DOI:

https://doi.org/10.14483/22486798.22039

Publicado:

2024-08-12

Edição:

v. 29 n. 1 (2024): Lenguaje, medios audiovisuales y tecnología (Ene-Jun)

Seção:

Lenguaje, medios audiovisuales y tecnología

Categorias

Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)

Artificial Intelligence in Schools: A Systematic Review (2019-2023)

Inteligência artificial nas escolas: uma revisão sistemática (2019 - 2023)

Autores

  • Robin Bustamante Bula Universidad San Buenaventura
  • Aureliano Camacho Bonilla Université Paris Nanterre, París

Palavras-chave:

Inteligencia artificial, formación de docentes, pedagogía, ética, gestión educativa (es).

Palavras-chave:

Artificial intelligence, teacher training, pedagogy, ethics, educational management (en).

Palavras-chave:

Inteligência artificial, formação de professores, pedagogia, ética, gestão educacional (pt).

Resumo (es)

La inteligencia artificial (IA) ha surgido como una herramienta innovadora, con programas como ChatGPT, Gemini, entre otros, con un gran potencial para transformar la educación, y para adaptarse a plataformas digitales existentes y revolucionando los procesos de enseñanza. Este artículo tiene el objetivo de proporcionar una visión amplia y equilibrada del panorama actual de la IA en las escuelas, para lo cual se realizó una revisión sistemática, mediante la metodología Prisma (preferred reporting items for systematic reviews and meta-analyses), a partir de la cual se encontraron 52 artículos indexados en la base de datos Scopus durante el periodo de 2019 a 2023, que abordaban la temática de la IA en las escuelas. Según los resultados, hay cuatro áreas temáticas clave que destacan el impacto de la IA: (a) procesos de enseñanza; (b) pedagogía, currículo y formación docente; (c) gestión educativa, y (d) implicaciones éticas. Se concluyó que esta tecnología presenta un gran potencial para transformar la educación, por medio de herramientas innovadoras; mejorar la calidad del aprendizaje; optimizar la gestión educativa, y abordar desafíos como la personalización de la enseñanza y la evaluación del rendimiento. No obstante, su implementación debe ser planificada meticulosamente, y enmarcada en principios éticos sólidos y acompañada de un proceso de formación docente adecuado para garantizar el uso responsable y efectivo de esta tecnología en el ámbito educativo.

Resumo (en)

Artificial intelligence (AI) has emerged as an innovative tool, with applications such as ChatGPT and Gemini, among others, that show great potential to transform education, adapting to existing digital platforms and revolutionizing teaching processes. This article aims to provide a complete and balanced view of the current landscape of AI in schools, for which a systematic review was carried out using the PRISMA methodology. This allowed finding 52 articles indexed in the Scopus database between 2019 and 2023, which addressed the topic of AI in schools. The results revealed four key thematic areas that highlight the impact of AI: 1) teaching processes; 2) pedagogy, curriculum, and teacher training; 3) educational management; and 4) ethical implications. It was concluded that this technology has great potential to transform education through innovative tools aimed at improving the quality of learning, optimizing educational management, and addressing challenges such as the personalization of teaching and performance assessment. However, its implementation must be meticulously planned, framed in solid ethical principles, and accompanied by an adequate teacher training process that ensures its responsible and effective use in the field of education. This is why the permanent collaboration of teachers, education professionals, researchers, and policy makers is required, in order to leverage the opportunities offered by AI and work together to build an educational future that is of higher quality and more equitable and inclusive.

