AI Applications in Vitagen-Based Education: Expanding Opportunities and Emerging Risks in Developing Students’ Mentality
DOI:
https://doi.org/10.6000/2818-3401.2025.03.11Keywords:
Vitagen-based education, artificial intelligence in education, mentality development, learning analytics, educational ethicsAbstract
Vitagen-based education treats learning not only as the acquisition of disciplinary knowledge, but as the deliberate cultivation of students’ mentality – a constellation of metacognitive, self-regulatory, motivational, and socio-emotional dispositions that shape how individuals relate to themselves, others, and a rapidly changing world. This article explores how artificial intelligence (AI) can both strengthen and destabilise this project. First, it reconstructs the theoretical foundations of vitagen-based education and clarifies the notion of mentality in relation to agency, reflection, and lifelong learning. It then maps four major clusters of AI applications – personalisation and adaptive pathways, intelligent mentoring and feedback, assessment and data-informed insights, and learning analytics for mentality development – showing how these tools can scaffold reflection, support growth mindset, and extend socio-emotional learning when carefully designed. At the same time, the article identifies four interlocking domains of risk: privacy and surveillance; bias, fairness, and inclusivity; dependency and erosion of critical thinking; and mental health and well-being. Drawing on international cases and longitudinal studies, the analysis distils pedagogical and policy implications for curriculum design, teacher professional development, and multi-level governance of AI in education. Finally, it outlines a research agenda for AI-enhanced vitagen education, arguing for ethically grounded, mixed-methods and cross-disciplinary inquiry. The article contends that AI will not automatically elevate vitagen-based education, but, under well-governed conditions, can become a powerful – though never neutral – partner in developing students’ mentality.
References
Xu, Y. E. (2024). The Double-Edged Sword of AI Writing Tools. DOI: https://doi.org/10.31235/osf.io/wnpd5
Schiff, D. (2021). Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI & society, 36(1), 331-348. DOI: https://doi.org/10.1007/s00146-020-01033-8
Mollick, E., &Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. arXiv preprint arXiv:2306.10052. DOI: https://doi.org/10.2139/ssrn.4475995
Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., Lu, G., ... &Zhai, X. (2023). Artificial general intelligence (AGI) for education. arXiv preprint arXiv:2304.12479, 1, 1-34.
August, S. E., &Tsaima, A. (2021). Artificial intelligence and machine learning: an instructor’s exoskeleton in the future of education. In Innovative Learning Environments in STEM Higher Education: Opportunities, Challenges, and Looking Forward (pp. 79-105). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-58948-6_5
Köbis, L., &Mehner, C. (2021). Ethical questions raised by AI-supported mentoring in higher education. Frontiers in Artificial Intelligence, 4, 624050. DOI: https://doi.org/10.3389/frai.2021.624050
Kadel, R., Mishra, B. K., Shailendra, S., Abid, S., Rani, M., &Mahato, S. P. (2024, July). Crafting tomorrow’s evaluations: assessment design strategies in the era of generative AI. In 2024 International Symposium on Educational Technology (ISET) (pp. 13-17). IEEE. DOI: https://doi.org/10.1109/ISET61814.2024.00012
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., ... & Tsai, C. C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in psychology, 11, 580820. DOI: https://doi.org/10.3389/fpsyg.2020.580820
Rienties, B., Domingue, J., Duttaroy, S., Herodotou, C., Tessarolo, F., &Whitelock, D. (2024). I would love this to be like an assistant, not the teacher: A voice of the customer perspective of what distance learning students want from an artificial intelligence digital assistant. arXiv preprint arXiv:2403.15396. DOI: https://doi.org/10.1080/01587919.2024.2338717
Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student attitudes toward learning analytics in higher education:“Thefitbit version of the learning world”. Frontiers in psychology, 7, 1959. DOI: https://doi.org/10.3389/fpsyg.2016.01959
Yan, L., Martinez-Maldonado, R., &Gasevic, D. (2024, March). Generative artificial intelligence in learning analytics: Contextualising opportunities and challenges through the learning analytics cycle. In Proceedings of the 14th learning analytics and knowledge conference (pp. 101-111). DOI: https://doi.org/10.1145/3636555.3636856
Reyes-Ortiz, A. (2019). Motivating Students with Learning Disabilities to Succeed in Education.
Ku, Y. R., & Stager, C. (2022). Rethinking the multidimensionality of growth mindset amid the COVID-19 pandemic: A systematic review and framework proposal. Frontiers in Psychology, 13, 572220. DOI: https://doi.org/10.3389/fpsyg.2022.572220
Tan, M. C. C., Chye, S. Y. L., & Teng, K. S. M. (2022). “In the shoes of another”: immersive technology for social and emotional learning. Education and Information Technologies, 27(6), 8165-8188. DOI: https://doi.org/10.1007/s10639-022-10938-4
Franco D’Souza, R., Mathew, M., Mishra, V., &Surapaneni, K. M. (2024). Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Medical Education Online, 29(1), 2330250. DOI: https://doi.org/10.1080/10872981.2024.2330250
Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61-65. DOI: https://doi.org/10.1007/s43681-020-00002-7
Lakkaraju, K., Khandelwal, V., Srivastava, B., Agostinelli, F., Tang, H., Singh, P., ... & Kundu, A. (2024). Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube. arXiv preprint arXiv:2402.01760.
UtterbergModén, M., Ponti, M., Lundin, J., &Tallvid, M. (2025). When fairness is an abstraction: equity and AI in Swedish compulsory education. Scandinavian Journal of Educational Research, 69(4), 790-804. DOI: https://doi.org/10.1080/00313831.2024.2349908
Bohdal, O., Hospedales, T., Torr, P. H., &Barez, F. (2023). Fairness in AI and its long-term implications on society. arXiv preprint arXiv:2304.09826.
Hawes, D., & Arya, A. (2023). A VR-based Priming Framework and Technology Implementation to Improve Learning Mindsets and Academic Performance in Post-Secondary Students. arXiv preprint arXiv:2303.11547.
Mitsea, E., Drigas, A., &Skianis, C. (2023). Digitally assisted mindfulness in training self-regulation skills for sustainable mental health: a systematic review. Behavioral Sciences, 13(12), 1008. DOI: https://doi.org/10.3390/bs13121008
Tseng, Y. J., & Yadav, G. (2023). ActiveAI: Introducing AI Literacy for Middle School Learners with Goal-based Scenario Learning. arXiv preprint arXiv:2309.12337.
Choung, H., David, P., &Seberger, J. S. (2023). A multilevel framework for AI governance. arXiv preprint arXiv:2307.03198. DOI: https://doi.org/10.4324/9781003316077-25
Van Brummelen, J., Heng, T., &Tabunshchyk, V. (2021, May). Teaching tech to talk: K-12 conversational artificial intelligence literacy curriculum and development tools. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 17, pp. 15655-15663). DOI: https://doi.org/10.1609/aaai.v35i17.17844
Latham, A., & Goltz, S. (2019, June). A Survey of the General Public’s Views on the Ethics of Using AI in Education. In International Conference on Artificial Intelligence in Education (pp. 194-206). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-23204-7_17
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and information technologies, 28(4), 4221-4241. DOI: https://doi.org/10.1007/s10639-022-11316-w
Downloads
Published
How to Cite
Issue
Section
