search in Google Scholar
  • 2 Mukhalalati BA, Taylor A. Adult learning theories in context: a quick guide for healthcare professional educators. J Med Educ Curric Dev 2019; 6: 2382120519840332
    Search in Google Scholar
  • 3 Moreno-Zambrano D, Sandrone S, Meza-Venegas J. et al. Exploring the key factors behind neurophobia: a systematic review of the English, Spanish and Portuguese literature. Brain Disord 2021; 2: 100011
    Search in Google Scholar
  • 4 Zimmerman WD, Pergakis MB, Gorman EF. et al. Scoping review: innovations in clinical neurology education. Neurol Educ 2023; 2 (01) e200048
    Search in Google Scholar
  • 5 Cook DA, Levinson AJ, Garside S, Dupras DM, Erwin PJ, Montori VM. Internet-based learning in the health professions: a meta-analysis. JAMA 2008; 300 (10) 1181-1196
    Search in Google Scholar
  • 6 Ellaway R, Masters K. AMEE Guide 32: e-learning in medical education part 1: learning, teaching and assessment. Med Teach 2008; 30 (05) 455-473
    Search in Google Scholar
  • 7 Goldstein J, Martindale JM, Albin C. et al. Be in the digital room where it happens, part II: social media for neurology educators. Child Neurol Open 2023 10. 2329048X231169400
    Search in Google Scholar
  • 8 Kulaksız T, Steinbacher J, Kalz M. Technology-enhanced learning in the education of oncology medical professionals: a systematic literature review. J Cancer Educ 2023; 38 (05) 1743-1751
    Search in Google Scholar
  • 9 Cheng A, Lang TR, Starr SR, Pusic M, Cook DA. Technology-enhanced simulation and pediatric education: a meta-analysis. Pediatrics 2014; 133 (05) e1313-e1323
    Search in Google Scholar
  • 10 Abdulnour REE, Gin B, Boscardin CK. Educational Strategies for clinical supervision of artificial intelligence use. N Engl J Med 2025; 393 (08) 786-797
    Search in Google Scholar
  • 11 Schwartzstein RM. Clinical reasoning and artificial intelligence: can AI really think?. Trans Am Clin Climatol Assoc 2024; 134: 133-145
    Search in Google Scholar
  • 12 Knowles: The adult learner: The definitive classic... - Google Scholar [Internet]. [cited 2025 Sep 17]. Accessed February 3, 2026, at: https://scholar.google.com/scholar_lookup?title=the%20adult%20learner:%20the%20definitive%20classic%20in%20adult%20education%20and%20human%20resource%20development&author=ms%20knowles&author=ief%20holton&author=ra%20swanson&author=pa%20robinson&publication_year=2020&
  • 13 Knowles M, Knowles MS. . III EFH, III EFH, Robinson PA, Swanson RA, et al. The Adult Learner: The Definitive Classic in Adult Education and Human Resource Development. 9th ed. London: Routledge; 2020. . 406 p.
    Search in Google Scholar
  • 14 Piaget J. The Construction Of Reality In The Child. London: Routledge; 2013. . 400 p.
