Palabras clave
Inteligencia artificial, educación, contemporáneo, aprendizaje electrónico, enseñanza en línea, aprendizaje profundo
Resumen
El término «Inteligencia Artificial» fue acuñado en 1956 en una conferencia en Dartmouth College, y desde entonces, este ha experimentado un desarrollo constante y ha evolucionado de manera significativa. Algunos de los pioneros más destacados incluyen a John McCarthy, Marvin Minsky, Allen Newell y Herbert A. Simon. La aplicación de la inteligencia artificial en la educación ha aumentado considerablemente a nivel mundial en la dinámica era digital. El objetivo de la investigación es analizar bibliométricamente las incidencias de la IA en la educación contemporánea. La metodología contiene un Prisma de tres bases de datos fundamentales Scopus (n=390), Mendeley (n=113) y Science Direct (n=3.594), para un total de n=4.097 artículos en idioma inglés y español. La revisión sistematizada de la literatura reciente tiene un enfoque mixto, cuantitativos y cualitativos empleando varios paradigmas de la investigación en función del objetivo, se obtiene que la IA ha revolucionado la educación, ofreciendo soluciones personalizadas y eficientes para mejorar el aprendizaje de los estudiantes. En las principales conclusiones se plantea que en los términos teóricos de mayor impacto están los estudiantes como elemento principal de la IA de la educación contemporánea. Por otra parte, los profesores juegan un papel fundamental en este proceso a través de sus metodologías y el uso de estas tecnologías. Así mismo están los currículos educacionales mediante la toma de decisiones en los colegios y universidades que están apostando por nuevos modelos tecnológicos educativos.
Referencias
Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., Chouchane, L., EINatour, R., Abd-Alrazaq, A., Pandas, S., & Sheikh, J. (2022). Wearable artificial intelligence for assessing physical activity in high school children. Sustainability, 15(1), 638. https://doi.org/10.3390/su15010638
Link DOI | Link Google Scholar
Alhumaid, K., Naqbi, S.A., Elsori, D., & Mansoori, M.A. (2023). The adoption of artificial intelligence applications in education. International Journal of Data and Network Science, 7(1), 457-466. https://doi.org/10.5267/j.ijdns.2022.8.013
Link DOI | Link Google Scholar
Allaoua-Chelloug, S., Ashfaq, H., Alsuhibany, S., Shorfuzzaman, M., Alsufyani, A., Jalal, A., & Park, J. (2023). Real objects understanding using 3D haptic virtual reality for e-learning education. Computers, Materials & Continua, 74(1), 1607-1624. https://doi.org/10.32604/cmc.2023.032245
Link DOI | Link Google Scholar
Aloisi, C. (2023). The future of standardised assessment: Validity and trust in algorithms for assessment and scoring. European Journal of Education, 58(1), 98-110. https://doi.org/10.1111/ejed.12542
Link DOI | Link Google Scholar
Arbelaez-Ossa, L., Rost, M., Lorenzini, G., Shaw, D.M., & Elger, B.S. (2023). A smarter perspective: Learning with and from AI-cases. Artificial Intelligence in Medicine, 135, 102458. https://doi.org/10.1016/j.artmed.2022.102458
Link DOI | Link Google Scholar
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Link DOI | Link Google Scholar
Bañeres, D., Rodríguez-González, M.E., Guerrero-Roldán, A.E., & Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1), 1-25. https://doi.org/10.1186/s41239-022-00371-5
Link DOI | Link Google Scholar
Cerqueira, J.M., Cleto, B., Moura, J.M., Sylla, C., & Ferreira, L. (2023). Potentiating learning through augmented reality and serious games. In A.Y.C. Nee & S.K. Ong (eds), Springer Handbook of Augmented Reality (pp. 369-390). Springer. https://doi.org/10.1007/978-3-030-67822-7_15
Link DOI | Link Google Scholar
