Evaluating Large Language Model Quality in Resource-Constrained Environments: An Educational Stakeholders’ Survey on Accuracy, Completeness, and Readability in Brazil
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Isotani, S., Bittencourt, I. I., and Challco, G. C. e. a. (2023). Aied unplugged: Leapfrogging the digital divide to reach the underserved. In International Conference on Artificial Intelligence in Education, pages 772–779. Springer.
Monteiro Santos, M., Barros, A., and Rodrigues, L. e. a. (2024). Near feasibility, distant practicality: Empirical analysis of deploying and using llms on resource-constrained smartphones. In Proceedings of the 13th International Conference on Information & Communication Technologies and Development. ACM.
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Vanlehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3):227–265.
Veloso, T. E., Chalco Challco, G., and Rodrigues, L. e. a. (2023). Its unplugged: Leapfrogging the digital divide for teaching numeracy skills in underserved populations. In Workshop on AI-Augmented Human Tutoring in Math Learning, AIED.
Yan, L., Sha, L., and Zhao, L. e. a. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1):90–112.
Barros, A. P. (2024). Evaluating lightweight llms in educational contexts: Accuracy, completeness and readability. Master’s thesis, Universidade Federal de Alagoas.
Chen, X., Zou, D., and Xie, H. e. a. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1):28–47.
Crompton, H., Jones, M. V., and Burke, D. (2024). Affordances and challenges of artificial intelligence in k-12 education: A systematic review. Journal of Research on Technology in Education, 56(3):248–268.
Gasevic, D., Prinsloo, P., and Chen, B. e. a. (2018). Learning Analytics for the Global South. Foundation for Information Technology Education and Development, Quezon City, Philippines.
Graesser, A. C., VanLehn, K., and Rosé, C. P. e. a. (2004). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4):39–51.
Isotani, S., Bittencourt, I. I., and Challco, G. C. e. a. (2023). Aied unplugged: Leapfrogging the digital divide to reach the underserved. In International Conference on Artificial Intelligence in Education, pages 772–779. Springer.
Monteiro Santos, M., Barros, A., and Rodrigues, L. e. a. (2024). Near feasibility, distant practicality: Empirical analysis of deploying and using llms on resource-constrained smartphones. In Proceedings of the 13th International Conference on Information & Communication Technologies and Development. ACM.
Rodrigues, L., Guerino, G., and Silva, T. E. V. e. a. (2024). Math-aide: A qualitative study of teachers’ perceptions of an its unplugged for underserved regions. International Journal of Artificial Intelligence in Education.
Vanlehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3):227–265.
Veloso, T. E., Chalco Challco, G., and Rodrigues, L. e. a. (2023). Its unplugged: Leapfrogging the digital divide for teaching numeracy skills in underserved populations. In Workshop on AI-Augmented Human Tutoring in Math Learning, AIED.
Yan, L., Sha, L., and Zhao, L. e. a. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1):90–112.
Publicado
24/11/2025
Como Citar
BARROS, Aristoteles Peixoto; DERMEVAL, Diego; RODRIGUES, Luiz.
Evaluating Large Language Model Quality in Resource-Constrained Environments: An Educational Stakeholders’ Survey on Accuracy, Completeness, and Readability in Brazil. In: CONCURSO ALEXANDRE DIRENE (CTD-IE) - TRABALHOS DE CONCLUSÃO DE CURSO - CONGRESSO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (CBIE), 14. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 119-120.
DOI: https://doi.org/10.5753/cbie_estendido.2025.13110.
