The potential scope of a humanoid robot in anatomy education: a review of a unique proposal
Abstract
Introduction Applications based on artificial intelligence and machine learning are becoming more popular in teaching learning. Advanced technologies have facilitated robots to carry out various human-like functions, which have navigated the interest of educators to discover the role of robots as potential teachers, instructors, or teaching assistants in education.Methods An extensive search for articles for humanoid robots and education either in the title or keywords was done utilizing PubMed, Google Scholar and Web of Science data sets. The tracking terms were artificial intelligence, education, medical education, anatomy, robots, humanoid robots, teaching, teaching assistant and tutor.Results The usage of artificial intelligence in the form of humanoid robots is quite common. However, literature citing its usage in medical education is rare. Humanoid robots as a teacher or teaching assistants are predominantly used in learning foreign languages. Primarily, a humanoid robot can discharge five functions as a potential teacher.Conclusion Humanoid robots can effectively fulfil numerous educational goals in medicine since they can replicate human responses, work relentlessly regardless of students' repeated mistakes, be loaded with innovative teaching methodologies, and be upgraded with more current information. As a subject of medicine, anatomy is highly visual; therefore, constant endeavors have been initiated to develop technology-enhanced learning over the decades. Although artificial intelligence in humanoid robots has been successfully used in primary education and in learning a foreign language, its scope as an anatomy teacher or teaching assistant is a new and unique idea that needs exploration.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the basic sciences, AI was reported most frequently in anatomy (n = 12); AI can support an interactive and visually immersive learning experience in anatomy, especially if it is integrated with virtual reality (VR) and augmented reality (AR); Asghar et al. proposed using a humanoid robot as a teaching assistant in anatomy classes.
“In the basic sciences, AI was reported most frequently in anatomy (n = 12). AI can support an interactive and visually immersive learning experience in anatomy, especially if it is integrated with virtual reality (VR) and augmented reality (AR). Asghar et al. proposed using a humanoid robot as a teaching assistant in anatomy classes [82]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [82]
Cites
Human–Robot Interaction: A Survey
Goodrich, M. A., & Schultz, A. C. (2008) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000005 · Cited by 1,541 · see citing works
Collaborative Learning: Cognitive and Computational Approaches
Jones, A. C. (2000) · Computers & Education · doi:10.1016/s0360-1315(00)00011-7 · Cited by 1,506 · see citing works
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Akgun, S., & Greenhow, C. (2022) · AI and Ethics · doi:10.1007/s43681-021-00096-7 · Cited by 1,473 · cited 3× · see citing works
“Problems may also arise when surveillance systems trigger issues related to autonomy, more specifically the person’s ability to act on her or his own interest and values [1].”
BackgroundSocially intelligent robots: Dimensions of human-robot interaction
Dautenhahn, K. (2007) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2006.2004 · Cited by 1,210 · see citing works
A REVIEW OF THE APPLICABILITY OF ROBOTS IN EDUCATION
Mubin, O., Stevens, C., Shahid, S., Mahmud, A. A., & Dong, J.-J. (2013) · Technology for Education and Learning · doi:10.2316/journal.209.2013.1.209-0015 · Cited by 762 · see citing works
Computational intelligence: a logical approach
(1998) · Choice Reviews Online · doi:10.5860/choice.35-5701 · Cited by 726 · see citing works
In the shades of the uncanny valley: An experimental study of human–chatbot interaction
Ciechanowski, L., Przegalińska, A., Magnuski, M., & Gloor, P. A. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.01.055 · Cited by 680 · see citing works
Tanaka, F., & Matsuzoe, S. (2012) · Journal of Human-Robot Interaction · doi:10.5898/jhri.1.1.tanaka · Cited by 376 · see citing works
Honig, S., & Oron-Gilad, T. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00861 · Cited by 338 · cited 3× · see citing works
“In the classroom setting, the success of robot teachers seems to be more empathetic, as, unlike human teachers, they do not punish their students for making mistake [21].”
BackgroundDynamic visualizations and learning: getting to the difficult questions
Hegarty, M. (2004) · Learning and Instruction · doi:10.1016/j.learninstruc.2004.06.007 · Cited by 333 · see citing works
Regan, P. M., & Jesse, J. (2019) · Ethics and Information Technology · doi:10.1007/s10676-018-9492-2 · Cited by 286 · see citing works
Robots Under COVID-19 Pandemic: A Comprehensive Survey
Shen, Y., Guo, D., Long, F., Mateos, L. A., Ding, H., Xiu, Z., Hellman, R. B., King, A., Chen, S., Zhang, C., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3045792 · Cited by 260 · see citing works
Technology-enhanced language learning: A case study
Yang, S. C., & Chen, Y. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2006.02.015 · Cited by 255 · see citing works
Lo Piano, S. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0501-9 · Cited by 229 · cited 2× · see citing works
“The most serious hazards of using AI in these situations are: sustaining existing institutional prejudice and discrimination and magnifying unfairness for students from primarily disadvantaged groups [25].”
