Artificial intelligence for SDG 4 of the 2030 agenda: Transforming education to achieve quality, equality, and inclusion
Abstract
The objective of this article is to discuss the possibility of using generative artificial intelligence (AI) to enhance teaching practices and pedagogical support to improve the quality of education provided to young people in elementary and secondary schooling. This issue is linked to the global perspective of a shortage of teachers, which directly affects Sustainable Development Goal 4 (SDG 4), concerning the enhancement of education quality as a target for global sustainable development. From this viewpoint, the potential use of AI may also relate to the improvement of educational quality and the reduction of social inequalities, yielding indirect effects on other sustainable development goals. As a method, we intend to conduct an extensive theoretical discussion addressing the challenges for teacher education and work worldwide, utilizing existing data from databases such as UNESCO, the UN, and the OECD, among others. In addition to data on teachers, we plan to analyze the potential for creating artificial intelligence based on existing ones but trained for the specific contexts of each country’s educational system. The goal is to examine the potential for formatting artificial intelligence to provide pedagogical support for teachers, such as: grading of objective and discursive assessments; individualized intelligent tutoring; analysis of students’ individual pedagogical development; preparation of individual student diagnoses; suggestions of specific pedagogical actions based on curricula and materials used; and all other pedagogical actions that support teachers in their educational journey. This work was funded by CAPES, CNPq, and FAPEMIG.
References
1. UNESCO. Global Education Monitoring Report 2023: Technology in Education-A Tool on Whose Terms? UNESCO; 2023.
2. Boeren E. Understanding Sustainable Development Goal (SDG) 4 on “quality education” from micro, meso and macro perspectives. International Review of Education. 2019; 65(2): 277-294. doi: 10.1007/s11159-019-09772-7
3. Saini M, Sengupta E, Singh M, et al. Sustainable Development Goal for Quality Education (SDG 4): A study on SDG 4 to extract the pattern of association among the indicators of SDG 4 employing a genetic algorithm. Education and Information Technologies. 2022; 28(2): 2031-2069. doi: 10.1007/s10639-022-11265-4
4. Podolsky A, Kini T, Bishop J, Darling-Hammond L. Solving the teacher shortage: How to attract and retain excellent educators. Learning Policy Institute; 2016.
5. André M. Policies and programs to support beginning teachers in Brazil (Portuguese). Cadernos de Pesquisa. 2012; 42(145): 112-129. doi: 10.1590/s0100-15742012000100008
6. Cudowska A. The condition of teachers in a comparative perspective. Studia z Teorii Wychowania. 2023; 14(3(44)): 125-136. doi: 10.5604/01.3001.0053.9200
7. Ladd HF. Teacher Labor Markets in Developed Countries. The Future of Children. 2007; 17(1): 201-217. doi: 10.1353/foc.2007.0006
8. UNESCO. Incheon Declaration and Framework for Action for the Implementation of Sustainable Development Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. UNESCO; 2015.
9. Ertel W. Machine learning and data mining. In: Introduction to Artificial Intelligence, 2nd ed. Springer; 2017.
10. Killian CM, Marttinen R, Howley D, et al. “Knock, Knock… Who’s There?” ChatGPT and Artificial Intelligence-Powered Large Language Models: Reflections on Potential Impacts Within Health and Physical Education Teacher Education. Journal of Teaching in Physical Education. 2023; 42(3): 385-389.
11. Yu S, Lu Y. An Introduction to Artificial Intelligence in Education. Springer; 2021.
12. Farrokhnia M, Banihashem SK, Noroozi O, Wals A. A SWOT Analysis of ChatGPT: Implications for Educational Practice and Research. Innovations in Education and Teaching International; 2023.
13. Santos BL, Arruda EP. Dimensions of Artificial Intelligence in the context of contemporary education (Portuguese). Educação Unisinos. 2019; 23(4). doi: 10.4013/edu.2019.234.08
14. Zhai X. Practices and Theories: How Can Machine Learning Assist in Innovative Assessment Practices in Science Education. Journal of Science Education and Technology. 2021; 30(2): 139-149. doi: 10.1007/s10956-021-09901-8
15. Edwards BI, Cheok AD. Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage. Applied Artificial Intelligence. 2018; 32(4): 345-360. doi: 10.1080/08839514.2018.1464286
16. Celik I, Dindar M, Muukkonen H, et al. The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. 2022; 66(4): 616-630. doi: 10.1007/s11528-022-00715-y
17. Rodríguez-Pose A, Tselios V. Individual Earnings and Educational Externalities in the European Union. Regional Studies. 2012; 46(1): 39-57. doi: 10.1080/00343404.2010.485351
18. Backlund E, Sorlie PD, Johnson NJ. A comparison of the relationships of education and income with mortality: The National Longitudinal Mortality Study. Social Science & Medicine. 1999; 49(10): 1373-1384.
19. Checchi D. Education inequality and income inequality. LSE STICERD Research Paper; 2001.
20. Censo da Educação Escolar—INEP. Available online: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/censo-escolar/resultados/2022 (accessed on 2 January 2024).
21. Gašević D, Siemens G, Sadiq S. Empowering learners for the age of artificial intelligence. Computers and Education: Artificial Intelligence. 2023; 4: 100130. doi: 10.1016/j.caeai.2023.100130
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