Chai, C.S., Chiu, T.K.F., Wang, X., Jiang, F., & Lin, X.F. (2023). Modeling Chinese Secondary School students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1), 605. https://doi.org/10.3390/su15010605
Link DOI | Link Google Scholar
Dabbous, A., & Boustani, N.M. (2023). Digital explosion and entrepreneurship education: Impact on promoting entrepreneurial intention for business students. Journal of Risk and Financial Management, 16(1), 27-48. https://doi.org/10.3390/jrfm16010027
Link DOI | Link Google Scholar
Dong, Y. (2022). Application of artificial intelligence software based on semantic web technology in english learning and teaching. Journal of Internet Technology, 23(1), 143-152. https://doi.org/10.53106/160792642022012301015
Link DOI | Link Google Scholar
Ednie, G., Kapoor, T., Koppel, O., Piczak, M.L., Reid, J.L., Murdoch, A.D., Cook, C.N., Sutherland, W.J., & Cooke, S.J. (2022). Foresight science in conservation: Tools, barriers, and mainstreaming opportunities. Ambio, 52(2), 411-424. https://doi.org/10.1007/s13280-022-01786-0
Link DOI | Link Google Scholar
Flores-Vivar, J., & García-Peñalvo, F. (2023). Reflexiones sobre la ética, potencialidades y desafíos de la inteligencia artificial en el marco de una educación de calidad (ODS4). [Reflexiones sobre la ética, potencialidades y desafíos de la IA en el marco de la Educación de Calidad (ODS4)]. Comunicar, 74, 37-47. https://doi.org/10.3916/C74-2023-03
Link DOI | Link Google Scholar
García-Orosa, B., Canavilhas, J., & Vázquez-Herrero, J. (2023). Algorithms and communication: A systematized literature review. [Algoritmos y comunicación: Revisión sistematizada de la literatura]. Comunicar, 74, 9-21. https://doi.org/10.3916/C74-2023-01
Link DOI | Link Google Scholar
Hinojo-Lucena, F., Aznar-Díaz, I., Cáceres-Reche, M., & Romero-Rodríguez, J. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51-60. https://doi.org/10.3390/educsci9010051
Link DOI | Link Google Scholar
Ho, M., Le, N., Mantello, P., Ho, M., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72, 102-166. https://doi.org/10.1016/j.techsoc.2022.102166
Link DOI | Link Google Scholar
Hort, M., Moussa, R., & Sarro, F. (2023). Multi-objective search for gender-fair and semantically correct word embeddings. Applied Soft Computing, 133, 109916. https://doi.org/10.1016/j.asoc.2022.109916
Link DOI | Link Google Scholar
Hu, Y., Fu, J.S., & Yeh, H. (2023). Developing an early-warning system through robotic process automation: Are intelligent tutoring robots as effective as human teachers? Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2022.2160467
Link DOI | Link Google Scholar
Hua-Hu, K. (2023). An exploration of the key determinants for the application of AI-enabled higher education based on a hybrid soft-computing technique and a DEMATEL approach. Expert Systems with Applications, 212, 118-762. https://doi.org/10.1016/j.eswa.2022.118762
Link DOI | Link Google Scholar
Huang, A.Y.Q., Lu, O.H.T., & Yang, S.J.H. (2023). Effects of artificial Intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers and Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
Link DOI | Link Google Scholar
Hussain, A. (2023). Use of artificial intelligence in the library services: prospects and challenges. Library Hi Tech News, 40(2), 15-17. https://doi.org/10.1108/LHTN-11-2022-0125
Link DOI | Link Google Scholar
Kaur, D., Uslu, S., Rittichier, K.J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1-38. https://doi.org/10.1145/3491209
Link DOI | Link Google Scholar
King, M.R., & chatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1-2. https://doi.org/10.1007/s12195-022-00754-8
Link DOI | Link Google Scholar
Lahza, H., Khosravi, H., & Demartini, G. (2023). Analytics of learning tactics and strategies in an online learnersourcing environment. Journal of Computer Assisted Learning, 39(1), 94-112. https://doi.org/10.1111/jcal.12729
