Project Convergência do Saber: Exploring Artificial Intelligence Fundamentals
Resumo
The integration of AI in education raises ethical and cognitive challenges not yet addressed by the Brazilian curriculum (BNCC). To bridge this gap, Project Convergência do Saber introduces AI fundamentals to public school students, blending Paulo Freire’s pedagogy with unplugged activities and practical tools like Scratch and Teachable Machine. By minimizing programming complexity, the curriculum prioritizes critical AI literacy and conceptual understanding of neural networks and supervised learning. Error is embraced as an epistemic learning tool, while ethical reflection permeates discussions on algorithmic bias and opacity. Ultimately, this framework provides an accessible and scalable model for democratizing AI education.Referências
Association for Computing Machinery (ACM) (2018). ACM Code of Ethics and Professional Conduct. [link].
Bell, T., Alexander, J., Freeman, I., and Grimley, M. (2009). Computer Science Unplugged: School Students Doing Real Computing Without Computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1):20–29.
Brasil. Ministry of Education (2018). National Common Curricular Base. [link].
Brazilian Internet Steering Committee (CGI.br) (2022). TIC Kids Online Brasil.
Fischer, C., et al. (2023). AI Tools, Cognitive Offloading, and Critical Thinking: Implications for Education. Societies, 13(2):45.
Freire, P. and Macedo, D. (1987). Literacy: Reading the Word and the World. Routledge, London.
Freire, P. (1996). Pedagogia da Autonomia. Paz e Terra, São Paulo.
Holmes, W., Bialik, M., and Fadel, C. (2022). Ethics of Artificial Intelligence in Education. Routledge, London.
Kapur, M. (2016). Examining Productive Failure, Productive Success, Unproductive Failure, and Unproductive Success in Learning. Educational Psychologist, 51(2):289–299.
Long, D. and Magerko, B. (2020). Foundations of Artificial Intelligence for K–12 Education. In Proc. Int. Conf. on Computing Education Research, pages 1–10.
Luckin, R., Holmes, W., Griffiths, M., and Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson, London.
Mitchell, T.M. (1997). Machine Learning. McGraw-Hill, New York.
Ojeda-Ramírez, M., et al. (2024). AI Literacy for Multilingual Learners: Combining Unplugged and Block-Based Programming. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED).
Brazilian Computer Society (SBC) (2026). Grand Challenges for Computer Science Research in Brazil. [link]. Last accessed 2026/01/15.
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press, Cambridge.
Touretzky, D., Gardner-McCune, C., Martin, F., and Seehorn, D. (2019). Envisioning AI for K–12: What Should Every Child Know about AI? In Proc. AAAI Conf. on Artificial Intelligence, volume 33, pages 9795–9799.
UNESCO (2023). Guidance for Generative AI in Education and Research. [link].
Masla, J. et al. 2025. Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 28 (Apr. 2025), 29178–29185. DOI: 10.1609/aaai.v39i28.35191.
Bell, T., Alexander, J., Freeman, I., and Grimley, M. (2009). Computer Science Unplugged: School Students Doing Real Computing Without Computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1):20–29.
Brasil. Ministry of Education (2018). National Common Curricular Base. [link].
Brazilian Internet Steering Committee (CGI.br) (2022). TIC Kids Online Brasil.
Fischer, C., et al. (2023). AI Tools, Cognitive Offloading, and Critical Thinking: Implications for Education. Societies, 13(2):45.
Freire, P. and Macedo, D. (1987). Literacy: Reading the Word and the World. Routledge, London.
Freire, P. (1996). Pedagogia da Autonomia. Paz e Terra, São Paulo.
Holmes, W., Bialik, M., and Fadel, C. (2022). Ethics of Artificial Intelligence in Education. Routledge, London.
Kapur, M. (2016). Examining Productive Failure, Productive Success, Unproductive Failure, and Unproductive Success in Learning. Educational Psychologist, 51(2):289–299.
Long, D. and Magerko, B. (2020). Foundations of Artificial Intelligence for K–12 Education. In Proc. Int. Conf. on Computing Education Research, pages 1–10.
Luckin, R., Holmes, W., Griffiths, M., and Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson, London.
Mitchell, T.M. (1997). Machine Learning. McGraw-Hill, New York.
Ojeda-Ramírez, M., et al. (2024). AI Literacy for Multilingual Learners: Combining Unplugged and Block-Based Programming. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED).
Brazilian Computer Society (SBC) (2026). Grand Challenges for Computer Science Research in Brazil. [link]. Last accessed 2026/01/15.
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press, Cambridge.
Touretzky, D., Gardner-McCune, C., Martin, F., and Seehorn, D. (2019). Envisioning AI for K–12: What Should Every Child Know about AI? In Proc. AAAI Conf. on Artificial Intelligence, volume 33, pages 9795–9799.
UNESCO (2023). Guidance for Generative AI in Education and Research. [link].
Masla, J. et al. 2025. Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 28 (Apr. 2025), 29178–29185. DOI: 10.1609/aaai.v39i28.35191.
Publicado
19/07/2026
Como Citar
JORDÃO, Juan Marques; SIMÕES, Eduardo do Valle.
Project Convergência do Saber: Exploring Artificial Intelligence Fundamentals. In: WORKSHOP SOBRE AS IMPLICAÇÕES DA COMPUTAÇÃO NA SOCIEDADE (WICS), 7. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 449-456.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2026.23377.
