Unint la motivació i la IA en educació: una perspectiva de la Teoria de l'Activitat
DOI:
https://doi.org/10.1344/der.2024.45.59-67Paraules clau:
motivació intrínseca, enfocament d'activitat, Teoria de l'Activitat, Intel·ligència Artificial, educacióResum
Després de la pandèmia, la investigació sobre Intel·ligència Artificial (IA) en educació ha augmentat a nivell mundial. Pocs estudis abans de la pandèmia van abordar la motivació intrínseca als estudiants, crucial per a la retenció del coneixement. Aquest estudi analitza com es tracta aquest tema en investigacions recents, fent una revisió de la literatura i una anàlisi crítica del discurs sota el marc teòric de la Teoria de l'Activitat (AT). L'objectiu és identificar la cobertura de les relacions entre nodes al sistema d'activitat educativa, amb especial atenció al subjecte (estudiants) i l'objecte, que reflecteix la naturalesa motivada de l'activitat humana. L'anàlisi va incloure 69 articles de Scopus publicats des del 2020. Els resultats mostren la cobertura d'algunes relacions, com Subjecte-Eines (interacció dels estudiants amb IA), Eines-Objecte (desenvolupament d'IA) i Eines-Comunitat (adaptació de la IA) a la comunitat educativa). La relació Subjecte-Objecte roman inexplorada. Les implicacions pràctiques inclouen un reenfocament en la motivació intrínseca, emfatitzant necessitats epistemològiques, significat i elecció, avaluant els beneficis i els riscos de la IA en casos educatius específics. Les implicacions teòriques impliquen explorar com mantenir la motivació intrínseca dels estudiants en el context de la implementació de IA.
Referències
Aguayo, M. A. C., Ulloa-Cazarez, R. L., & Artemova, I. (2024). Implicaciones ético-pedagógicas de la inteligencia artificial en la educación en línea. In F. Ramón Fernández (Ed.), Ciencia de datos y perspectivas de la inteligencia artificial (pp. 551-571). Tirant lo Blanch.
Bakhurst, D. (2009). Reflections on activity theory. Educational Review, 61(2), 197–210. https://doi.org/10.1080/00131910902846916
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools. Sustainability, 16(2), 781. https://doi.org/10.3390/su16020781
Cagnini, H., Dôres, S., Freitas, A., & Barros, R. (2023). A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review, 38. https://doi.org/10.1017/S0269888923000024
Cheng, C. T., Chen, C. C., Fu, C. Y., Chaou, C. H., Wu, Y. T., Hsu, C. P., ... & Liao, C. H. (2020). Artificial intelligence-based education assists medical students’ interpretation of hip fracture. Insights into Imaging, 11, 1-8. https://doi.org/10.1186/s13244-020-00932-0
Cornwall, J., Hildebrandt, S., Champney, T. H., & Goodman, K. (2023). Ethical concerns surrounding artificial intelligence in anatomy education: Should AI human body simulations replace donors in the dissection room? Anatomical sciences education. https://doi.org/10.1002/ase.2335
Crawford, J., Cowling, M., & Allen, K. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice, 20(3). https://doi.org/10.53761/1.20.3.02.
Deci, E. L., & Ryan, R. M. (2002). Self-determination research: Reflections and future directions. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 431–441). University of Rochester Press.
Deng, X., & Yu, Z. (2023). A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education. Sustainability, 15(4). https://doi.org/10.3390/su15042940.
Dodgson, J. (2021). Critical Analysis: The Often-Missing Step in Conducting Literature Review Research. Journal of Human Lactation, 37(1), 27 - 32. https://doi.org/10.1177/0890334420977815
Dogan, M., Dogan, T., & Bozkurt, A. (2023). The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies. Applied Sciences, 13(5). https://doi.org/10.3390/app13053056.
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Orienta-Konsultit.
Fonseca-Soares, D., Galvinicio, J., Eliziário, S., & Ramos-Ridao, Á. (2022). A Bibliometric Analysis of the Trends and Characteristics of Railway Research. Sustainability, 14(21). https://doi.org/10.3390/su142113956.
Howard, J. L., Bureau, J. S., Guay, F., Chong, J. X., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis from self-determination theory. Perspectives on Psychological Science, 16(6), 1300-1323. https://doi.org/10.1177/1745691620966789
Ifenthaler, D., & Schumacher, C. (2023). Reciprocal issues of artificial and human intelligence in education. Journal of Research on Technology in Education, 55(1), 1 - 6. https://doi.org/10.1080/15391523.2022.2154511.
Ivanov, S. (2023). The dark side of artificial intelligence in higher education. The Service Industries Journal, 43(15-16), 1055-1082. https://doi.org/10.1080/02642069.2023.2258799
Johnson, M. N., & McLean, E. (2020). Discourse Analysis. International Encyclopedia of Human Geography. In A. Kobayashi (Ed.), International Encyclopedia of Human Geography (Second Edition) (pp. 377–383). Elsevier. https://doi.org/10.1016/B978-0-08-102295-5.10814-5
Kim, S. W. (2023). Change in Attitude toward Artificial Intelligence through Experiential Learning in Artificial Intelligence Education. International Journal on Advanced Science, Engineering & Information Technology, 13(5). https://doi.org/10.18517/ijaseit.13.5.19039
Klimova, B., Pikhart, M., & Kacetl, J. (2023). Ethical issues of the use of AI-driven mobile apps for education. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1118116
Krive, J., Isola, M., Chang, L., Patel, T., Anderson, M., & Sreedhar, R. (2023). Grounded in reality: artificial intelligence in medical education. JAMIA Open, 6(2), ooad037. https://doi.org/10.1093/jamiaopen/ooad037
Leontiev, A. N. (1978). Activity, Consciousness, and Personality. Prentice-Hall.
