Letramento em Feedback e Inteligência Artificial Explicável no Ensino Superior de Computação: um Mapeamento Sistemático
Resumo
Sistemas automatizados e LLMs ampliaram o feedback personalizado em contextos relacionados ao ensino superior de Computação, mas trazem desafios de confiabilidade, explicabilidade e uso crítico. Este mapeamento sistemático examina a articulação entre letramento em feedback, feedback automatizado e XAI. A partir de 586 registros recuperados, 18 artigos foram selecionados para extração e análise. Os resultados indicam articulação parcial, com predomínio de Learning Analytics, abordagens adaptativas e clarificação baseada em LLM, enquanto a XAI formal permanece restrita. As lacunas envolvem critérios, evidências, limites das recomendações, próximos passos e heterogeneidade discente.
Palavras-chave:
Letramento em Feedback, Inteligência Artificial Explicável, Mapeamento Sistemático
Referências
ABET (2023) Criteria for Accrediting Computing Programs, 2023–2024, ABET, Baltimore.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Benjamins, R., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, V. and Herrera, F. (2020) "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI", Information Fusion, v. 58, p. 82–115.
Bender, E. M., Gebru, T., McMillan-Major, A. and Shmitchell, S. (2021) "On the dangers of stochastic parrots: Can language models be too big?", In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), ACM, New York, p. 610–623.
Black, P. and Wiliam, D. (2010) "Inside the black box: Raising standards through classroom assessment", Phi Delta Kappan, v. 92, n. 1, p. 81–90.
Bommasani, R., Hudson, D. A., Adluri, S., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E. et al. (2021) "On the opportunities and risks of foundation models", Stanford Center for Research on Foundation Models, Stanford.
Butler, D. L. and Winne, P. H. (1995) "Feedback and self-regulated learning: A theoretical synthesis", Review of Educational Research, v. 65, n. 3, p. 245–281.
Carless, D. and Boud, D. (2018) "The development of student feedback literacy: Enabling uptake of feedback", Assessment & Evaluation in Higher Education, v. 43, n. 8, p. 1315–1325.
Douce, C., Livingstone, D. and Orwell, J. (2005) "Automatic test-based assessment of programming: A review", Journal on Educational Resources in Computing, v. 5, n. 3, p. 1–13.
Hattie, J. and Timperley, H. (2007) "The power of feedback", Review of Educational Research, v. 77, n. 1, p. 81–112.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A. and Fung, P. (2023) "Survey of hallucination in natural language generation", ACM Computing Surveys, v. 55, n. 12, article 248.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S. et al. (2023) "ChatGPT for good? On opportunities and challenges of large language models for education", Learning and Individual Differences, v. 103, article 102274.
Keuning, H., Jeuring, J. and Heeren, B. (2018) "A systematic literature review of automated feedback generation for programming exercises", ACM Transactions on Computing Education, v. 19, n. 1, article 3.
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S. and Gašević, D. (2022) "Explainable Artificial Intelligence in education", Computers and Education: Artificial Intelligence, v. 3, article 100074.
Kitchenham, B. and Charters, S. (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE-2007-01, Keele University and Durham University Joint Report.
Kitchenham, B., Budgen, D. and Brereton, P. (2016) Evidence-Based Software Engineering and Systematic Literature Reviews, CRC Press, Boca Raton.
Kumar, A. N. et al. (2024) Computer Science Curricula 2023: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science, Association for Computing Machinery, New York. DOI: 10.1145/3664191.
Messer, M., Brown, N. C. C., Kölling, M. and Shi, M. (2024) "Automated grading and feedback tools for programming education: A systematic review", ACM Transactions on Computing Education, v. 24, n. 1, article 10.
Nicol, D. J. and Macfarlane-Dick, D. (2006) "Formative assessment and self-regulated learning: A model and seven principles of good feedback practice", Studies in Higher Education, v. 31, n. 2, p. 199–218.
Sadler, D. R. (1989) "Formative assessment and the design of instructional systems", Instructional Science, v. 18, n. 2, p. 119–144.
Siemens, G. (2013) "Learning analytics: The emergence of a discipline", American Behavioral Scientist, v. 57, n. 10, p. 1380–1400.
