1. Alraddadi A. Literature review of anatomical variations: clinical significance, identification approach, and teaching strategies. Cureus. 2021; 13(4):e14451.
Article
2. 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.
Article
3. Harden RM. Ten key features of the future medical school-not an impossible dream. Med Teach. 2018; 40(10):1010–5.
Article
4. Frenk J, Chen L, Bhutta ZA, Cohen J, Crisp N, Evans T, et al. Health professionals for a new century: transforming education to strengthen health systems in an inter-dependent world. Lancet. 2010; 376(9756):1923–58.
Article
5. Grainger R, Liu Q, Geertshuis S. Learning technologies: a medium for the transformation of medical education? Med Educ. 2021; 55(1):23–9.
Article
6. Dahle LO, Brynhildsen J, Behrbohm Fallsberg M, Rundquist I, Hammar M. Pros and cons of vertical integration between clinical medicine and basic science within a problem-based undergraduate medical curriculum: examples and experiences from Linköping, Sweden. Med Teach. 2002; 24(3):280–5.
Article
7. Brauer DG, Ferguson KJ. The integrated curriculum in medical education: AMEE Guide No. 96. Med Teach. 2015; 37(4):312–22.
Article
8. Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005; 43(11):5721–32.
Article
9. Wilbert SA, Mark Welch JL, Borisy GG. Spatial ecology of the human tongue dorsum microbiome. Cell Rep. 2020; 30(12):4003–4015e3.
Article
10. Blender: a 3D modelling and rendering package [Internet]. Amsterdam, Netherlands: Stichting Blender Foundation;2018. [cited at 2021 Sep 30]. Available from:
http://www.blender.org
.
11. Unity: using the UI tools [Internet]. San Francisco (CA): Unity Technologies;c2021. [cited at 2021 Sep 30]. Available from:
https://unity.com/
.
12. Khalil MK, Paas F, Johnson TE, Payer AF. Design of interactive and dynamic anatomical visualizations: the implication of cognitive load theory. Anat Rec B New Anat. 2005; 286(1):15–20.
Article
13. Sweller J. Cognitive load theory and educational technology. Educ Technol Res Dev. 2020; 68(1):1–16.
Article
14. Martens R, Gulikers J, Bastiaens T. The impact of in trinsic motivation on e-learning in authentic computer tasks. J Comput Assist Learn. 2004; 20(5):368–76.
15. Keller JM. Motivational design for learning and performance: the ARCS model approach. New York (NY): Springer;2010.
16. Li K, Keller JM. Use of the ARCS model in education: a literature review. Computers & Education. 2018; 122:54–62.
Article
17. Milman NB, Wessmiller J. Motivating the online learner using Keller’s ARCS model. Distance Learn. 2016; 13(2):67–71.
18. Bandura A. Social learning theory. Englewood Cliffs (NJ): Prentice-Hall;1977.
19. Knowles MS. Self-directed learning: a guide for learners and teachers. New York (NY): Association Press;1975.
20. Azer SA. A multimedia CD-ROM tool to improve student understanding of bile salts and bilirubin metabolism: evaluation of its use in a medical hybrid PBL course. Adv Physiol Educ. 2005; 29(1):40–50.
Article
21. Rosenberg H, Grad HA, Matear DW. The effectiveness of computer-aided, self-instructional programs in dental education: a systematic review of the literature. J Dent Educ. 2003; 67(5):524–32.
Article
22. Revell SM, McCurry MK. Engaging millennial learners: effectiveness of personal response system technology with nursing students in small and large classrooms. J Nurs Educ. 2010; 49(5):272–5.
Article
23. Janssen A, Shaw T, Goodyear P, Kerfoot BP, Bryce D. A little healthy competition: using mixed methods to pilot a team-based digital game for boosting medical student engagement with anatomy and histology content. BMC Med Educ. 2015; 15:173.
Article
24. Thompson ME, Ford R, Webster A. Effectiveness of interactive, online games in learning neuroscience and students’ perception of the games as learning tools: a pre-experimental study. J Allied Health. 2011; 40(3):150–5.