Resumo (pt)

A inteligência artificial (IA) surgiu como uma ferramenta inovadora, com programas como ChatGPT, Gemini, entre outros, com grande potencial para transformar a educação, adaptando-se às plataformas digitais existentes e revolucionando os processos de ensino. Este artigo tem como objetivo fornecer uma visão completa e equilibrada do panorama atual da IA nas escolas, para o qual foi realizada uma revisão sistemática, utilizando a metodologia PRISMA, que permitiu encontrar 52 artigos indexados na base de dados Scopus durante o período de 2019 até 2023, que abordou o tema IA nas escolas. Os resultados revelaram quatro áreas temáticas principais que destacam o impacto da IA: 1) Processos de ensino; 2) Pedagogia, currículo e formação docente; 3) Gestão educacional; e 4) Implicações éticas. Concluiu-se que esta tecnologia tem grande potencial para transformar a educação, através de ferramentas inovadoras para melhorar a qualidade da aprendizagem, otimizar a gestão educacional e enfrentar desafios como a personalização do ensino e a avaliação de desempenho. No entanto, a sua implementação deve ser meticulosamente planejada e enquadrada em sólidos princípios éticos, bem como ser acompanhada de um processo de formação de professores adequado para garantir a utilização responsável e eficaz desta tecnologia no campo educativo, razão pela qual é necessária a colaboração permanente dos professores, profissionais, pesquisadores da educação e atores políticos para aproveitarem as oportunidades oferecidas pela IA e trabalharem em conjunto para construir um futuro educativo mais equitativo, inclusivo e de qualidade.

Biografia do Autor

Robin Bustamante Bula, Universidad San Buenaventura

Magíster en Ciencias de la Educación. Universidad San Buenaventura, Colombia. Correo electrónico: robinnet@hotmail.com

Aureliano Camacho Bonilla, Université Paris Nanterre, París

Doctor en Urbanismo y ordenamiento del territorio. Université Paris Nanterre, París. Correo electrónico: acamacho83@uan.edu.co

 

Referências

A’mar, F. y Eleyan, D. (2022). Effect of principal s technology leadership on teacher´s technology integration. International Journal of Instruction, 15(1), 781-798. https://doi.org/10.29333/iji.2022.15145a

Alexandre, F., Becker, J., Comte, M.-H., Lagarrigue, A., Liblau, R., Romero, M. y Viéville, T. (2021). Why, What and How to help each citizen to understand artificial intelligence? KI: Kunstliche Intelligenz, 35(2), 191-199. https://doi.org/10.1007/s13218-021-00725-7

An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C. y Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187-5208. https://doi.org/10.1007/s10639-022-11286-z

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D. y Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099

Bach, N. X., Thanh, P. D. y Oanh, T. T. (2020). Question analysis towards a Vietnamese question answering system in the education domain. Cybernetics and Information Technologies, 20(1), 112-128. https://doi.org/10.2478/cait-2020-0008

Bonneton-Botté, N., Fleury, S., Girard, N., le Magadou, M., Cherbonnier, A., Renault, M., Anquetil, E. y Jamet, E. (2020). Can tablet apps support the learning of handwriting? An investigation of learning outcomes in kindergarten classroom. Computers and Education, 151, 103831. https://doi.org/10.1016/j.compedu.2020.103831

Chang, D. H., Lin, M. P.-C., Hajian, S. y Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. https://doi.org/10.3390/su151712921

Cheng, E. C. K. y Wang, T. (2023). Leading digital transformation and eliminating barriers for teachers to incorporate artificial intelligence in basic education in Hong Kong. Computers and Education: Artificial Intelligence, 5, 100171. https://doi.org/10.1016/j.caeai.2023.100171

Costa-Mendes, R., Oliveira, T., Castelli, M. y Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y

Dai, Y. (2023). Negotiation of epistemological understandings and teaching practices between primary teachers and scientists about artificial intelligence in professional development. Research in Science Education, 53(3), 577-591. https://doi.org/10.1007/s11165-022-10072-8

Deveci Topal, A., Dilek Eren, C. y Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241-6265. https://doi.org/10.1007/s10639-021-10627-8

Freestone, M. y Mason, J. (2019). Questions in smart digital environments. Frontiers in Education, 4, art. 98. https://doi.org/10.3389/feduc.2019.00098

Georgara, A., Kazhamiakin, R., Mich, O., Palmero Aprosio, A., Pazzaglia, J.-C., Rodríguez Aguilar, J. A. y Sierra, C. (2023). The AI4Citizen pilot: Pipelining AI-based technologies to support school-work alternation programmes. Applied Intelligence, 53(20), 24157-24186. https://doi.org/10.1007/s10489-023-04758-3

