Autoeficacia académica y diversidad en la era de la Inteligencia Artificial
DOI:
https://doi.org/10.65598/rps.5940Palabras clave:
Inteligencia Artificial, autoeficacia, rendimiento académico, diferencias de género, confianza, educación superior, uso de las tecnologíasResumen
Este estudio explora cómo el colectivo estudiantil universitario interactúa con las herramientas de Inteligencia Artificial (IA) y cómo estas interacciones afectan su autoeficacia académica. El objetivo es identificar las implicaciones psicológicas y conductuales del uso de la IA en la educación superior, en particular en relación con la autoconfianza, la autonomía, el género y la percepción de competencia de los/as estudiantes.
Mediante un diseño de investigación cuantitativa, se encuestó a 226 estudiantes universitarios españoles mediante un cuestionario en línea ad hoc. El instrumento midió los patrones de uso de la IA, la utilidad percibida y las limitaciones de las herramientas de IA, y la autoeficacia de los estudiantes. Se emplearon modelos de ecuaciones estructurales para analizar las relaciones entre los beneficios percibidos de la IA, el género y la autoeficacia académica.
Los resultados muestran una relación significativa y positiva entre la percepción de la utilidad de la IA por parte de los/as estudiantes y su autoeficacia. No se encontraron diferencias de género estadísticamente significativas ni en la adopción de la IA ni en la manifestación del efecto Dunning-Kruger. El uso general de herramientas de IA se asoció positivamente con la competencia académica percibida por los/as estudiantes.
Este artículo contribuye a la creciente literatura sobre la IA en la educación al centrarse en sus efectos psicológicos, más allá del rendimiento técnico. El estudio también aumenta la conciencia sobre los riesgos de la dependencia excesiva y la falsa autoconfianza, y pide una integración más equilibrada de la IA en los entornos académicos.
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Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(1), 10. DOI: https://doi.org/10.1186/s41239-024-00444-7
Alangari, H. (2024). Disrupting education: Artificial intelligence in higher education. In M. D. Lytras, A. Alkhaldi, S. Malik, A. C. Serban, & T. Aldosemani (Eds.), The evolution of artificial intelligence in higher education (pp. 63-81). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83549-486-820241004 DOI: https://doi.org/10.1108/978-1-83549-486-820241004
Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977-997. DOI: https://doi.org/10.1007/s11165-024-10176-3
Aleksandara, H. (2023). Role of AI in education. Injuruty: Interdisciplinary Journal and Humanity, 2(3).
Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. DOI: https://doi.org/10.3390/su132212902
Bada, S. O., & Olusegun, S. (2015). Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education, 5(6), 66-70.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1-26. DOI: https://doi.org/10.1146/annurev.psych.52.1.1
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, O., & Mariman, R. (2024). Generative AI can harm learning. SSRN, 4895486. DOI: https://doi.org/10.2139/ssrn.4895486
Bosch. (2024). Bosch tech compass 2024. https://assets.bosch.com/media/en/global/stories/technology_report_tech_compass_2024/bosch-tech-compass-2024.pdf
Chang, Y., Lee, S., Wong, S. F., & Jeong, S. (2022). AI-powered learning application use and gratification: An integrative model. Information Technology & People, 35(7). DOI: https://doi.org/10.1108/ITP-09-2020-0632
Chang, T., Wang, H., MacDonald, A., Song, M., Lai, S., & Hsieh, S. (2022). Enhancing student creativity through an interdisciplinary, project-oriented problem-based learning undergraduate curriculum. Thinking Skills and Creativity, 46. DOI: https://doi.org/10.1016/j.tsc.2022.101173
Cropley, A. (2005). Creativity and problem-solving: Implications for classroom assessment. British Psychological Society Monograph.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
Digital Education Council. (2024, August 28). Survey: 86% of students already use AI in their studies. Campus Technology. https://campustechnology.com/articles/2024/08/28/survey-86-of-students-already-use-ai-in-their-studies.aspx
Dunning, D., & Kruger, J. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121-1134. DOI: https://doi.org/10.1037//0022-3514.77.6.1121
Edeni, C. A., Adeleye, O. O., & Adeniyi, I. S. (2024). The role of AI-enhanced tools in overcoming socioeconomic barriers in education: A conceptual analysis. World Journal of Advanced Research and Reviews, 21(3), 944-951. https://doi.org/10.30574/wjarr.2024.21.3.0780 DOI: https://doi.org/10.30574/wjarr.2024.21.3.0780