Leontiev, A. N. (2001). Lektsii po obshchei psikhologii [Lectures on General Psychology]. Smysl.
Leontiev, A. N. (1936/2009). Psikhologicheskoe issledovanie detskikh interesov [Psychological investigation ofchildren’s interests]. In A. N. Leontiev (Ed.), Psikhologicheskie osnoverazvitiya rebenka i obucheniya [Psychological Foundations of Child Development and Education] (pp.46-100). Smysl.
Leontiev, D. A. (2012). Personal meaning as the basis of motivational processes. In D. Leontiev (Ed.), Motivation, consciousness, and self-regulation (pp. 65–78). Nova.
Leontiev, D. A. (2016). A.N. Leontiev's concept of motive and the issue of the quality of motivation. Lomonosov Psychology Journal, 2, 3-18. https://msupsyj.ru/en/articles/article/6661/
Liu, F., Lin, Z., & Qu, Y. (2020). System Dynamics Modeling for Online Encyclopedias Based on Activity Theory. Proceedings of the 6th International Conference on Frontiers of Educational Technologies (pp. 173-177). https://doi.org/10.1145/3404709.3404753.
Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50, 2824-2838. https://doi.org/10.1111/BJET.12861
Miettinen, R. (2005). Object of Activity and Individual Motivation. Mind, Culture, and Activity, 12, 52-69. https://doi.org/10.1207/s15327884mca1201_5.
Page, M. J., Mckenzie, J. E., Bossuyt, P.M. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(89). https://doi.org/10.1186/s13643-021-01626-4
Pantelimon, F., Bologa, R., Toma, A., & Posedaru, B. (2021). The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic. Sustainability, 13(23). https://doi.org/10.3390/su132313501.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
Sansone, C., & Morgan, C. (1992). Intrinsic motivation and education: Competence in context. Motivation and emotion, 16(3), 249-270. https://doi.org/10.1007/BF00991654
Segbenya, M., Bervell, B., Frimpong-Manso, E., Otoo, I. C., Andzie, T. A., & Achina, S. (2023). Artificial intelligence in higher education: Modelling the antecedents of artificial intelligence usage and effects on 21st century employability skills among postgraduate students in Ghana. Computers and Education: Artificial Intelligence, 5, 100188. https://doi.org/10.1016/j.caeai.2023.100188
Schröder, L. M. U., Wals, A. E., & Van Koppen, C. S. A. (2020). Analysing the state of student participation in two Eco-Schools using Engeström’s Second Generation Activity Systems Model. Environmental Education Research, 26(8), 1088-1111. https://doi.org/10.1080/13504622.2020.1779186
Stuart, D., & Petersen, D. (2022). Citation Analysis and Medical Reference: PubMed, Web of Science, and Scopus. Journal of Electronic Resources in Medical Libraries, 19, 121 - 126. https://doi.org/10.1080/15424065.2022.2150352
Su, J., & Zhong, Y. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education: Artificial Intelligence, 3, 100072. https://doi.org/10.1016/j.caeai.2022.100072
Teng, L., Tan, Q., & Ehsani, A. (2021). Assessing the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of cloud-based e-learning systems in the COVID-19 era. Kybernetes, 51, 2795-2813. https://doi.org/10.1108/k-12-2020-0838
Vasileva, O., & Balyasnikova, N. (2019). (Re) Introducing Vygotsky’s thought: from historical overview to contemporary psychology. Frontiers in psychology, 10, 411651. https://doi.org/10.3389/fpsyg.2019.01515
Vinogradova E.M. & Kotliar I.A. (2006). Understanding A.N. Leontiev's Activity Theory: Y. Engestrom and His Works. Kul'turno-istoricheskaya psikhologiya = Cultural-Historical Psychology, 2(4), 74-78. https://psyjournals.ru/en/journals/chp/archive/2006_n4/Korepanova
Wong, L. H., Chan, T. W., Chen, W., Looi, C. K., Chen, Z. H., Liao, C. C., King, R. B., & Wong, S. L. (2020). IDC theory: interest and the interest loop. Research and Practice in Technology Enhanced Learning, 15, 1-16. https://doi.org/10.1186/s41039-020-0123-2
Yang, D., Oh, E.-S., Wang, Y. (2020). Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot. Sustainability,12(19), 8000. https://doi.org/10.3390/su12198000
Yang, H., Shankar, A., & Velliangiri, S. (2023). Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services. International Journal of e-Collaboration, 19(2), 1-18. https://doi.org/10.4018/ijec.316655.
Zhao, Y., & Gao, L. (2023). Classroom Design and Application of Art Design Education Based on Artificial Intelligence. International Journal of Information Technology and Web Engineering (IJITWE), 18(1), 1-18. http://doi.org/10.4018/IJITWE.334008
Zhang, K., & Aslan, A. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/J.CAEAI.2021.100025
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