AI-Based Code Assessment in Programming Education: A Systematic Review of Feedback, Reliability, and Ethics
Resumo
Artificial intelligence (AI) increasingly supports code assessment in programming education, yet evidence of its educational impact, reliability, and responsible use remains fragmented. Following PRISMA guidelines, this systematic review synthesizes 106 empirical studies published from 2021 to 2025. The corpus is concentrated in higher education and introductory programming, with large language models as the most frequent approach, often in hybrid pipelines. Although feedback is common and increasingly explanatory or procedural, only six studies combine experimental or quasi-experimental designs with objective learning indicators. Reliability is stronger for bounded judgements, such as error detection and binary correctness, than for semantic, rubric-based, or code-quality assessment. Reported concerns focus mainly on robustness, generalization, privacy, and compliance, while transparency and governance remain underexplored. Overall, the field is technically promising but pedagogically and methodologically uneven.
Palavras-chave:
AI-Based Code Assessment, Programming Education, Systematic Review
Referências
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Alrabah, A. and Alawini, A. (2025). Codelens: A generative ai framework for automated feedback on sql assignments. In Proceedings of the 4th International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd '25, page 29–34, New York, NY, USA. Association for Computing Machinery.
Berrezueta-Guzman, J. and Krusche, S. (2023). Recommendations to create programming exercises to overcome chatgpt. In 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T), pages 147–151.
Calatayud, V. G., Espinosa, M. P. P., and Vila, R. R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12):5467.
Castilho, G. U., Herrera, V. A., and Rodriguez, C. L. (2025). Explorando o potencial da ia generativa em ambientes virtuais de aprendizagem: Uma revisão sistemática. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, SBIE 2025, pages 113–126. Sociedade Brasileira de Computação.
Chen, E., Huang, R., Chen, H.-S., Tseng, Y.-H., and Li, L.-Y. (2023). Gptutor: A chatgpt-powered programming tool for code explanation. In Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., and Santos, O., editors, Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, volume 1831 of Communications in Computer and Information Science, pages 632–647. Springer, Cham.
Cámara, J., Troya, J., Montes-Torres, J., and Jaime, F. J. (2024). Generative ai in the software modeling classroom: An experience report with chatgpt and unified modeling language. IEEE Software, 41(06):73–81.
Denny, P., MacNeil, S., Savelka, J., Porter, L., and Luxton-Reilly, A. (2024a). Desirable characteristics for ai teaching assistants in programming education. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, page 408–414, New York, NY, USA. Association for Computing Machinery.
Denny, P., Smith IV, D. H., Fowler, M., Prather, J., Becker, B. A., and Leinonen, J. (2024b). Explaining code with a purpose: An integrated approach for developing code comprehension and prompting skills. In Proceedings of the 2024 Conference on Innovation and Technology in Computer Science Education (ITiCSE).
Dong, D. and Liang, Y. (2024). Grading programming assignments by summarization. In Proceedings of the ACM Turing Award Celebration Conference - China 2024, ACM-TURC '24, page 53–58, New York, NY, USA. Association for Computing Machinery.
Douce, C., Livingstone, D., and Orwell, J. (2005). Automatic test-based assessment of programming: a review. Journal on Educational Resources in Computing (JERIC), 5(3):Article 4–es.
Frankford, E., Sauerwein, C., Bassner, P., Krusche, S., and Breu, R. (2024). Ai-tutoring in software engineering education. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, ICSE-SEET '24, page 309–319, New York, NY, USA. Association for Computing Machinery.
Gomes, P. C. R. and Hübner, J. F. (2025). Avaliação da aprendizagem em programação com IA generativa na educação profissional e tecnológica. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1529–1539, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Hattie, J. and Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1):81–112.
Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanpää, L., and Sorva, J. (2023). Exploring the responses of large language models to beginner programmers' help requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research (ICER '23), volume 1, pages 93–105. Association for Computing Machinery.
Hou, X., Wu, Z., Wang, X., and Ericson, B. J. (2024). Codetailor: Llm-powered personalized parsons puzzles for engaging support while learning programming. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24), pages 51–62. Association for Computing Machinery.
Kaleem, M., Hassan, M. A., Anwar, Z., and Ehsan, N. (2024). A machine learning-based adaptive feedback system to enhance programming skill using computational thinking. In 2024 IEEE Global Engineering Education Conference (EDUCON). IEEE.
Keuning, H., Jeuring, J., and Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education, 19(1):1–43.
Kimmel, B., Geisert, A. L., Yaro, L., Gipson, B., Hotchkiss, R. T., Osae-Asante, S. K., Vaught, H., Wininger, G., and Yamaguchi, C. (2024). Enhancing programming error messages in real time with generative ai. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA '24, New York, NY, USA. Association for Computing Machinery.
Kitchenham, B., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman and Hall/CRC.
Kitchenham, B., Madeyski, L., and Budgen, D. (2023). Segress: Software engineering guidelines for reporting secondary studies. IEEE Transactions on Software Engineering, 49(3):1273–1298.