    Search in Google Scholar
  • 15 Kolb DA. The process of experiential learning. In: Culture and Processes of Adult Learning. Routledge; 1993
    Search in Google Scholar
  • 16 Bordage G. Conceptual frameworks to illuminate and magnify. Med Educ 2009; 43 (04) 312-319
    Search in Google Scholar
  • 17 Luke K. Twelve tips for designing an inclusive curriculum in medical education using universal design for learning (UDL) principles. MedEdPublish 2021; 10: 118
    Search in Google Scholar
  • 18 Baroni F, Lazzari M. Universal design for learning at university: technologies, blended learning and teaching methods. Stud Health Technol Inform 2022; 297: 541-548
    Search in Google Scholar
  • 19 Dunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT. Improving students' learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol Sci Public Interest 2013; 14 (01) 4-58
    Search in Google Scholar
  • 20 Moulton ST, Türkay S, Kosslyn SM. Does a presentation's medium affect its message? PowerPoint, Prezi, and oral presentations. PLoS One 2017; 12 (07) e0178774
    Search in Google Scholar
  • 21 Gao C, Gheihman G, Kaplan T. et al. Education research: creating online interactive case-based learning experiences from educational case reports with large language models: a feasibility study. Neurol Educ 2025; 4 (04) e200250
    Search in Google Scholar
  • 22 Gamification in Medical Education – Improve Teaching [Internet]. 2025 [cited 2025 Sep 17]. Accessed February 3, 2026, at: https://improveteaching.med.jhmi.edu/gamification/
    Search in Google Scholar
  • 23 Edwards SL, Gantwerker E, Cosimini M. et al. Game-based learning in neuroscience: key terminology, literature survey, and how to guide to create a serious game. Neurol Educ 2023; 2 (04) e200103
    Search in Google Scholar
  • 24 Ahmed A, Kerr E, O'Malley A. Quality assurance and validity of AI-generated single best answer questions. BMC Med Educ 2025; 25 (01) 300
    Search in Google Scholar
  • 25 Laupichler MC, Rother JF, Grunwald Kadow IC, Ahmadi S, Raupach T. Large language models in medical education: comparing ChatGPT- to human-generated exam questions. Acad Med 2024; 99 (05) 508-512
    Search in Google Scholar
  • 26 Indran IR, Paranthaman P, Gupta N, Mustafa N. Twelve tips to leverage AI for efficient and effective medical question generation: a guide for educators using Chat GPT. Med Teach 2024; 46 (08) 1021-1026
    Search in Google Scholar
  • 27 Diaz-Cotto J. Strategies to promote collaborative online learning. Innov Educ (Mex) 2025
    Search in Google Scholar
  • 28 Xu M, Luo Y, Zhang Y, Xia R, Qian H, Zou X. Game-based learning in medical education. Front Public Health 2023; 11: 1113682
    Search in Google Scholar
  • 29 Roediger III HL, Butler AC. The critical role of retrieval practice in long-term retention. Trends Cogn Sci 2011; 15 (01) 20-27
    Search in Google Scholar
  • 30 Lau KHV, Lakhan SE, Achike F. New media, technology and neurology education. Semin Neurol 2018; 38 (04) 457-464
    Search in Google Scholar
  • 31 George P, Newey CR, Bhimraj A. The tablet device in hospital neurology and in neurology graduate medical education: a preliminary study. Neurohospitalist 2015; 5 (01) 15-21
    Search in Google Scholar
  • 32 Hackethal V. Top 10 Neurology Apps. [Internet]. NeurologyLive. [cited July 30, 2025]. Accessed February 3, 2026, at: https://www.neurologylive.com/view/top-10-neurology-apps?slide=18
  • 33 Albin CSW, Greene JP, LaHue SC. et al. Reviews in medical education: advances in simulation to address new challenges in neurology. Neurol Educ 2023; 2 (01) e200042
    Search in Google Scholar
  • 34 Ghoshal S, Albin CSW, Malhotra NA. et al. Education research: utilization of simulation in residency programs: a survey of adult neurology residency program directors. Neurol Educ 2024; 3 (04) e200156
    Search in Google Scholar
  • 35 Gheihman G, Harrold GK, Howard D, Albin CSW, Kaplan TB. Using neurology trainees as standardized patients in a neurological emergency simulation curriculum for medical students. Med Sci Educ 2024; 34 (03) 589-599
