Applying Generative AI to the Learning Analytics Cycle to Support Competency Development in Health Professions Education
DOI:
https://doi.org/10.18608/jla.2026.9145Keywords:
professional learning analytics, genAI, healthcare, self-regulated learning, informal learning, research paperAbstract
Professional learning environments offer a unique opportunity for exploring professional competencies due to the use of workplace data, which captures important aspects of professional performance. This data is different from that used in formal learning analytics contexts, as it is not collected by systems designed to understand learning. Instead, workplace data represents the digital fingerprint professionals leave behind when interacting with technologies to do their jobs. Harnessing this data to support workplace learning is challenging for a range of reasons, including being able to identify meaningful metrics to identify individual performance and scaffold the use of this data to support professional learning interventions on knowledge and performance. Generative AI (GenAI) has great potential to enhance professional learning analytics by addressing some of the challenges inherent in using workplace data. These challenges include needing to process large amounts of unstructured data to understand individual performance and transform this data into interfaces and interventions that can support learning. In our paper, we modify Clow’s learning analytics cycle to inform a modified framework describing the intersection of learning analytics with professional learning. Subsequently, we illustrate the potential power of GenAI for supporting professional learning across the framework through three case studies in health professions education.
References
Aghaei Hashjin, A., Ravaghi, H., Kringos, D. S., Ogbu, U. C., Fischer, C., Azami, S. R., & Klazinga, N. S. (2014). Using quality measures for quality improvement: The perspective of hospital staff. PloS One, 9(1), e86014. https://doi.org/10.1371/journal.pone.0086014
Allen, L. M., Balmer, D., & Varpio, L. (2024). Physicians’ lifelong learning journeys: A narrative analysis of continuing professional development struggles. Medical Education, 58(9), 1086–1096. https://doi.org/10.1111/medu.15375
Almansour, M., & Alfhaid, F. M. (2024). Generative artificial intelligence and the personalization of health professional education: A narrative review. Medicine, 103(31). https://doi.org/10.1097/MD.0000000000038955
AlSaad, R., Abd-Alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024). Multimodal large language models in health care: Applications, challenges, and future outlook. Journal of Medical Internet Research, 26, e59505. https://doi.org/10.2196/59505
Arain, S. A., Akhund, S. A., Barakzai, M. A., & Meo, S. A. (2025). Transforming medical education: Leveraging large language models to enhance PBL—a proof-of-concept study [PMID: 39918742]. Advances in Physiology Education, 49(2), 398–404. https://doi.org/10.1152/advan.00209.2024
Australian Government Department of Health, Disability, and Ageing. (2025, March). Hospital Casemix Protocol (HCP) data. Retrieved June 4, 2025, from https://www.health.gov.au/topics/hospital-care/our-role/hcp-data
Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458. https://doi.org/10.1177/1073191116648209
Biswas, A., & Talukdar, W. (2024). Intelligent clinical documentation: Harnessing generative AI for patient-centric clinical note generation. arXiv preprint arXiv:2405.18346. https://doi.org/10.48550/arXiv.2405.18346
Bojic, I., Mammadova, M., Ang, C. - S., Teo, W. L. T., Diordieva, C., Pienkowska, A., Gasevic, D., & Car, J. (2023). Empowering health care education through learning analytics: In-depth scoping review. Journal of Medical Internet Research, 25, e41671. https://doi.org/10.2196/41671
Bourne, D., & Jankowicz, D. A. (2017). The repertory grid technique. In M. Ciesielska & D. Jemielniak (Eds.), Qualitative methodologies in organization studies: Volume II: Methods and possibilities (pp. 127–149). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-65442-3_6
Bragazzi, N. L., & Garbarino, S. (2024). Toward clinical generative AI: Conceptual framework. JMIR AI, 3(1), e55957. https://doi.org/10.2196/55957
Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 3–53). Springer. https://doi.org/10.1007/978-3-540-72079-9_1
Bucalon, B., Whitelock-Wainwright, E., Williams, C., Conley, J., Veysey, M., Kay, J., & Shaw, T. (2023). Thought leader perspectives on the benefits, barriers, and enablers for routinely collected electronic health data to support professional development: Qualitative study. Journal of Medical Internet Research, 25, e40685. https://doi.org/10.2196/40685
Bucalon, B., Williams, C., Conley, J., Rankin, D., Veysey, M., Shaw, T., & Kay, J. (2022). ”You can’t improve until you measure”: A need finding study on repurposed clinical indicators for professional learning. In P. Sweetser, J. L. Taylor, C. Martin, D. Mckay, M. Rogerson, B. Cumbo, G. Wadley, L. Hespanhol, J. Tsimeris, M. Xi, J. Turner, S. Yoo, N. Cooper, J. Rahman, J. Andres, A. G. Pillai, & C. Kutay (Eds.), 34th Australian Conference on Human-Computer Interaction (OzCHI 2022), 29 November–2 December 2022, Canberra, Australia (pp. 172–179). ACM. https://doi.org/10.1145/3572921.3572952
Bucalon, B., Williams, C., Conley, J., Veysey, M., Shaw, T., & Kay, J. (2025). Towards clinical practice reflection based on repurposed administrative data: Qualitative study of physicians and surgeons. In N. Yamashita, V. Evers, K. Yatani, & X. Ding (Eds.), Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA 2025), 26 April–1 May 2025, Yokohama, Japan. ACM. https://doi.org/10.1145/3706599.3719822
Campos, F. C., Ahn, J., DiGiacomo, D. K., Nguyen, H., & Hays, M. (2021). Making sense of sensemaking: Understanding how K–12 teachers and coaches react to visual analytics. Journal of Learning Analytics, 8(3), 60–80. https://doi.org/10.18608/jla.2021.7113
Chaiyachati, K. H., Shea, J. A., Asch, D. A., Liu, M., Bellini, L. M., Dine, C. J., Sternberg, A. L., Gitelman, Y., Yeager, A. M., Asch, J. M., & Desai, S. V. (2019). Assessment of inpatient time allocation among first-year internal medicine residents using time-motion observations. JAMA Internal Medicine, 179(6), 760–767. https://doi.org/10.1001/jamainternmed.2019.0095
Chan, A. K., Botelho, M. G., & Lam, O. L. (2019). Use of learning analytics data in health care–related educational disciplines: Systematic review. Journal of Medical Internet Research, 21(2), e11241. https://doi.org/10.2196/11241
Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis. Journal of Medical Internet Research, 25, e47184. https://doi.org/10.2196/47184
Chua, C. E., Clara, N. L. Y., Furqan, M. S., Kit, J. L. W., Makmur, A., Tham, Y. C., Santosa, A., & Ngiam, K. Y. (2024). Integration of customised LLM for discharge summary generation in real-world clinical settings: A pilot study on RUSSELL GPT. The Lancet Regional Health—Western Pacific, 51. https://doi.org/10.1016/j.lanwpc.2024.101211
Cirkony, C., Rickinson, M., Walsh, L., Gleeson, J., Salisbury, M., Cutler, B., Berry, M., & Smith, K. (2024). Beyond effective approaches: A rapid review response to designing professional learning. Professional Development in Education, 50(1), 24–45. https://doi.org/10.1080/19415257.2021.1973075
Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the Second International Conference on Learning Analytics and Knowledge (LAK 2012), 29 April–2 May 2012, Vancouver, British Columbia, Canada (pp. 134–138). ACM. https://doi.org/10.1145/2330601.2330636
Coggins, A., Zaklama, R., Szabo, R. A., Diaz-Navarro, C., Scalese, R. J., Krogh, K., & Eppich, W. (2021). Twelve tips for facilitating and implementing clinical debriefing programmes. Medical Teacher, 43(5), 509–517. https://doi.org/10.1080/0142159X.2020.1817349
Cohen, B., DuBois, S., Lynch, P. A., Swami, N., Noftle, K., & Arensberg, M. B. (2023). Use of an artificial intelligence-driven digital platform for reflective learning to support continuing medical and professional education and opportunities for interprofessional education and equitable access. Education Sciences, 13(8), 760. https://doi.org/10.3390/educsci13080760
Cole, M. (2000). Learning through reflective practice: A professional approach to effective continuing professional development among healthcare professionals. Research in Post-Compulsory Education, 5(1), 23–38. https://doi.org/10.1080/13596740000200067