Enders, C. K. (2010). Applied missing data analysis. New York, NY. Guilford Press. Everitt, BS (1981). A Monte Carlo investigation of the likelihood ratio test for the number of components in a mixture of normal distributions. Multivariate Behavioral Research, 16, 171-180. DOI: https://doi.org/10.1207/s15327906mbr1602_3
Elbadiansyah, Z., Sain, H., Lawal, U. S., Thelma, C. C., & Aziz, A. L. (2024). Exploring the role of artificial intelligence in enhancing student motivation and cognitive development in higher education. Techcomp Innovations, 1(2), 59-67. https://doi.org/10.70063/techcompinnovations.v1i2.47 DOI: https://doi.org/10.70063/techcompinnovations.v1i2.47
Enriquez Canto, Y., Zapater Ferrer, E., & Díaz Gervasi, G. M. (2021). Disposición, habilidades del pensamiento crítico y éxito académico en estudiantes universitarios: Metaanálisis. Revista Complutense de Educación, 32(4), 525-536. https://doi.org/10.5209/rced.70748 DOI: https://doi.org/10.5209/rced.70748
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2021). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37-56. https://doi.org/10.1177/14757257211037149 DOI: https://doi.org/10.1177/14757257211037149
Gödel, K. (1931). Diskussion zur Grundlegung der Mathematik. Erkenntnis, 2(1), 135-151. DOI: https://doi.org/10.1007/BF02028146
Guan, J., He, X., Su, Y., & Zhang, X. A. (2025). The Dunning-Kruger effect and artificial intelligence: Knowledge, self-efficacy and acceptance. Management Decision. https://doi.org/10.1108/MD-06-2023-0893 DOI: https://doi.org/10.1108/MD-06-2023-0893
Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students' attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Educational Technology, 27, 11635-11667. https://doi.org/10.1007/s10639-022-11086-5 DOI: https://doi.org/10.1007/s10639-022-11086-5
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15, 12451. DOI: https://doi.org/10.3390/su151612451
Katz, E., Gurevitch, M., & Haas, H. (1973). On the use of the mass media for important things. American Sociological Review, 38(2), 164-181. DOI: https://doi.org/10.2307/2094393
Kozikoğlu, İ. (2019). Investigating critical thinking in prospective teachers: Metacognitive skills, problem solving skills and academic self-efficacy. Journal of Social Studies Education Research, 10.
Lin, S., Mastrokoukou, S., Longobardi, C., Bozzato, P., Gastaldi, F., & Berchiatti, M. (2022). Students' transition into higher education: The role of self-efficacy, regulation strategies, and academic achievements. Higher Education Quarterly, 77(1), 121-137. DOI: https://doi.org/10.1111/hequ.12374
Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26-29. https://doi.org/10.1108/LHTN-01-2023-0009 DOI: https://doi.org/10.1108/LHTN-01-2023-0009
Lytras, M. D., Alkhaldi, A., Malik, S., Serban, A. C., & Aldosemani, T. (2024). The artificial intelligence (AI) landscape in higher education (HE): Current developments, opportunities, and threats. In M. D. Lytras, A. Alkhaldi, S. Malik, A. C. Serban, & T. Aldosemani (Eds.), The evolution of artificial intelligence in higher education (pp. 1-10). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83549-486-820241001 DOI: https://doi.org/10.1108/978-1-83549-486-820241001
Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., & Darwis, A. (2023). Exploring artificial intelligence in academic essay: Higher education student's perspective. International Journal of Educational Research Open, 5, 100296. https://doi.org/10.1016/j.ijedro.2023.100296 DOI: https://doi.org/10.1016/j.ijedro.2023.100296
Mantas, C., Malik, S., & Karapetsas, V. (2024). The integration and development of AI (artificial intelligence) in higher education (HE); Challenges, innovations, and recommendations for the academics. In M. D. Lytras, A. Alkhaldi, S. Malik, A. C. Serban, & T. Aldosemani (Eds.), The evolution of artificial intelligence in higher education (pp. 147-160). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83549-486-820241009 DOI: https://doi.org/10.1108/978-1-83549-486-820241009
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27, 12-14.
McKinley, J. (2013). Displaying critical thinking in EFL academic writing: A discussion of Japanese to English contrastive rhetoric. RELC Journal, 44(2), 195-208. https://doi.org/10.1177/0033688213488386 DOI: https://doi.org/10.1177/0033688213488386
Möller, M., et al. (2024). Revolutionising distance learning: A comparative study of learning progress with AI-driven tutoring. arXiv preprint, arXiv:2403.14642.