Gray, S. L. (2020). Artificial intelligence in schools: Towards a democratic future. London Review of Education, 18(2), 163-177. https://doi.org/10.14324/LRE.18.2.02

Gresse von Wangenheim, C., Hauck, J. C. R., Pacheco, F. S. y Bertonceli Bueno, M. F. (2021). Visual tools for teaching machine learning in K-12: A ten-year systematic mapping. Education and Information Technologies, 26(5), 5733–5778. https://doi.org/10.1007/s10639-021-10570-8

Guo, Q. (2022). System analysis of the learning behavior recognition system for students in a law classroom: Based on the improved SSD behavior recognition algorithm. Scientific Programming, 2022, 1-11. https://doi.org/10.1155/2022/3525266

Henze, J., Schatz, C., Malik, S. y Bresges, A. (2022). How might we raise interest in robotics, coding, artificial intelligence, STEAM and sustainable development in university and on-the-job teacher training? Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.872637

Howard, S. K., Swist, T., Gasevic, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N. y Selwyn, N. (2022). Educational data journeys: Where are we going, what are we taking and making for AI? Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100073

Hwang, Y., Choi, E. y Park, N. (2022). The development and demonstration of creative education programs focused on intelligent information technology. Journal of Curriculum and Teaching, 11(5), 155-161. https://doi.org/10.5430/jct.v11n5P155

Inusah, F., Missah, Y. M., Najim, U. y Twum, F. (2023). Agile neural expert system for managing basic education. Intelligent Systems with Applications, 17, 200178. https://doi.org/10.1016/j.iswa.2023.200178

Joo, K. H. y Park, N. H. (2022). Design artificial intelligence convergence teaching and learning model CP3 and evaluations. Journal of Curriculum and Teaching, 11(8), 291-302. https://doi.org/10.5430/jct.v11n8p291

Kajiwara, Y., Matsuoka, A. y Shinbo, F. (2023). Machine learning role playing game: Instructional design of AI education for age-appropriate in K-12 and beyond. Computers and Education: Artificial Intelligence, 5, 100162. https://doi.org/10.1016/j.caeai.2023.100162

Kandlhofer, M., Steinbauer, G., Lassnig, J., Menzinger, M., Baumann, W., Ehardt-Schmiederer, M., Bieber, R., Winkler, T., Plomer, S., Strobl-Zuchtriegl, I., Alfoldi, I. y Szalay, I. (2021). EDLRIS: A European driving license for robots and intelligent systems. KI: Kunstliche Intelligenz, 35(2), 221-232. https://doi.org/10.1007/s13218-021-00716-8

Kaufmann, E. (2021). Algorithm appreciation or aversion? Comparing in-service and pre-service teachers’ acceptance of computerized expert models. Computers and Education: Artificial Intelligence, 2, 100028. https://doi.org/10.1016/j.caeai.2021.100028

Kim, K. y Kwon, K. (2023). Exploring the AI competencies of elementary school teachers in South Korea. Computers and Education: Artificial Intelligence, 4, 100137. https://doi.org/10.1016/j.caeai.2023.100137

Kim, N. J. y Kim, M. K. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, 755914. https://doi.org/10.3389/feduc.2022.755914

Lin, X., Liu, H., Sun, Q., Li, X., Qian, H., Sun, Z. y Lam, T. L. (2022). Applying project-based learning in artificial intelligence and marine discipline: An evaluation study on a robotic sailboat platform. IET Cyber-Systems and Robotics, 4(2), 86-96. https://doi.org/10.1049/csy2.12050

Lu, W.-Y. y Fan, S.-C. (2023). Developing a weather prediction project-based machine learning course in facilitating AI learning among high school students. Computers and Education: Artificial Intelligence, 5, 100154. https://doi.org/10.1016/j.caeai.2023.100154

McKenzie, M. y Gulson, K. N. (2023). The incommensurability of digital and climate change priorities in schooling: An infrastructural analysis and implications for education governance. Research in Education, 117(1), 58-72. https://doi.org/10.1177/00345237231208658