Cordero-Guevara, J. A., Parraza-Diez, N., Vrotsou, K., Machon, M., Orruno, E., Onaindia-Ecenarro, M. J., Millet-Sampedro, M., & Regalado de los Cobos, J. (2022). Factors associated with the workload of health professionals in hospital at home: A systematic review. BMC Health Services Research, 22(1), 704. https://doi.org/10.1186/s12913-022-08100-4
Dawson, S. (2020). Learning analytics—A field on the verge of relevance? Keynote address at the 10th International Conference on Learning Analytics and Knowledge (LAK 2020), 23–27 March 2020, Frankfurt, Germany. https://www.youtube.com/watch?v=StN1fY7ckZE
Dennerlein, S., Rella, M., Tomberg, V., Theiler, D., Treasure-Jones, T., Kerr, M., Ley, T., Al-Smadi, M., & Trattner, C. (2014). Making sense of bits and pieces: A sensemaking tool for informal workplace learning. In C. Rensing, S. de Freitas, T. Ley, & P. Munoz-Merino (Eds.), Open learning and teaching in educational communities. EC-TEL 2014. Lecture notes in computer science (pp. 391–397, Vol. 8719). Springer. https://doi.org/10.1007/978-3-319-11200-8_31
Dickerson, J. E. (2023). Clinical audit, quality improvement and data quality. Anaesthesia & Intensive Care Medicine, 24(8), 486–489. https://doi.org/10.1016/j.mpaic.2023.05.005
Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., Hamer, S., Whitewood-Moores, Z., Hardiker, N., Borycki, E., & Currie, L. (2015). Dashboards for improving patient care: Review of the literature. International Journal of Medical Informatics, 84(2), 87–100. https://doi.org/10.1016/j.ijmedinf.2014.10.001
Doyle, P. R., Clark, L., & Cowan, B. R. (2021). What do we see in them? Identifying dimensions of partner models for speech interfaces using a psycholexical approach. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021), 8–13 May 2021, Yokohama, Japan. ACM. https://doi.org/10.1145/3411764.3445206
DunnGalvin, A., Cooper, J. B., Shorten, G., & Blum, R. H. (2019). Applied reflective practice in medicine and anaesthesiology. British Journal of Anaesthesia, 122(5), 536–541. https://doi.org/10.1016/j.bja.2019.02.006
Eraut, M. (2004). Informal learning in the workplace. Studies in Continuing Education, 26(2), 247–273. https://doi.org/10.1080/158037042000225245
Fransella, F., Bell, R., & Bannister, D. (2004). A manual for repertory grid technique. John Wiley & Sons. García-Mieres, H., Niño-Robles, N., Ochoa, S., & Feixas, G. (2019). Exploring identity and personal meanings in psychosis using the repertory grid technique: A systematic review. Clinical Psychology & Psychotherapy, 26(6), 717–733. https://doi.org/10.1002/cpp.2394
Gibbons, P. (2002). Scaffolding language, scaffolding learning. Heinemann.
Gu, B., Shao, V., Liao, Z., Carducci, V., Brufau, S. R., Yang, J., & Desai, R. J. (2025). Scalable information extraction from free text electronic health records using large language models. BMC Medical Research Methodology, 25(1), 23. https://doi.org/10.1186/s12874-025-02470-z
Hadley, G., & Grogan, M. (2022). Using repertory grids as a tool for mixed methods research: A critical assessment. Journal of Mixed Methods Research, 17(2), 209–227. https://doi.org/10.1177/15586898221077569
Hoagland, D. L. A., Jonathan. (2025). Self-learning and simulation in the 21st century: From textbooks to ChatGPT. Clinics in Colon and Rectal Surgery, 39(4), 296–302. https://doi.org/10.1055/s-0045-1813685
Hong, E., Kazmir, S., Dylik, B., Auerbach, M., Rosati, M., Athanasopoulou, S., Himmelstein, R., Whitfill, T. M., Johnston, L., Wolbrink, T. A., Rosen, A. S., & Gross, I. T. (2025). Exploring the use of a large language model in simulation debriefing: An observational simulation-based pilot study. Simulation in Healthcare, 20(6). https://doi.org/10.1097/SIH.0000000000000861
Hough, J., Culley, N., Erganian, C., & Alahdab, F. (2025). Potential risks of GenAI on medical education. BMJ Evidence-Based Medicine, 30(6), 406–408. https://doi.org/10.1136/bmjebm-2025-114339
Janssen, A., Coggins, A., Tadros, J., Quinn, D., Shetty, A., & Shaw, T. (2025). Using electronic health data to deliver an adaptive online learning solution to emergency trainees: Mixed methods pilot study. JMIR Medical Education, 11, e65287. https://doi.org/10.2196/65287
