Ética e Integridad Académica en la Educación Virtual: Retos y Estrategias Ante el uso de Inteligencia Artificial Generativa en la Educación Superior

Mario Fernández Solís; Erika Jadira Romero Cardenas

doi:10.63371/ic.v5.n2.a948

Autores/as

Mario Fernández Solís Universidad Estatal de Milagro Autor/a https://orcid.org/0009-0001-9648-3670
Erika Jadira Romero Cardenas Universidad Estatal de Milagro Autor/a https://orcid.org/0000-0001-8715-5993

DOI:

https://doi.org/10.63371/ic.v5.n2.a948

Palabras clave:

integridad académica, inteligencia artificial generativa, educación virtual, ética educativa, autorregulación del aprendizaje

Resumen

La expansión acelerada de la inteligencia artificial generativa (IAg) en los entornos de educación superior virtual ha reconfigurado los modos en que los estudiantes acceden, procesan y producen conocimiento académico. Herramientas como ChatGPT, Gemini o Claude ofrecen capacidades de generación de texto, síntesis de información y resolución de problemas que, en ausencia de supervisión presencial y marcos normativos sólidos, pueden comprometer seriamente la integridad académica. El presente artículo examina los desafíos éticos y las oportunidades pedagógicas que implica el uso de la IAg en modalidades 100% virtuales, con énfasis en la autorregulación del aprendizaje, la conciencia ética estudiantil y las políticas institucionales. Se adopta un enfoque mixto con diseño explicativo-secuencial, articulado sobre fundamentos teóricos provenientes de la Teoría Sociocognitiva de Bandura, el modelo de autorregulación de Zimmerman, la Teoría de la Acción Razonada de Ajzen y los marcos de integridad académica de Bretag. Los hallazgos esperados apuntan a la identificación de perfiles diferenciados de uso ético y no ético, al rol moderador de la autorregulación y la claridad de políticas, y a la formulación de estrategias pedagógicas e institucionales para fortalecer la ética en contextos virtuales mediados por IA.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324.

https://doi.org/10.1002/hbe2.195

Aquino, K., & Reed, A. (2002). The self-importance of moral identity. Journal of Personality and Social Psychology, 83(6), 1423–1440.

https://doi.org/10.1037/0022-3514.83.6.1423

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62.

https://doi.org/10.61969/jai.1337500

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26.

https://doi.org/10.1146/annurev.psych.52.1.1

Bandura, A. (2016). Moral disengagement: How people do harm and live with themselves. Worth Publishers.

Bearman, M., Nieminen, J. H., & Ajjawi, R. (2022). Designing for authenticity and resisting the AI tide. Assessment & Evaluation in Higher Education. Advance online publication.

https://doi.org/10.1080/02602938.2022.2045817

Bertram Gallant, T. (2017). Academic integrity as a teaching and learning issue: From theory to practice. Theory Into Practice, 56(2), 88–94.

https://doi.org/10.1080/00405841.2017.1308173

Boud, D., & Soler, R. (2016). Sustainable assessment revisited. Assessment & Evaluation in Higher Education, 41(3), 400–413.

https://doi.org/10.1080/02602938.2015.1018133

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.

https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352.

https://doi.org/10.1080/14780887.2020.1769238

Bretag, T. (2016). Handbook of academic integrity. Springer.

Bretag, T., Harper, R., Burton, M., Ellis, C., Newton, P., Rozenberg, P., Saddiqui, S., & van Haeringen, K. (2019). Contract cheating: A survey of Australian university students. Studies in Higher Education, 44(11), 1837–1856.

https://doi.org/10.1080/03075079.2018.1462420

CEPAL. (2022). Universalizar el acceso a las tecnologías digitales para enfrentar los efectos del COVID-19. Comisión Económica para América Latina y el Caribe.

https://repositorio.cepal.org/handle/11362/45789

Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43.

https://doi.org/10.1186/s41239-023-00411-8

Comas-Forgas, R., Sureda-Negre, J., & Mut-Amengual, B. (2021). Academic plagiarism prevalence, reasons, and solutions: An empirical study at a Spanish university. Cultura y Educación, 33(2), 443–477.

https://doi.org/10.1080/11356405.2021.1910698

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.

https://doi.org/10.1080/14703297.2023.2190148

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.

Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3–8.

https://doi.org/10.1016/j.iheduc.2011.06.002

Dee, T. S., & Jacob, B. A. (2012). Rational ignorance in education: A field experiment in student plagiarism. Journal of Human Resources, 47(2), 397–434.

https://doi.org/10.3368/jhr.47.2.397

Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1206936.

https://doi.org/10.3389/feduc.2023.1206936

Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning & Teaching, 6(1), 57–63.

https://doi.org/10.37074/jalt.2023.6.1.35

Guerrero-Dib, J. G., Portales, L., & Heredia-Escorza, Y. (2020). Impact of academic integrity on workplace ethical behaviour. International Journal for Educational Integrity, 16(1), 2.

https://doi.org/10.1007/s40979-020-0051-3

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. The Annals of Family Medicine, 13(6), 554–561.

https://doi.org/10.1370/afm.1865

Hair, J. F., Henseler, J., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.

Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, P. (2018). Metodología de la investigación (6ta ed.). McGraw-Hill.

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(4), 504–526.

https://doi.org/10.1007/s40593-021-00239-1

Ives, B., & Gee, E. (2019). Academic dishonesty and its relation to student practices and beliefs: The role of articulated expectations. Journal of Academic Ethics, 17(1), 77–93.

https://doi.org/10.1007/s10805-018-9313-3

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

https://doi.org/10.1016/j.lindif.2023.102274

Lancaster, T., & Clarke, R. (2016). Contract cheating: The outsourcing of assessed student work. In T. Bretag (Ed.), Handbook of academic integrity (pp. 639–654). Springer.

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.

https://doi.org/10.1016/j.ijme.2023.100790

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16.

https://doi.org/10.1145/3313831.3376727

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Macfarlane, B., Zhang, J., & Pun, A. (2014). Academic integrity: A review of the literature. Studies in Higher Education, 39(2), 339–358.

https://doi.org/10.1080/03075079.2012.709495

Marinoni, G., Van't Land, H., & Jensen, T. (2020). The impact of COVID-19 on higher education around the world. International Association of Universities.

McCabe, D. L., Butterfield, K. D., & Treviño, L. K. (2012). Cheating in college: Why students do it and what educators can do about it. Johns Hopkins University Press.

McNeish, D. (2018). Thanks coefficient alpha, we'll take it from here. Psychological Methods, 23(3), 412–433.

https://doi.org/10.1037/met0000144

Newton, P. M., & Lang, C. (2016). Custom essay writers, freelancers, and other paid third parties. In T. Bretag (Ed.), Handbook of academic integrity (pp. 249–271). Springer.

Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2023). Detection of GPT-4 generated text in higher education: Combining human judgment and software to identify generative AI tool misuse. Journal of Academic Ethics. Advance online publication. https://doi.org/10.1007/s10805-023-09492-6

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Pintrich, P. R., Smith, D. A. F., García, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.

Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS. https://www.smartpls.com

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363. https://doi.org/10.37074/jalt.2023.6.1.9

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays: Should professors worry? Nature. https://doi.org/10.1038/d41586-022-04397-7

Sun, Z., Xie, K., & Anderman, L. H. (2021). The role of self-regulated learning in students' success in flipped undergraduate math courses. The Internet and Higher Education, 44, 100720. https://doi.org/10.1016/j.iheduc.2019.100720

Tashakkori, A., & Teddlie, C. (2010). SAGE handbook of mixed methods in social and behavioral research (2nd ed.). SAGE.

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x

UNESCO. (2021a). Recommendation on the ethics of artificial intelligence. United Nations Educational, Scientific and Cultural Organization.

https://unesdoc.unesco.org/ark:/48223/pf0000381137

UNESCO. (2021b). AI and education: Guidance for policy-makers. United Nations Educational, Scientific and Cultural Organization.

https://unesdoc.unesco.org/ark:/48223/pf0000376709

Warschauer, M., & Tate, T. (2020). AI literacy and digital equity in education. In M. Khosrow-Pour (Ed.), Encyclopedia of information science and technology (5th ed., pp. 1–12). IGI Global.

Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe's content validity ratio. Measurement and Evaluation in Counseling and Development, 45(3), 197–210.

https://doi.org/10.1177/0748175612440286

Wolters, C. A. (2003). Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38(4), 189–205.

https://doi.org/10.1207/S15326985EP3804_1

World Medical Association. (2013). World Medical Association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194.

https://doi.org/10.1001/jama.2013.281053

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39.

https://doi.org/10.1186/s41239-019-0171-0

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2