Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience
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Background and objective In this paper we propose to include an intelligent tutoring system (ITS) within a magnetic resonance (MR) simulator that has been developed in house. With this, we intend to measure the impact, in terms of user experience, of including an ITS in our simulator. Methods We thoroughly describe the integration procedure and we have tested the benefits of this integration by means of two actual educational experiences, with one of them using the simulator as a standalone tool, and the other with the joint use of simulator+ITS. The experiences have consisted of two online courses with a number of students around 180 in both of them, where measurements of usability, perceived utility and likelihood to recommend were collected. Results We have observed that the three measurements improved noticeably in the second course with respect to the first one; specifically, overall usability improved by 22.3%, perceived utility by an average of 55.1% and likelihood to recommend by 13.7%. In addition, quantitative measurements are complemented with comments in free text format directly provided by the students. Results show evidence on the benefits of integrating an ITS in terms of quantitative user experience, as well as qualitative comparative comments directly by students of both courses. Conclusions This is the first time that an ITS is used within the scope of MR simulation for training purposes. Benefits of integrating an ITS within an MR simulator have been evalu…
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Immediate feedback is an important factor in promoting effective learning, as it allows students to correct misconceptions and reinforce correct understanding during the learning process.
“Immediate feedback has been widely recognized as an important factor in promoting effective learning, as it allows students to correct misconceptions and reinforce correct understanding during the learning process [28, 34].”
Cites
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
The Factor Structure of the System Usability Scale
Lewis, J. R., & Sauro, J. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-02806-9_12 · Cited by 1,278 · see citing works
AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction
Carbonell, J. (1970) · IEEE Transactions on Man Machine Systems · doi:10.1109/tmms.1970.299942 · Cited by 955 · see citing works
Intelligent tutoring systems and learning outcomes: A meta-analysis
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014) · Journal of Educational Psychology · doi:10.1037/a0037123 · Cited by 840 · see citing works
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2018.1558257 · Cited by 605 · see citing works
Effectiveness of Cognitive Tutor Algebra I at Scale
Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2014) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373713507480 · Cited by 317 · see citing works
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., & Tai, M. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0023-y · Cited by 263 · see citing works
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Artificial Intelligence developments in medical education: a conceptual and practical framework
Masters, K. (2020) · MedEdPublish · doi:10.15694/mep.2020.000239.1 · Cited by 25 · see citing works
Zhao, C., Zhu, J., Liu, J., Zhao, W., & Pang, Y. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09469-0 · Cited by 1 · see citing works
“Immediate feedback has been widely recognized as an important factor in promoting effective learning, as it allows students to correct misconceptions and reinforce correct understanding during the learning process [28, 34].”
- Authors
- Treceño-Fernández, D., Calabia-del-Campo, J., Bote-Lorenzo, M. L., Gómez-Sánchez, E., Luis-García, R. D., & Alberola-López, C.
- Affiliations
- Universidad de Valladolid (ES)
- Hospital Clínico Universitario de Valladolid (ES)
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 195
- Pages
- 105634
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W3039428972
- Cited by
- 12
- References
- 7
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 3 · 2021: 2 · 2020: 1
- FWCI
- 0.59
- Type
- journal article
- Language
- en
- Open access
- green
- uvadoc.uva.es
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Hispana (submittedVersion)
- UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- intelligent tutoring systems, medical education, user experience, usability, perceived usefulness, likelihood to recommend, simulator-based training, online courses
- MeSH terms
- Clinical Competence, Computer Simulation, Feasibility Studies, Humans, Magnetic Resonance Spectroscopy
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