McMahon, D. D. y Walker, Z. (2019). Leveraging emerging technology to design an inclusive future with universal design for learning. Center for Educational Policy Studies Journal, 9(3), 75-93. https://doi.org/10.26529/cepsj.639

Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E. y Nshimyumukiza, P. C. (2022). Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence, 3, 100066. https://doi.org/10.1016/j.caeai.2022.100066

Nordby, S. K., Bjerke, A. H. y Mifsud, L. (2022). Primary mathematics teachers’ understanding of computational thinking. KI: Kunstliche Intelligenz, 36(1), 35-46. https://doi.org/10.1007/s13218-021-00750-6

Page, M., McKenzie, J., Bossuyt, M., Boutron I., Hoffmann, T., Mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., McDonald, S. ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372(71). https://doi.org/10.1136/bmj.n71

Pangrazio, L. y Gaibisso, L. C. (2020). Beyond cybersafety: The need to develop social media literacies in pre-teens. Digital Education Review, 37, 49-63. https://doi.org/10.1344/DER.2020.37.49-63

Peláez, A., Jacobson, A., Trias, K. y Winston, E. (2022). The Turing teacher: Identifying core attributes for AI learning in K-12. Frontiers in Artificial Intelligence, 5, 1031450. https://doi.org/10.3389/frai.2022.1031450

Reiss, M. J. (2021). The use of AI in education: Practicalities and ethical considerations. London Review of Education, 19(1), 1-14. https://doi.org/10.14324/LRE.19.1.05

Rodríguez García, J. D., Moreno León, J., Román González, M. y Robles, G. (2020). LearningML: A tool to foster computational thinking skills through practical artificial intelligence projects. Revista de Educación a Distancia, 20(63). https://doi.org/10.6018/RED.410121

Rott, K. J., Lao, L., Petridou, E. y Schmidt-Hertha, B. (2022). Needs and requirements for an additional AI qualification during dual vocational training: Results from studies of apprentices and teachers. Computers and Education: Artificial Intelligence, 3, 100102. https://doi.org/10.1016/j.caeai.2022.100102

Saltman, K. J. (2020). Artificial intelligence and the technological turn of public education privatization: In defence of democratic education. London Review of Education, 18(2), 196-208. https://doi.org/10.14324/LRE.18.2.04

Sam, C., Naicker, N. y Rajkoomar, M. (2021). Selection of social media applications for ubiquitous learning using fuzzy TOPSIS. International Journal of Advanced Computer Science and Applications, 12(2), 231-239. https://doi.org/10.14569/IJACSA.2021.0120230

Sanusi, I. T., Olaleye, S. A., Agbo, F. J. y Chiu, T. K. F. (2022). The role of learners’ competencies in artificial intelligence education. Computers and Education: Artificial Intelligence, 3, 100098. https://doi.org/10.1016/j.caeai.2022.100098

Sañudo Guerra, L. S. (2022). Del abandono a la permanencia escolar en Secundaria. Profesorado: Revista de Currículum y Formación del Profesorado, 26(1), 213-233. https://doi.org/10.30827/profesorado.v26i1.13535

Soboleva, E. V. (2019). Quest in a digital school: The potential and peculiarities of mobile technology implementation. European Journal of Contemporary Education, 8(3), 613-626. https://doi.org/10.13187/ejced.2019.3.613

Soboleva, E. V., Suvorova, T. N., Grinshkun, A. V. y Bocharov, M. I. (2021). Applying gamification in learning the basics of algorithmization and programming to improve the quality of students’ educational results. European Journal of Contemporary Education, 10(4), 987-1002. https://doi.org/10.13187/EJCED.2021.4.987

Sperling, K., Stenliden, L., Nissen, J. y Heintz, F. (2022). Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor-Network Theory. European Journal of Education, 57(4), 584-600. https://doi.org/10.1111/ejed.12526

Vachkova, S. N., Petryaeva, E. Y., Kupriyanov, R. B. y Suleymanov, R. S. (2021). School in digital age: How big data help to transform the curriculum. Information (Switzerland), 12(1), 1-14. https://doi.org/10.3390/info12010033