    Search in Google Scholar
  • 36 Odom M, Gomez JR, Danelson KA, Sarwal A. Development of a homemade spinal model for simulation to teach ultrasound guidance for lumbar puncture. Neurocrit Care 2019; 31 (03) 550-558
    Search in Google Scholar
  • 37 Tai J. UCSF Lumbar Puncture Simulator Project [Internet]. UCSF Library. 2023 [cited 2025 Oct 1]. Available from: http://www.library.ucsf.edu/news/ucsf-lumbar-puncture-simulator-project/
    Search in Google Scholar
  • 38 Massoth C, Röder H, Ohlenburg H. et al. High-fidelity is not superior to low-fidelity simulation but leads to overconfidence in medical students. BMC Med Educ 2019; 19 (01) 29
    Search in Google Scholar
  • 39 Wolpaw TM, Wolpaw DR, Papp KK. SNAPPS: a learner-centered model for outpatient education. Acad Med 2003; 78 (09) 893-898
    Search in Google Scholar
  • 40 Neher JO, Gordon KC, Meyer B, Stevens N. A five-step “microskills” model of clinical teaching. J Am Board Fam Pract 1992; 5 (04) 419-424
    Search in Google Scholar
  • 41 Teixeira Ferraz Grünewald S, Grünewald T, Ezequiel OS, Lucchetti ALG, Lucchetti G. One-minute preceptor and SNAPPS for clinical reasoning: a systematic review and meta-analysis. Intern Med J 2023; 53 (05) 680-689
    Search in Google Scholar
  • 42 Lee CW, Chen GL, Yu MJ, Cheng PL, Lee YK. A study to analyze narrative feedback record of an emergency department. J Acute Med 2021; 11 (02) 39-48
    Search in Google Scholar
  • 43 Fernandez A, Moeller JJ, Harrar DB. et al. Curriculum innovation: design and implementation of synchronous and asynchronous curricula to enhance residents' EEG knowledge and experience. Neurol Educ 2023; 2 (04) e200101
    Search in Google Scholar
  • 44 Marawar R, Albin C, Saylor D. et al. NeurAnki: behind the scenes of creating the first-ever flashcard based deck for resident education (P12–5.009). Neurology 2025; 104 (7, suppl_1): 2054
    Search in Google Scholar
  • 45 Ratliff JB, Nascimento FA, Tornes L. et al. Curriculum innovations: a podcast-based neurologic emergency flipped classroom curriculum for neurology residents. Neurol Educ 2023; 2 (03) e200081
    Search in Google Scholar
  • 46 Thesen T, Alilonu NA, Stone S. AI patient actor: an open-access generative-AI app for communication training in health professions. Med Sci Educ 2024; 35 (01) 25-27
    Search in Google Scholar
  • 47 Qian C, Gao C, Park SO. et al. Use of large language models for rapid quantitative feedback in case-based learning: a pilot study. Med Sci Educ 2025; 35 (03) 1169-1171
    Search in Google Scholar
  • 48 Peralta Ramirez AA, Trujillo López S, Navarro Armendariz GA, De la Torre Othón SA, Sierra Cervantes MR, Medina Aguirre JA. Clinical simulation with ChatGPT: a revolution in medical education?. J CME 2025; 14 (01) 2525615
    Search in Google Scholar
  • 49 Cabrera D, Vartabedian BS, Spinner RJ, Jordan BL, Aase LA, Timimi FK. More than likes and tweets: creating social media portfolios for academic promotion and tenure. J Grad Med Educ 2017; 9 (04) 421-425
    Search in Google Scholar
  • 50 Husain A, Repanshek Z, Singh M. et al. Consensus guidelines for digital scholarship in academic promotion. West J Emerg Med 2020; 21 (04) 883-891
    Search in Google Scholar
  • 51 Thoma B, Sanders JL, Lin M, Paterson QS, Steeg J, Chan TM. The social media index: measuring the impact of emergency medicine and critical care websites. West J Emerg Med 2015; 16 (02) 242-249
    Search in Google Scholar
  • 52 Acquaviva KD, Mugele J, Abadilla N. et al. Documenting social media engagement as scholarship: a new model for assessing academic accomplishment for the health professions. J Med Internet Res 2020; 22 (12) e25070
    Search in Google Scholar
  • 53 Cabrera D, Roy D, Chisolm MS. Social media scholarship and alternative metrics for academic promotion and tenure. J Am Coll Radiol 2018; 15 (1 Pt B): 135-141
    Search in Google Scholar