Winstone, N. E., Nash, R. A., Rowntree, J. and Parker, M. (2017) "'It'd be useful, but I wouldn't use it': Barriers to university students' feedback seeking and recipience", Studies in Higher Education, v. 42, n. 11, p. 2026–2041.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Benjamins, R., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, V. and Herrera, F. (2020) "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI", Information Fusion, v. 58, p. 82–115.
Bender, E. M., Gebru, T., McMillan-Major, A. and Shmitchell, S. (2021) "On the dangers of stochastic parrots: Can language models be too big?", In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), ACM, New York, p. 610–623.
Black, P. and Wiliam, D. (2010) "Inside the black box: Raising standards through classroom assessment", Phi Delta Kappan, v. 92, n. 1, p. 81–90.
Bommasani, R., Hudson, D. A., Adluri, S., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E. et al. (2021) "On the opportunities and risks of foundation models", Stanford Center for Research on Foundation Models, Stanford.
Butler, D. L. and Winne, P. H. (1995) "Feedback and self-regulated learning: A theoretical synthesis", Review of Educational Research, v. 65, n. 3, p. 245–281.
Carless, D. and Boud, D. (2018) "The development of student feedback literacy: Enabling uptake of feedback", Assessment & Evaluation in Higher Education, v. 43, n. 8, p. 1315–1325.
Douce, C., Livingstone, D. and Orwell, J. (2005) "Automatic test-based assessment of programming: A review", Journal on Educational Resources in Computing, v. 5, n. 3, p. 1–13.
Hattie, J. and Timperley, H. (2007) "The power of feedback", Review of Educational Research, v. 77, n. 1, p. 81–112.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A. and Fung, P. (2023) "Survey of hallucination in natural language generation", ACM Computing Surveys, v. 55, n. 12, article 248.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S. et al. (2023) "ChatGPT for good? On opportunities and challenges of large language models for education", Learning and Individual Differences, v. 103, article 102274.
Keuning, H., Jeuring, J. and Heeren, B. (2018) "A systematic literature review of automated feedback generation for programming exercises", ACM Transactions on Computing Education, v. 19, n. 1, article 3.
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S. and Gašević, D. (2022) "Explainable Artificial Intelligence in education", Computers and Education: Artificial Intelligence, v. 3, article 100074.
Kitchenham, B. and Charters, S. (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE-2007-01, Keele University and Durham University Joint Report.
Kitchenham, B., Budgen, D. and Brereton, P. (2016) Evidence-Based Software Engineering and Systematic Literature Reviews, CRC Press, Boca Raton.
Kumar, A. N. et al. (2024) Computer Science Curricula 2023: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science, Association for Computing Machinery, New York. DOI: 10.1145/3664191.
Messer, M., Brown, N. C. C., Kölling, M. and Shi, M. (2024) "Automated grading and feedback tools for programming education: A systematic review", ACM Transactions on Computing Education, v. 24, n. 1, article 10.
Nicol, D. J. and Macfarlane-Dick, D. (2006) "Formative assessment and self-regulated learning: A model and seven principles of good feedback practice", Studies in Higher Education, v. 31, n. 2, p. 199–218.
Sadler, D. R. (1989) "Formative assessment and the design of instructional systems", Instructional Science, v. 18, n. 2, p. 119–144.
Siemens, G. (2013) "Learning analytics: The emergence of a discipline", American Behavioral Scientist, v. 57, n. 10, p. 1380–1400.
Winstone, N. E., Nash, R. A., Rowntree, J. and Parker, M. (2017) "'It'd be useful, but I wouldn't use it': Barriers to university students' feedback seeking and recipience", Studies in Higher Education, v. 42, n. 11, p. 2026–2041.
Publicado
05/10/2026
Como Citar
FERREIRA, Victor A. dos Santos; TEDESCO, Patrícia Cabral De Azevedo Restelli; ROCHA, Hemilis Joyse Barbosa.
Letramento em Feedback e Inteligência Artificial Explicável no Ensino Superior de Computação: um Mapeamento Sistemático. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2331-2344.
DOI: https://doi.org/10.5753/sbie.2026.28413.
