Teacher-Centered Intelligent Tutoring Systems: Design Considerations from Brazilian, Public School Teachers

  • Luiz Rodrigues UFAL
  • Guilherme Guerino UNESPAR
  • Geiser Chalco Challco UFERSA
  • Thomaz Edson Veloso UFC
  • Lívia Oliveira UFAL
  • Rodolfo Sena da Penha UFC
  • Rafael Ferreira Melo UFRPE
  • Thales Vieira UFAL
  • Marcelo Marinho UFRPE
  • Valmir Macario UFRPE
  • Ig Ibert Bittencourt UFAL / Harvard Graduate School of Education
  • Seiji Isotani UFAL / Harvard Graduate School of Education
  • Diego Dermeval UFAL


While Intelligent Tutoring Systems (ITSs) might enhance learning with personalized instruction, the active involvement of teachers is crucial in achieving the best results. However, teachers have not been adequately involved in the design and usage of ITSs, leading to a research gap in understanding their needs and contexts as well as designing teacher-centered ITSs. To address this gap, we present a qualitative study based on semi-structured interviews with elementary school math teachers in Brazil. Our findings reveal insights connecting teachers’ current practices with their needs, providing design considerations for teacher-centered ITSs. The study emphasizes the importance of curriculum alignment, adaptive scaffolding, comprehensive assessment mechanisms, personalized instruction, and support for teaching challenges. Our findings contribute to designing and developing ITSs tailored for Brazilian public school teachers, considering limited technology access and connectivity issues.


Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26:600–614.

Barbosa, S. D. J., Silva, B. d., Silveira, M. S., Gasparini, I., Darin, T., and Barbosa, G. D. J. (2021). Interação humano-computador e experiência do usuario. Auto publicação.

Blandford, A., Furniss, D., and Makri, S. (2016). Qualitative hci research: Going behind the scenes. Synthesis lectures on human-centered informatics, 9(1):1–115.

Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2):77–101.

Broschert, S., Coughlin, T., Ferraris, M., Flammini, F., Florido, J. G., Gonzalez, A. C., Henz, P., de Kerckhove, D., Rosen, R., Saracco, R., et al. (2019). Symbiotic autonomous systems: white paper iii.

Dermeval, D., Albuquerque, J., Bittencourt, I. I., Vassileva, J., Lemos, W., da Silva, A. P., and Paiva, R. (2018a). Amplifying teachers intelligence in the design of gamified intelligent tutoring systems. In Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part II 19, pages 68–73. Springer.

Dermeval, D. and Bittencourt, I. I. (2020). Co-designing gamified intelligent tutoring systems with teachers. Revista Brasileira De Informática Na Educação, 28:73–91.

Dermeval, D., Paiva, R., Bittencourt, I. I., Vassileva, J., and Borges, D. (2018b). Authoring tools for designing intelligent tutoring systems: a systematic review of the literature. International Journal of Artificial Intelligence in Education, 28:336–384.

du Boulay, B. (2016). Recent meta-reviews and meta–analyses of aied systems. International Journal of Artificial Intelligence in Education, 26(1):536–537.

Fagen, A. P., Crouch, C. H., and Mazur, E. (2002). Peer instruction: Results from a range of classrooms. The physics teacher, 40(4):206–209.

Gašević, D. (2018). Include us all! directions for adoption of learning analytics in the global south. Learning analyfics for the global south, pages 1–22.

Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., and Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153:103897.

INEP (2021). Resumo técnico: Censo escolar da educação básica 2021.

Isotani, S., Bittencourt, I. I., Challco, G. C., Dermeval, D., and Mello, R. F. (2023). Aied unplugged: Leapfrogging the digital divide to reach the underserved. In International Conference on Artificial Intelligence in Education, pages 772–779. Springer.

Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. (2021). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1):142–163.

Nkambou, R., Mizoguchi, R., and Bourdeau, J. (2010). Advances in intelligent tutoring systems, volume 308. Springer Science & Business Media.

Reimers, F. M. (2022). Primary and secondary education during Covid-19: Disruptions to educational opportunity during a pandemic. Springer Nature.

Soofi, A. A. and Ahmed, M. U. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10(3).

Steenbergen-Hu, S. and Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of educational psychology, 106(2):331.

Tenório, K., Chalco Challco, G., Dermeval, D., Lemos, B., Nascimento, P., Santos, R., and Pedro da Silva, A. (2020). Helping teachers assist their students in gamified adaptive educational systems: Towards a gamification analytics tool. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II 21, pages 312–317. Springer.

UNICEF et al. (2018). Raising learning outcomes: the opportunities and challenges of ict for learning. New York: UNICEF.

Veloso, T. E., Chalco Challco, G., Rogrigues, L., Versuti, F. M., Sena da Penha, R., Silva Oliveira, L., Corredato Guerino, G., Cavalcanti de Amorim, L. F., Monteiro Marinho, M. L., Macario, V., Dermeval, D., Bittencourt, I. I., and Isotani, S. (2023). Its unplugged: Leapfrogging the digital divide for teaching numeracy skills in underserved populations. In Proceedings of the Workshop Towards the Future of AI-augmented Human Tutoring in Math Learning 24th International Conference on Artificial Intelligence in Education (AIED). Springer.

Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in software engineering. Springer Science & Business Media.

Xhakaj, F., Aleven, V., and McLaren, B. M. (2017). Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning. In Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings 12, pages 315–329. Springer.

Xia, Q., Chiu, T. K., Zhou, X., Chai, C. S., and Cheng, M. (2022). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, page 100118.
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RODRIGUES, Luiz et al. Teacher-Centered Intelligent Tutoring Systems: Design Considerations from Brazilian, Public School Teachers. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1419-1430. DOI: https://doi.org/10.5753/sbie.2023.235159.