Janssen, A., Donnelly, C., Murphy, A. D., Trinh, B., Moujaber, T., Shah, K., Harnett, P., & Shaw, T. (2024). Feasibility of personalised online learning programs aligned with authentic workplace practice. Health Education in Practice: Journal of Research for Professional Learning, 7(1), 1–15. https://doi.org/10.33966/hepj.7.1.18088
Janssen, A., Donnelly, C., & Shaw, T. (2024). A taxonomy for health information systems. Journal of Medical Internet Research, 26, e47682. https://doi.org/10.2196/47682
Janssen, A., Kay, J., Talic, S., Pusic, M., Birnbaum, R. J., Cavalcanti, R., Gašević, D., & Shaw, T. (2022). Electronic health records that support health professional reflective practice: A missed opportunity in digital health. Journal of Healthcare Informatics Research, 6(4), 375–384. https://doi.org/10.1007/s41666-022-00123-0
Janssen, A., Shah, K., Keep, M., & Shaw, T. (2024). Community perspectives on the use of electronic health data to support reflective practice by health professionals. BMC Medical Informatics and Decision Making, 24(1), 226. https://doi.org/10.1186/s12911-024-02626-9
Janssen, A., & Shaw, T. (2022). The attitudes of medical practitioners towards the actionability of performance data. International Journal of Electronic Healthcare, 12(3), 191–202. https://doi.org/10.1504/IJEH.2022.124492
Janssen, A., Talic, S., Gašević, D., Kay, J., & Shaw, T. (2021). Exploring the intersection between health professionals’ learning and eHealth data: Protocol for a comprehensive research program in practice analytics in health care. JMIR Research Protocols, 10(12), e27984. https://doi.org/10.2196/27984
Jin, Y., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2025). GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 9, 100436. https://doi.org/10.1016/j.caeai.2025.100436
Jin, Y., Yang, K., Yan, L., Echeverria, V., Zhao, L., Alfredo, R., Milesi, M., Fan, J. X., Li, X., Gašević, D., & Martinez-Maldonado, R. (2025). Chatting with a learning analytics dashboard: The role of generative AI literacy on learner interaction with conventional and scaffolding chatbots. In Proceedings of the 15th International Conference on Learning Analytics and Knowledge (LAK 2025), 3–7 March 2025, Dublin, Ireland (pp. 579–590). ACM. https://doi.org/10.1145/3706468.3706545
Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. In Proceedings of the Eighth International Conference on Learning Analytics and Knowledge (LAK 2018), 7–9 March 2018, Sydney, Australia (pp. 31–40). ACM. https://doi.org/10.1145/3170358.3170421
Joynes, V., Kerr, M., & Treasure-Jones, T. (2017). Exploring informal workplace learning in primary healthcare for continuous professional development. Education for Primary Care, 28(4), 216–222. https://doi.org/10.1080/14739879.2017.1298405
Kaliisa, R., Misiejuk, K., López-Pernas, S., Khalil, M., & Saqr, M. (2024). Have learning analytics dashboards lived up to the hype? A systematic review of impact on students’ achievement, motivation, participation and attitude. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Tokyo, Japan (pp. 295–304). ACM. https://doi.org/10.1145/3636555.3636884
Karas, M., Sheen, N. J., North, R. V., Ryan, B., & Bullock, A. (2020). Continuing professional development requirements for UK health professionals: A scoping review. BMJ Open, 10(3), e032781. https://doi.org/10.1136/bmjopen-2019-032781
Khairat, S. S., Dukkipati, A., Lauria, H. A., Bice, T., Travers, D., & Carson, S. S. (2018). The impact of visualization dashboards on quality of care and clinician satisfaction: Integrative literature review. JMIR Human Factors, 5(2), e9328. https://doi.org/10.2196/humanfactors.9328
Khosravi, H., Shibani, A., Jovanovic, J., Pardos, Z. A., & Yan, L. (2025). Generative AI and learning analytics: Pushing boundaries, preserving principles. Journal of Learning Analytics, 12(1), 1–11. https://doi.org/10.18608/jla.2025.8961
Khousa, E. A., Atif, Y., & Masud, M. M. (2015). A social learning analytics approach to cognitive apprenticeship. Smart Learning Environments, 2, 1–23. https://doi.org/10.1186/s40561-015-0021-z
Kim, N. J., Belland, B. R., & Walker, A. E. (2018). Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educational Psychology Review, 30, 397–429. https://doi.org/10.1007/s10648-017-9419-1