Link DOI | Link Google Scholar
Li, C., Zheng, P., Yin, Y., Wang, B., & Wang, L. (2023). Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology, 40, 75-101. https://doi.org/10.1016/j.cirpj.2022.11.003
Link DOI | Link Google Scholar
Matthew, J., Pagea, J.E., McKenziea, P.M., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuiness, L.A., … Moher, D. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016
Link DOI | Link Google Scholar
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4
Link DOI | Link Google Scholar
Picciano, A.G. (2019). Artificial intelligence and the academy’s loss of purpose. Online Learning Journal, 23(3), 270-284. https://doi.org/10.24059/olj.v23i3.2023
Link DOI | Link Google Scholar
Sayed, B.T., Madanan, M., & Biju, N. (2023). An efficient artificial intelligence-based educational data mining approach for higher education and early recognition system. SN Computer Science, 4(2), 130. https://doi.org/10.1007/s42979-022-01562-7
Link DOI | Link Google Scholar
Shen, C., & Tan, Y. (2023). Effect evaluation model of computer aided physical education teaching and training based on artificial intelligence. Computer-Aided Design and Applications, 20(S5), 106-115. https://doi.org/10.14733/cadaps.2023.S5.106-115
Link DOI | Link Google Scholar
Sun, F., & Ye, R. (2023). Moral considerations of artificial intelligence. Science and Education, 32(1), 1-17. https://doi.org/10.1007/s11191-021-00282-3
Link DOI | Link Google Scholar
Tongkachok, K., Ali, B.M., Ganguly, M., Kumar, S., Malathi, M., & Subramanian, M. (2023). A detailed exploration of artificial intelligence and digital education and its sustainable impact on the youth of society. In S. Yadav., A. Haleem, P.K. Arora., & H. Kumar, H. (eds), Proceedings of Second International Conference in Mechanical and Energy Technology (pp. 139-146). Springer. https://doi.org/10.1007/978-981-19-0108-9_15
Link DOI | Link Google Scholar
Ursani, Z., & Ursani, A.A. (2023). The theory of probabilistic hierarchical learning for classification. Annals of Emerging Technologies in Computing, 7(1), 61-74. https://doi.org/10.33166/AETiC.2023.01.005
Link DOI | Link Google Scholar
Vila, E.M.S., & Penín, M.L. (2007). Introduction to special issue AI techniches applied in education. Inteligencia Artificial, 11(33), 7-12. https://doi.org/10.4114/ia.v11i33.914
Link DOI | Link Google Scholar
Wang, X., Liu, Q., Pang, H., Tan, S.C., Lei, J., Wallace, M.P., & Li, L. (2023). What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis. Computers and Education, 194, 104703. https://doi.org/10.1016/j.compedu.2022.104703
Link DOI | Link Google Scholar
Zhen, R., Song, W., He, Q., Cao, J., Shi, L., & Luo, J. (2023). Human-computer interaction system: A survey of talking-head generation. Electronics, 12(1), 218-239. https://doi.org/10.3390/electronics12010218
Link DOI | Link Google Scholar
Zhou, W. (2023). The development system of local music teaching materials based on deep learning. Optik, 273, 170421. https://doi.org/10.1016/j.ijleo.2022.170421
Link DOI | Link Google Scholar
Ficha técnica
Recibido: 09-02-2023
Revisado: 25-03-2023
Aceptado: 02-05-2023
OnlineFirst: 30-06-2023
Fecha publicación: 01-10-2023
Tiempo de revisión del artículo : 44 (en días) | Media de tiempo de revisión de los manuscritos del número 77: 32 (en días)
Tiempo de aceptación del artículo: 82 (en días) | Media tiempo aceptación de los manuscritos del número 77: 76 (en días)
Tiempo de edición OnlineFirst: 189 (en días) | Media tiempo edición de los OnlineFirst del número 77: 183 (en días)
Tiempo de publicacicón final del artículo: 234 (en días) | Media tiempo de publicación final de los articulos del número 77: 228 (en días)
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José-Ramón Sanabria-Navarro, Universidad de Córdoba (Colombia)