Wang, T. y Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K-12 schools incorporating artificial intelligence in education. Computers and Education: Artificial Intelligence, 2, 100031. https://doi.org/10.1016/j.caeai.2021.100031

Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M. y Zagami, J. (2021). Machine learning for human learners: opportunities, issues, tensions and threats. Educational Technology Research and Development, 69(4), 2109-2130. https://doi.org/10.1007/s11423-020-09858-2

Wu, W., Burdina, G. y Gura, A. (2023). Use of Artificial Intelligence in Teacher Training. International Journal of Web-Based Learning and Teaching Technologies, 18(1), 1-15. https://doi.org/10.4018/IJWLTT.331692

Xia, Q., Chiu, T. K. F., Chai, C. S. y Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot. British Journal of Educational Technology, 54(4), 967-986. https://doi.org/10.1111/bjet.13305

Yau, K. W., Chai, C. S., Chiu, T. K. F., Meng, H., King, I. y Yam, Y. (2023). A phenomenographic approach on teacher conceptions of teaching artificial intelligence (AI) in K-12 schools. Education and Information Technologies, 28(1), 1041-1064. https://doi.org/10.1007/s10639-022-11161-x

Zhai, X., He, P. y Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765-1794. https://doi.org/10.1002/tea.21773

Zhang, J. (2023). School wireless network classroom teaching system based on artificial intelligence. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2219563

Zhao, X., Guo, Z. y Liu, S. (2021). Exploring key competencies and professional development of music teachers in primary schools in the era of artificial intelligence. Scientific Programming, 2021, 1-9. https://doi.org/10.1155/2021/5097003

Como Citar

APA

Bustamante Bula, R., e Camacho Bonilla, A. (2024). Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023). Enunciación, 29(1), 62–82. https://doi.org/10.14483/22486798.22039

ACM

[1]
Bustamante Bula, R. e Camacho Bonilla, A. 2024. Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023). Enunciación. 29, 1 (ago. 2024), 62–82. DOI:https://doi.org/10.14483/22486798.22039.

ACS

(1)
Bustamante Bula, R.; Camacho Bonilla, A. Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023). Enunciación 2024, 29, 62-82.

ABNT

BUSTAMANTE BULA, Robin; CAMACHO BONILLA, Aureliano. Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023). Enunciación, [S. l.], v. 29, n. 1, p. 62–82, 2024. DOI: 10.14483/22486798.22039. Disponível em: https://revistas.udistrital.edu.co/index.php/enunc/article/view/22039. Acesso em: 27 set. 2024.

Chicago

Bustamante Bula, Robin, e Aureliano Camacho Bonilla. 2024. “Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)”. Enunciación 29 (1):62-82. https://doi.org/10.14483/22486798.22039.

Harvard

Bustamante Bula, R. e Camacho Bonilla, A. (2024) “Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)”, Enunciación, 29(1), p. 62–82. doi: 10.14483/22486798.22039.

IEEE

[1]
R. Bustamante Bula e A. Camacho Bonilla, “Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)”, Enunciación, vol. 29, nº 1, p. 62–82, ago. 2024.

MLA

Bustamante Bula, Robin, e Aureliano Camacho Bonilla. “Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)”. Enunciación, vol. 29, nº 1, agosto de 2024, p. 62-82, doi:10.14483/22486798.22039.

Turabian

Bustamante Bula, Robin, e Aureliano Camacho Bonilla. “Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023)”. Enunciación 29, no. 1 (agosto 12, 2024): 62–82. Acessado setembro 27, 2024. https://revistas.udistrital.edu.co/index.php/enunc/article/view/22039.

Vancouver

1.
Bustamante Bula R, Camacho Bonilla A. Inteligencia artificial (IA) en las escuelas: una revisión sistemática (2019-2023). Enunciación [Internet]. 12º de agosto de 2024 [citado 27º de setembro de 2024];29(1):62-8. Disponível em: https://revistas.udistrital.edu.co/index.php/enunc/article/view/22039

Baixar Citação

Visitas

88

Dimensions


PlumX


Downloads

Não há dados estatísticos.
Loading...