Kooken, J., Ley, T., & De Hoog, R. (2007). How do people learn at the workplace? Investigating four workplace learning assumptions. In E. Duval, R. Klamma, & M. Wolpers (Eds.), Creating new learning experiences on a global scale. EC-TEL 2007. Lecture notes in computer science (pp. 158–171, Vol. 4753). Springer. https://doi.org/10.1007/978-3-540-75195-3_12
Kump, B., Seifert, C., Beham, G., Lindstaedt, S. N., & Ley, T. (2012). Seeing what the system thinks you know: Visualizing evidence in an open learner model. In Proceedings of the Second International Conference on Learning Analytics and Knowledge (LAK 2012), 29 April–2 May 2012, Vancouver, British Columbia, Canada (pp. 153–157). ACM. https://doi.org/10.1145/2330601.2330640
Lang, C., Wise, A. F., Merceron, A., Gašević, D., & Siemens, G. (2022). What is learning analytics? In The handbook of learning analytics (pp. 8–18). Society for Learning Analytics Research. https://doi.org/10.18608/hla22
Leach, C., Freshwater, K., Aldridge, J., & Sunderland, J. (2001). Analysis of repertory grids in clinical practice. British Journal of Clinical Psychology, 40(3), 225–248. https://doi.org/10.1348/014466501163652
Li, H., Wang, Y., & Qu, H. (2024). Where are we so far? Understanding data storytelling tools from the perspective of human AI collaboration. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024), 11–16 May 2024, Honolulu, Hawaii, USA (pp. 1–19). ACM. https://doi.org/10.1145/3613904.3642726
Littlejohn, A., & Margaryan, A. (2014). Technology-enhanced professional learning. In S. Billett, C. Harteis, & H. Gruber (Eds.), International handbook of research in professional and practice-based learning (pp. 1187–1212). Springer. https://doi.org/10.1007/978-94-017-8902-8_43
Medical Board of Australia. (2019). Building a professional performance framework. https://www.medicalboard.gov.au/Professional-Performance-Framework.aspx
Mesko, B., & Topol, E. J. (2023). The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine, 6(1), 120. https://doi.org/10.1038/s41746-023-00873-0
Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2025). Mapping the landscape of generative artificial intelligence in learning analytics: A systematic literature review. Journal of Learning Analytics, 12(1), 12–31. https://doi.org/10.18608/jla.2025.8591
Moen, R. D., & Norman, C. L. (2010). Circling back. Quality Progress, 43(11), 22–28.
Molenaar, I., & Knoop-van Campen, C. A. N. (2019). How teachers make dashboard information actionable. IEEE Transactions on Learning Technology, 12(3), 347–355. https://doi.org/10.1109/TLT.2018.2851585
Monib, W. K., Qazi, A., & Apong, R. A. (2025). Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes. Heliyon, 11(2), e41413. https://doi.org/10.1016/j.heliyon.2024.e41413
Moore, N., Ahmadpour, N., Brown, M., Poronnik, P., & Davids, J. (2022). Designing virtual reality experiences to supplement clinician Code Black education. International Journal of Healthcare Simulation, 1(Supplement SRSIS 1), S12–S14. https://doi.org/10.54531/DNZC8446
Pammer, V., Krogstie, B., & Prilla, M. (2017). Let’s talk about reflection at work. International Journal of Technology Enhanced Learning, 9(2-3), 151–168. https://doi.org/10.1504/IJTEL.2017.084493
Pandey, S., & Ottley, A. (2023). Mini-VLAT: A short and effective measure of visualization literacy. Computer Graphics Forum, 42(3), 1–11. https://doi.org/10.1111/cgf.14809
Pethani, F., Chapman, A., Conway, M., Dai, X., Bishay, D., Choh, V. J. X., He, A., Lim, S.- E., Ng, H. Y., Mahony, T., Yaacoub, A., Karimi, S., Spallek, H., & Dunn, A. G. (2025). Extracting social determinants of health from dental clinical notes. Applied Clinical Informatics, 16(4), 1281–1291. https://doi.org/10.1055/a-2616-9858
Phillips, J. L., Heneka, N., Bhattarai, P., Fraser, C., & Shaw, T. (2019). Effectiveness of the spaced education pedagogy for clinicians’ continuing professional development: A systematic review. Medical Education, 53(9), 886–902. https://doi.org/10.1111/medu.13895
Pizzuti, C. (2024). Using eHealth data to strengthen continuing professional development (CPD) for medical practitioners. An exploration of regulatory and organisational factors influencing eHealth data analytics implementations within the CPD ecosystem [Doctoral dissertation, The University of Sydney].
