Artificial intelligence and educational sustainability in virtual higher education 114 52
DOI:
https://doi.org/10.51896/btcacz98Keywords:
educational sustainability, artificial intelligence, higher education, virtual education, ethics, tutoring, digital inclusion, governanceAbstract
Virtual higher education seeks to address the growing demand for sustainable, inclusive, and ethical educational models within the context of digital transformation and the progressive integration of Artificial Intelligence (AI). The purpose of this study is to analyze the pedagogical, ethical, and organizational conditions required for the implementation of AI applications related to academic tutoring and student monitoring from the perspective of educational sustainability. The research adopts a sequential explanatory mixed-methods design. The quantitative phase consists of a questionnaire using Likert-scale items administered to students, faculty members, and academic tutors, followed by a qualitative phase based on semi-structured interviews aimed at exploring in greater depth the perceptions, experiences, and tensions identified during the quantitative stage. The discussion is structured around four key dimensions: institutional ethical governance, AI-supported academic tutoring, digital inclusion, and systematic evaluation. The findings suggest that AI can contribute to ensuring the continuity of students’ educational pathways, enhancing personalized learning experiences, and enabling the early identification of academic risks. However, it may also give rise to challenges related to privacy, algorithmic bias, surveillance, technological dependency, and the displacement of professional judgment. The study concludes that AI can effectively contribute to educational sustainability only if it is implemented as a transparent decision-support tool that complements, rather than replaces, human judgment while ensuring data protection, accessibility, faculty oversight, and institutional accountability.
References
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8
Eynon, R., & Malmberg, L. E. (2021). Lifelong learning and the Internet: ¿Who benefits most from learning en línea? British Journal of Educational Technology, 52(2), 569–583.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). IA4People: An ethical framework for a good IA society. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
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, 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
OECD. (2021). IA and the future of skills, volume 1: Capabilities and assessments. OECD Publishing.
Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 46–55.
Selwyn, N. (2019). Should robots replace teachers? IA and the future of education. Polity Press.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
UNESCO. (2021). IA and education: Guidance for policy-makers. UNESCO.
UNESCO. (2023). Guidance for generative IA in education and research. UNESCO.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in IA in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
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