BackgroundIntrinsic content, active memory and the extended mind
Clark, A. (2005) · Analysis · doi:10.1111/j.1467-8284.2005.00514.x · Cited by 204 · see citing works
Teaching anatomy using an active and engaging learning strategy
Singh, K., Bharatha, A., Sa, B., Adams, O. P., & Majumder, M. A. A. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1590-2 · Cited by 179 · see citing works
Guidelines for Designing Social Robots as Second Language Tutors
Belpaeme, T., Vogt, P., van den Berghe, R., Bergmann, K., Göksun, T., de Haas, M., Kanero, J., Kennedy, J., Küntay, A., Oudgenoeg-Paz, O., et al. (2018) · International Journal of Social Robotics · doi:10.1007/s12369-018-0467-6 · Cited by 156 · see citing works
Goel, A. K., & Polepeddi, L. (2018) · Routledge eBooks · doi:10.4324/9781351186193-7 · Cited by 146 · see citing works
Teaching a humanoid robot to walk faster through Safe Reinforcement Learning
García, J., & Shafie, D. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103360 · Cited by 79 · see citing works
Learning together: ASIMO developing an interactive learning partnership with children
Okita, S. Y., Ng‐Thow‐Hing, V., & Sarvadevabhatla, R. K. (2009) · IEEE RO-MAN · doi:10.1109/roman.2009.5326135 · Cited by 77 · see citing works
Learning about, from, and with Robots: Students' Perspectives
Shin, N., & Kim, S. (2007) · IEEE RO-MAN · doi:10.1109/roman.2007.4415235 · Cited by 67 · see citing works
Humanoid Robots as Teachers and a Proposed Code of Practice
Newton, D. P., & Newton, L. D. (2019) · Frontiers in Education · doi:10.3389/feduc.2019.00125 · Cited by 61 · see citing works
Artificial intelligence, nano-technology and genomic medicine: The future of anaesthesia
Naaz, S., & Asghar, A. (2022) · Journal of Anaesthesiology Clinical Pharmacology · doi:10.4103/joacp.joacp_139_20 · Cited by 45 · see citing works
Self-citationA Two Years Informal Learning Experience Using the Thymio Robot
Riedo, F., Rétornaz, P., Bergeron, L., Nyffeler, N., & Mondada, F. (2012) · Advances in Autonomous Mini Robots · doi:10.1007/978-3-642-27482-4_7 · Cited by 41 · see citing works
Physical Learning Activities with a Teaching Assistant Robot in Elementary School Music Class
Han, J., Kim, D., & Kim, J. (2009) · doi:10.1109/ncm.2009.407 · Cited by 40 · see citing works
Modeling and simulation of an anatomy teaching system
Zhang, X., Yang, J., Chen, N., et al. (2019) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-019-0019-4 · Cited by 28 · see citing works
Auditory mood detection for social and educational robots
Ruvolo, P., Fasel, I., & Movellan, J. R. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543754 · Cited by 21 · see citing works
Tuna, A., & Tuna, G. (2019) · Center for Educational Policy Studies Journal · doi:10.26529/cepsj.679 · Cited by 19 · see citing works
Constructivist learning of anatomy: Gaining knowledge by creating anatomical casts
Hermiz, D. J., O'Sullivan, D. J., Lujan, H. L., et al. (2011) · Anatomical Sciences Education · doi:10.1002/ase.206 · Cited by 17 · see citing works
Robot vs. Human Teacher: Instruction in the Digital Age for ESL Learners
Kwok, V. (2015) · English Language Teaching · doi:10.5539/elt.v8n7p157 · Cited by 15 · see citing works
An Innovative Approach to Teaching Robotics
Doswell, J. T., & Mosley, P. (2006) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2006.1652653 · Cited by 11 · see citing works
How robotic surgery is changing our understanding of anatomy
Moro, F. D. (2017) · Arab Journal of Urology · doi:10.1016/j.aju.2017.10.001 · Cited by 10 · see citing works
IFR World Robotics 2008 2007: 6.5 million robots in operation world-wide
(2009) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.2009.04936dab.003 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
“Asghar et al. proposed using a humanoid robot as a teaching assistant in anatomy classes [82]; however, some scholars raised concerns that AI might not capture individual anatomical variations [83].”
- Authors
- Asghar, A., Patra, A., & Ravi, K. S.
- Affiliations
- All India Institute of Medical Sciences, Patna (IN)
- All India Institute of Medical Sciences (IN)
- All India Institute of Medical Sciences Rishikesh (IN)
- Corresponding author
- Patra, Apurba
- Venue
- Surgical and Radiologic Anatomy
- Published
- 2022-09-20
- Volume
- 44
- Issue
- 10
- Pages
- 1309-1317
- ISSN
- 0930-1038 (ISSN-L)
- 1279-8517
- OpenAlex
- W4297875007
- Cited by
- 23
- References
- 33
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 9
- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Surgical and Radiologic Anatomy (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- humanoid robots, anatomy education, medical education, artificial intelligence in education, teaching assistants, robot tutors, technology-enhanced learning, foreign language learning, educational robotics
- MeSH terms
- Artificial Intelligence, Education, Medical, Humans, Robotics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