Pozdniakov, S., Brazil, J., Mohammadi, M., Dollinger, M., Sadiq, S., & Khosravi, H. (2025). AI-assisted co-creation: Bridging skill gaps in student-generated content. Journal of Learning Analytics, 12(1), 129–151. https://doi.org/10.18608/jla.2025.8601
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gasevic, D. (2023). How do teachers use dashboards enhanced with data storytelling elements according to their data visualisation literacy skills? In Proceedings of the 13th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, Texas, USA (pp. 89–99). ACM. https://doi.org/10.1145/3576050.3576063
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.- S., Srivastava, N., Liu, Y., & Gasevic, D. (2023). Single or multi-page learning analytics dashboards? relationships between teachers’ cognitive load and visualisation literacy. In O. Viberg, I. Jivet, P. Muñoz-Merino, M. Perifanou, & T. Papathoma (Eds.), Responsive and sustainable educational futures. EC-TEL 2023. Lecture notes in computer science (pp. 339–355, Vol. 14200). Springer. https://doi.org/10.1007/978-3-031-42682-7_23
Preiksaitis, C., & Rose, C. (2023). Opportunities, challenges, and future directions of generative artificial intelligence in medical education: Scoping review. JMIR Medical Education, 9, e48785. https://doi.org/10.2196/48785
Pusic, M. V., Birnbaum, R. J., Thoma, B., Hamstra, S. J., Cavalcanti, R. B., Warm, E. J., Janssen, A., & Shaw, T. (2023). Frameworks for integrating learning analytics with the electronic health record. Journal of Continuing Education in the Health Professions, 43(1), 52–59. https://doi.org/10.1097/CEH.0000000000000444
Rashid, C. (2016). Using clinical audit to reflect and revalidate. Nursing Times, 112(12), 9–11. https://www.nursingtimes.net/education-and-training/using-clinical-audit-to-reflect-and-revalidate-03-10-2016/
Roberts, L. J., Jayasena, R., Khanna, S., Arnott, L., Lane, P., & Bain, C. (2025). Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare. Internal Medicine Journal, 55(7), 1063–1069. https://doi.org/10.1111/imj.70035
Robinson, T., Janssen, A., Kirk, J., DeFazio, A., Goodwin, A., Tucker, K., & Shaw, T. (2017). New approaches to continuing medical education: A QStream (spaced education) program for research translation in ovarian cancer. Journal of Cancer Education, 32, 476–482. https://doi.org/10.1007/s13187-015-0944-7
Rozenszajn, R., Kavod, G. Z., & Machluf, Y. (2021). What do they really think? The repertory grid technique as an educational research tool for revealing tacit cognitive structures. International Journal of Science Education, 43(6), 906–927. https://doi.org/10.1080/09500693.2021.1891323
Ruiz-Calleja, A., Prieto, L. P., Ley, T., Rodríguez-Triana, M. J., & Dennerlein, S. (2021). Learning analytics for professional and workplace learning: A literature review. IEEE Transactions on Learning Technologies, 14(3), 353–366. https://doi.org/10.1109/TLT.2021.3092219
Ryan, G., Lyon, P., Kumar, K., Bell, J., Barnet, S., & Shaw, T. (2007). Online CME: an effective alternative to face-to-face delivery. Medical Teacher, 29(8), e251–e257. https://doi.org/10.1080/01421590701551698
Sailer, M., Ninaus, M., Huber, S. E., Bauer, E., & Greiff, S. (2024). The end is the beginning is the end: The closed-loop learning analytics framework. Computers in Human Behavior, 158, 108305. https://doi.org/10.1016/j.chb.2024.108305
Samuelsen, J., Chen, W., & Wasson, B. (2019). Integrating multiple data sources for learning analytics—review of literature. Research and Practice in Technology Enhanced Learning, 14(1), 11. https://doi.org/10.1186/s41039-019-0105-4
Saqr, M., & López-Pernas, S. (2021). Idiographic learning analytics: A single student (n=1) approach using psychological networks. In O. Poquet, B. Chen, M. Saqr, & T. Hecking (Eds.), Proceedings of the NetSciLA2021 Workshop “Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda” (NetSciLA 2021), 12 April 2021, Newport Beach, California, USA (pp. 16–22). CEUR. http://ceur-ws.org/Vol-2868/#article_4
Sargeant, J., Bruce, D., & Campbell, C. M. (2013). Practicing physicians’ needs for assessment and feedback as part of professional development. Journal of Continuing Education in the Health Professions, 33(S1), S54–S62. https://doi.org/10.1002/chp.21202
Schofield, P., Shaw, T., & Pascoe, M. (2019). Toward comprehensive patient-centric care by integrating digital health technology with direct clinical contact in Australia. Journal of Medical Internet Research, 21(6), e12382. https://doi.org/10.2196/12382
