Declared preferences for virtual tutoring styles in two curricular cohorts of Kinesiology students: an exploratory cross-sectional study.

Authors

  • Italo Campos Montenegro arrera de Kinesiología, Facultad de Ciencias de la Vida, Universidad Viña del Mar, Viña del Mar, Chile https://orcid.org/0009-0007-0325-3344
DOI: https://doi.org/10.6018/edumed.723391
Keywords: virtual tutor, AI, ChatGPT, Socratic tutor, Clinical reasoning

Abstract

Objective: To compare the reported preference for virtual tutoring styles between two curricular cohorts of Kinesiology students and to describe differences in perceived usefulness, study strategies, and functional orientation of tutor use. Methods: A quantitative, observational, cross-sectional study was conducted with 6th and 8th semester Kinesiology students at Universidad Viña del Mar. Students had open access to two virtual tutors—one structured-explanatory and the other reflective-Socratic—without assignment, standardized exposure, or objective recording of interactions. The primary variable was the reported preference for the reflective-Socratic tutor. Secondary outcomes were obtained through an ad hoc questionnaire and interpreted as contextual indicators of self-reporting. The difference between curricular cohorts in preference for the reflective-Socratic tutor was estimated using Fisher's exact test and Firth's penalized logistic regression, adjusted for frequency of use and prior experience with virtual tutors. Results: 44 of 70 invited students participated. Preference for the reflective-Socratic tutor was reported by 10% of sixth-semester students and by 70.8% of eighth-semester students. The association was significant in the unadjusted analysis and remained so after adjustment using Firth's penalized logistic regression. Perceived usefulness was high in both groups. Secondary outcomes showed different descriptive patterns in reflection, justification, and the creation of summaries or outlines, but none of the eight comparisons retained statistical significance after Holm's adjustment. Conclusions: Marked differences were observed in the reported preference for online tutoring styles between the two curricular cohorts studied. Because each cohort corresponded to a different subject and different contextualized tutors, the results do not allow these differences to be attributed solely to the educational level. The observed patterns allow us to formulate hypotheses about the relationship between tutoring configuration, curricular context, task characteristics, and expected degree of autonomy, which should be evaluated using designs with equivalent exposure and objective educational outcomes.

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References

1. Okonkwo CW, Ade-Ibijola A. Chatbots applications in education: a systematic review. Comput Educ Artif Intell. 2021, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033.

2. Abdallah N, Katmah R, Khalaf K, Jelinek HF. Systematic review of ChatGPT in higher education: navigating impact on learning, wellbeing, and collaboration. Soc Sci Humanit Open. 2025, 12, 101866. https://doi.org/10.1016/j.ssaho.2025.101866.

3. Xi L, Zhang Y, Wang Q. Investigating the effects of an LLM-based Socratic conversational agent on students’ academic performance and reflective thinking in higher education. Comput Educ. 2026, 241, 105494. https://doi.org/10.1016/j.compedu.2025.105494.

4. Fakour H, Imani M. Socratic wisdom in the age of AI: a comparative study of ChatGPT and human tutors in enhancing critical thinking skills. Front Educ. 2025, 10, 1528603. https://doi.org/10.3389/feduc.2025.1528603.

5. Chang DH, Lin MP-C, Hajian S, Wang QQ. Educational design principles of using AI chatbot that supports self-regulated learning in education: goal setting, feedback, and personalization. Sustainability. 2023, 15, 12921. https://doi.org/10.3390/su151712921.

6. Guan R, Raković M, Chen G, Gašević D. How educational chatbots support self-regulated learning? A systematic review of the literature. Educ Inf Technol. 2025, 30, 4493–4518. https://doi.org/10.1007/s10639-024-12881-y.

7. Bassner P, Lenk-Ostendorf B, Beinstingel R, Wasner T, Krusche S. Less stress, better scores, same learning: the dissociation of performance and learning in AI-supported programming education. Comput Educ Artif Intell. 2026, 10, 100537. https://doi.org/10.1016/j.caeai.2025.100537.

8. Bastani H, Bastani O, Sungu A, Ge H, Kabakcı Ö, Mariman R. Generative AI without guardrails can harm learning: evidence from high school mathematics. Proc Natl Acad Sci U S A. 2025, 122, e2422633122. https://doi.org/10.1073/pnas.2422633122.

9. Zhai C, Wibowo S, Li LD. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learn Environ. 2024, 11, 28. https://doi.org/10.1186/s40561-024-00316-7.

10. Campos Montenegro I, Martínez Mena M. Alfabetización en IA y experiencia subjetiva con un tutor virtual basado en GPT en estudiantes de Kinesiología: estudio transversal. Rev Esp Edu Med. 2026, 7(4). https://doi.org/10.6018/edumed.715291.

11. Feigerlova E, Hani H, Hothersall-Davies E. A systematic review of the impact of artificial intelligence on educational outcomes in health professions education. BMC Med Educ. 2025, 25, 129. https://doi.org/10.1186/s12909-025-06719-5.

12. Izquierdo-Condoy JS, Arias-Intriago M, Tello-De-la-Torre A, Busch F, Ortiz-Prado E. Generative artificial intelligence in medical education: enhancing critical thinking or undermining cognitive autonomy? J Med Internet Res. 2025, 27, e76340. https://doi.org/10.2196/76340.

13. Çiçek FE, Ülker M, Özer M, Kıyak YS. ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial. Postgrad Med J. 2025, 101, 458–463. https://doi.org/10.1093/postmj/qgae170.

14. Corral-Gudino L, Marcos M. La escritura en educación médica en la era de la inteligencia artificial generativa: recomendaciones para fomentar la creatividad y el pensamiento crítico en un entorno de aprendizaje asistido por IA generativa. Rev Esp Edu Med. 2026, 7(3). https://doi.org/10.6018/edumed.708901.

15. Bonilla Mejia JJ, Cortés Fuenzalida TD, Polanco Aliaga DH, Martínez Carrillo C, Herrera Alcaíno AA. Inteligencia artificial en la formación clínica práctica de estudiantes de medicina de pregrado: una revisión de alcance de aplicaciones, resultados y brechas. Rev Esp Edu Med. 2026, 7(3). https://doi.org/10.6018/edumed.710071.

Published
28-07-2026
How to Cite
Campos Montenegro, I. (2026). Declared preferences for virtual tutoring styles in two curricular cohorts of Kinesiology students: an exploratory cross-sectional study. Spanish Journal of Medical Education, 10(1). https://doi.org/10.6018/edumed.723391
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