Molina-Lopez, M. M., González-Pérez, S., García-Centeno, M. C., & Martínez-Martínez, M. (2024). Climbing the Ladder: An Authentic Leadership Program Boosting Adolescent Girls’ Potential. SAGE Open, 14(4), 21582440241307697. https://doi.org/10.1177/21582440241307697 DOI: https://doi.org/10.1177/21582440241307697
Motlagh, N. Y., Khajavi, M., Sharifi, A., & Ahmadi, M. (2023). The impact of artificial intelligence on the evolution of digital education: A comparative study of OpenAI text generation tools. Computers and Education: Artificial Intelligence, 4, 100147.
Ofosu-Ampong, K. (2023). Gender differences in perception of artificial intelligence-based tools. Journal of Digital Art & Humanities, 4(2), 52-56. https://doi.org/10.33847/2712-8149.4.2_6 DOI: https://doi.org/10.33847/2712-8149.4.2_6
Omar, H. A. E.-H., & Zoube, R. R. (2024). The relationship between critical thinking skills and self-efficacy among fourth year students academic year at University of Jordan in light of some variables. Jordanian Educational Journal, 9(1), 240-264. https://doi.org/10.46515/jaes.v9i1.471 DOI: https://doi.org/10.46515/jaes.v9i1.471
Rodríguez-Ruiz, J., Marín-López, I., & Espejo-Siles, R. (2025). Is artificial intelligence use related to self-control, self-esteem and self-efficacy among university students? Educational Technology, 30, 2507-2524. https://doi.org/10.1007/s10639-024-12906-6 DOI: https://doi.org/10.1007/s10639-024-12906-6
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432-448). Routledge.
Rubin, A. M. (2009). Uses-and-gratifications perspective on media effects. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (pp. 181-200). Routledge. https://doi.org/10.4324/9780203877111 DOI: https://doi.org/10.4324/9780203877111-14
Rubio-Andrés, M., Ramos-González, M. M., Molina-López, M. M., & Sastre-Castillo, M. A. (2023). Training higher education students for employability skills: Is it worth it?. Entrepreneurship and Sustainability Issues, 10(4), 390. http://doi.org/10.9770/jesi.2023.10.4(24) DOI: https://doi.org/10.9770/jesi.2023.10.4(24)
Shahzad, M. F., Xu, S., & Zahid, H. (2024). Exploring the impact of generative AI-based technologies on learning performance. Education and Information Technologies, 1-26.
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417-453. https://doi.org/10.3102/00346543075003417 DOI: https://doi.org/10.3102/00346543075003417
Tino, C., & Fedeli, M. (2024). The importance of soft skills for employability and the role of higher education: Undergraduates' perceptions. Italian Journal of Educational Research, 33, 205-218. https://doi.org/10.7346/sird-022024-p205
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433-460. DOI: https://doi.org/10.1093/mind/LIX.236.433
UNESCO. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development (UNESCO Working Papers on Education Policy, No. 7). https://unesdoc.unesco.org/ark:/48223/pf0000370307
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes' artificial intelligence capability on students' self-efficacy, creativity and learning performance. Educational Technology, 28, 4919-4939. https://doi.org/10.1007/s10639-022-11338-4 DOI: https://doi.org/10.1007/s10639-022-11338-4
Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide. MIT Press. DOI: https://doi.org/10.7551/mitpress/6699.001.0001
Wecks, J. O., Voshaar, J., Plate, B. J., & Zimmermann, J. (2024). Generative AI usage and exam performance. SSRN. DOI: https://doi.org/10.2139/ssrn.4812513
White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461-481. https://doi.org/10.1037/0033-2909.91.3.461 DOI: https://doi.org/10.1037//0033-2909.91.3.461
Yeruva, A. R. (2023). Providing a personalized healthcare service to the patients using AIOPs monitoring. Eduvest-Journal of Universal Studies, 3(2), 327-334. DOI: https://doi.org/10.36418/eduvest.v3i2.742
Yilmaz, R., & Yilmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. DOI: https://doi.org/10.1016/j.caeai.2023.100147
Zanetti, M., Rendina, S., Piceci, L., & Peluso Cassese, F. (2020). Potential risks of artificial intelligence in education. Form@re - Open Journal Per La Formazione in Rete, 20(1), 368-378. https://doi.org/10.13128/form-8113
Zapata, S., & Onwuegbuzie, A. (2023). Threats or opportunities that undermine or facilitate first-year university. Journal of Higher Education Theory and Practice, 23, 115. https://doi.org/10.33423/jhetp.v23i5.5944 DOI: https://doi.org/10.33423/jhetp.v23i5.5944
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021(1), 8812542.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7 DOI: https://doi.org/10.1186/s40561-024-00316-7
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