Schon, D. (1992). The reflective practitioner: How professionals think in action. Routledge. https://doi.org/10.4324/9781315237473
Scott, I. A., Reddy, S., Kelly, T., Miller, T., & van der Vegt, A. (2025). Using generative artificial intelligence in clinical practice: A narrative review and proposed agenda for implementation. Medical Journal of Australia, 223(11), 664–672. https://doi.org/10.5694/mja2.70057
Shaw, T., Janssen, A., Barnet, S., Nicholson, J., Avery, J., Henenka, N., & Phillips, J. (2018). The CASE methodology: A guide to developing clinically authentic case-based scenarios for online learning programs targeting evidence-based practice. Health Education in Practice: Journal of Research for Professional Learning, 1(1), 18–31. https://doi.org/10.33966/hepj.1.1.12591
Shaw, T., Janssen, A., Crampton, R., O’Leary, F., Hoyle, P., Jones, A., Shetty, A., Gunja, N., Ritchie, A. G., Spallek, H., Solman, A., Kay, J., Makeham, M. A., & Harnett, P. (2019). Attitudes of health professionals to using routinely collected clinical data for performance feedback and personalised professional development. Medical Journal of Australia, 210, S17–S21. https://doi.org/10.5694/mja2.50022
Siadaty, M., Gašević, D., & Hatala, M. (2016a). Associations between technological scaffolding and micro-level processes of self-regulated learning: A workplace study. Computers in Human Behavior, 55, 1007–1019. https://doi.org/10.1016/j.chb.2015.10.035
Siadaty, M., Gašević, D., & Hatala, M. (2016b). Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning. Computers in Human Behavior, 59, 469–482. https://doi.org/10.1016/j.chb.2016.02.025
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, Z., Ali, L., Giljanović, A., & Hatala, M. (2012). Learn-B: A social analytics-enabled tool for self-regulated workplace learning. In Proceedings of the Second International Conference on Learning Analytics and Knowledge (LAK 2012), 29 April–2 May 2012, Vancouver, British Columbia, Canada (pp. 115–119). ACM. https://doi.org/10.1145/2330601.2330632
Siemens, G., & Gašević, D. (2012). Guest editorial—learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 1–2. https://drive.google.com/file/d/1SJQZSFOrix9WZTvBtzvUL70bsLa_eqQ/view?pli=1
Singer, S. J., Benzer, J. K., & and, S. U. H. (2015). Improving health care quality and safety: The role of collective learning. Journal of Healthcare Leadership, 7, 91–107. https://doi.org/10.2147/JHL.S70115
Tavares, W., Sockalingam, S., Valanci, S., Giuliani, M., Davis, D., Campbell, C., Silver, I., Charow, R., Jeyakumar, T., Younus, S., & Wiljer, D. (2024). Performance data advocacy for continuing professional development in health professions. Academic Medicine, 99(2), 153–158. https://doi.org/10.1097/ACM.0000000000005490
van Leeuwen, A., Knoop-van Campen, C. A., Molenaar, I., & Rummel, N. (2021). How teacher characteristics relate to how teachers use dashboards: Results from two case studies in K-12. Journal of Learning Analytics, 8(2), 6–21. https://doi.org/10.18608/jla.2021.7325
Van De Wiel, M. W., Van den Bossche, P., Janssen, S., & Jossberger, H. (2011). Exploring deliberate practice in medicine: How do physicians learn in the workplace? Advances in Health Sciences Education, 16, 81–95. https://doi.org/10.1007/s10459-010-9246-3
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK 2020), 23–27 March 2020, Frankfurt, Germany (pp. 35–40). ACM. https://doi.org/10.1145/3375462.3375504
Wang, M., Pantell, M. S., Gottlieb, L. M., & Adler-Milstein, J. (2021). Documentation and review of social determinants of health data in the EHR: Measures and associated insights. Journal of the American Medical Informatics Association, 28(12), 2608–2616. https://doi.org/10.1093/jamia/ocab194
Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16(4), 409–421. https://doi.org/10.1287/orsc.1050.0133
Westbrook, J. I., Ampt, A., Kearney, L., & Rob, M. I. (2008). All in a day’s work: An observational study to quantify how and with whom doctors on hospital wards spend their time. Medical Journal of Australia, 188(9), 506–509. https://doi.org/10.5694/j.1326-5377.2008.tb02159.x
Whitelock-Wainwright, E., Koh, J. W., Whitelock-Wainwright, A., Talic, S., Rankin, D., & Gašević, D. (2022). An exploration into physician and surgeon data sensemaking: A qualitative systematic review using thematic synthesis. BMC Medical Informatics and Decision Making, 22, 20. https://doi.org/10.1186/s12911-022-01997-1
Whitelock-Wainwright, E., Rankin, D., Talic, S., & Gašević, D. (2024). A mixed-method case study: Medical practitioner sensemaking in the context of practice analytics. In F. F. Mueller, P. Kyburz, J. R. Williamson, & C. Sas (Eds.), Extended abstracts of the CHI Conference on Human Factors in Computing Systems (CHI-EA 2024), 11–16 May 2024, Honolulu, Hawaii, USA. ACM. https://doi.org/10.1145/3613905.3637120
Wiljer, D., Tavares, W., Charow, R., Williams, S., Campbell, C., Davis, D., Jeyakumar, T., Mylopoulos, M., Okrainec, A., Silver, I., & Sockalingam, S. (2023). A qualitative study to understand the cultural factors that influence clinical data use for continuing professional development. The Journal of Continuing Education in the Health Professions, 43(1), 34–41. https://doi.org/10.1097/CEH.0000000000000423
Womack-Adams, K., Morbitzer, K. A., Ondek, C., Collins, H., & McLaughlin, J. E. (2025). A review of microcredentials in health professions continuing professional development. Frontiers in Medicine, 12, 1532811. https://doi.org/10.3389/fmed.2025.1532811
Wornow, M., Xu, Y., Thapa, R., Patel, B., Steinberg, E., Fleming, S., Pfeffer, M. A., Fries, J., & Shah, N. H. (2023). The shaky foundations of large language models and foundation models for electronic health records. npj Digital Medicine, 6(1), 135. https://doi.org/10.1038/s41746-023-00879-8
Xie, C. X., Chen, Q., Hincapié, C. A., Hofstetter, L., Maher, C. G., & Machado, G. C. (2022). Effectiveness of clinical dashboards as audit and feedback or clinical decision support tools on medication use and test ordering: A systematic review of randomized controlled trials. Journal of the American Medical Informatics Association, 29(10), 1773–1785. https://doi.org/10.1093/jamia/ocac094
Xu, R., & Wang, Z. (2024). Generative artificial intelligence in healthcare from the perspective of digital media: Applications, opportunities and challenges. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32364
Yan, L., Echeverria, V., Jin, Y., Fernandez-Nieto, G., Zhao, L., Li, X., Alfredo, R., Swiecki, Z., Gašević, D., & Martinez- Maldonado, R. (2024). Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning. British Journal of Educational Technology, 55(5), 1900–1925. https://doi.org/10.1111/bjet.13498
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
Yan, L., Martinez-Maldonado, R., & Gasevic, D. (2024). Generative artificial intelligence in learning analytics: Contextualising opportunities and challenges through the learning analytics cycle. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Tokyo, Japan (pp. 101–111). ACM. https://doi.org/10.1145/3636555.3636856
Yan, L., Martinez-Maldonado, R., Jin, Y., Echeverria, V., Milesi, M., Fan, J., Zhao, L., Alfredo, R., Li, X., & Gašević, D. (2025). The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics. Computers & Education, 105322. https://doi.org/10.1016/j.compedu.2025.105322
Yan, L., Martinez-Maldonado, R., Zhao, L., Dix, S., Jaggard, H., Wotherspoon, R., Li, X., & Gašević, D. (2023). The role of indoor positioning analytics in assessment of simulation-based learning. British Journal of Educational Technology, 54(1), 267–292. https://doi.org/10.1111/bjet.13262
Yan, L., Zhao, L., Echeverria, V., Jin, Y., Alfredo, R., Li, X., Gašević, D., & Martinez-Maldonado, R. (2024). VizChat: Enhancing learning analytics dashboards with contextualised explanations using multimodal generative AI chatbots. In A. Olney, I. Chounta, Z. Liu, O. Santos, & I. Bittencourt (Eds.), Artificial intelligence in education. AIED 2024. Lecture notes in computer science (pp. 180–193, Vol. 14830). Springer. https://doi.org/10.1007/978-3-031-64299-9_13
Yanagita, Y., Yokokawa, D., Ihara, S., Yoshida, R., Okano, Y., & Uehara, T. (2025). Quality assessment of large language model–generated medical dialogue for clinical vignettes: Evaluation study. JMIR Formative Research, 9, e80752. https://doi.org/10.2196/80752
Yoon, J., Drumright, L. N., & Van Der Schaar, M. (2020). Anonymization through data synthesis using generative adversarial networks (ADS-GAN). IEEE Journal of Biomedical and Health Informatics, 24(8), 2378–2388. https://doi.org/10.1109/JBHI.2020.2980262
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