Lagakis, P. and Demetriadis, S. (2024). Evaai: A multi-agent framework leveraging large language models for enhanced automated grading. In Sifaleras, A. and Lin, F., editors, Generative Intelligence and Intelligent Tutoring Systems. ITS 2024. Lecture Notes in Computer Science, volume 14798, page 378–385. Springer, Cham.
Lampou, R. (2023). The integration of artificial intelligence in education: Opportunities and challenges. Review of Artificial Intelligence in Education, 4(00):e015.
Li, J., Zhao, Y., Li, Y., Li, G., and Jin, Z. (2024). Acecoder: An effective prompting technique specialized in code generation. ACM Trans. Softw. Eng. Methodol., 33(8).
Messer, M., Brown, N. C. C., Kölling, M., and Shi, M. (2023). Machine learning based automated grading and feedback tools for programming: A meta analysis. In Proceedings of ITiCSE 2023, pages 491–497. ACM.
Miao, F. and Holmes, W. (2023). Guidance for generative AI in education and research. Technical report, UNESCO, Paris, France.
Naik, A., Yin, J. R., Kamath, A., Ma, Q., Wu, S. T., Murray, C., Bogart, C., Sakr, M., and Rose, C. P. (2024). Generating situated reflection triggers about alternative solution paths: A case study of generative ai for computer-supported collaborative learning. In Artificial Intelligence in Education: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I, page 46–59. Springer-Verlag, Berlin, Heidelberg.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Paiva, J. C., Leal, J. P., and Figueira, A. (2022). Automated assessment in computer science education: A state-of-the-art review. ACM Trans. Comput. Educ., 22(3).
Pargman, T. C., McGrath, C., and Milrad, M. (2024). Towards responsible ai in education: Challenges and implications for research and practice. Computers and Education: Artificial Intelligence, page 100345.
Pathak, A., Gandhi, R., Uttam, V., Ramamoorthy, A., Ghosh, P., Jindal, A. R., Verma, S., Mittal, A., Ased, A., Khatri, C., Nakka, Y., Devansh, Challa, J. S., and Kumar, D. (2025). Rubric is all you need: Improving llm-based code evaluation with question-specific rubrics. In Proceedings of the 2025 ACM Conference on International Computing Education Research V.1, ICER '25, page 181–195, New York, NY, USA. Association for Computing Machinery.
Porayska-Pomsta, K., Holmes, W., and Nemorin, S. (2024). The ethics of ai in education. In du Boulay, B., Mitrovic, A., and Yacef, K., editors, Handbook of Artificial Intelligence in Education, pages 571–604. Edward Elgar.
Qi, L., Zamfirescu-Pereira, J., Kim, T., Hartmann, B., DeNero, J., and Norouzi, N. (2025). A knowledge-component-based methodology for evaluating ai assistants. In Proceedings of the ACM Global on Computing Education Conference 2025 Vol 1, CompEd 2025, page 78–84, New York, NY, USA. Association for Computing Machinery.
Rico-Juan, J. R., Sánchez-Cartagena, V. M., Valero-Mas, J. J., and Gallego, A. J. (2023). Identifying student profiles within online judge systems using explainable artificial intelligence. IEEE Transactions on Learning Technologies, 16(6):955–969.
Roldán-Álvarez, D. and Mesa, F. J. (2024). Intelligent deep-learning tutoring system to assist instructors in programming courses. IEEE Transactions on Education, 67(1):153–161.
Sanchez-Anguix, V., Alberola, J. M., Del Val, E., Palomares, A., and Teruel, M. D. (2023). Comparing computational algorithms for team formation in the classroom: a classroom experience. Applied Intelligence, 53(20):23883–23904.
Silva, F. G. and Aranha, E. H. S. (2025). Feedback formativo automatizado com LLMs: Desenvolvimento e análise de um sistema para aprendizagem progressiva em programação. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1159–1173, Curitiba, PR, Brasil.
Silva, T. L. d., Vidotto, K. N. S., Tarouco, L. M. R., and Silva, P. F. d. (2024). Potencialidades do uso de inteligência artificial generativa como apoio ao ensino de programação. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1942–1956, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Sokač, M., Fabijanić, M., Mekterović, I., and Mršić, L. (2025). Automated grading through contrastive learning: A gradient analysis and feature ablation approach. Machine Learning and Knowledge Extraction, 7(2):41.
Taeb, M. and Chi, H. (2021). A personalized learning framework for software vulnerability detection and education. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), pages 119–126.
Troussas, C., Krouska, A., and Virvou, M. (2023). A multilayer inference engine for individualized tutoring model: adapting learning material and its granularity. Neural Comput & Applic, 35:61–75.
Vadaparty, A., Zingaro, D., Smith IV, D. H., Padala, M., Alvarado, C., Gorson Benario, J., and Porter, L. (2024). Cs1-llm: Integrating llms into cs1 instruction. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, pages 297–303, New York, NY, USA. Association for Computing Machinery.
Wieser, M., Röder, F., Helmer, S., and Weitz, J. (2023). Investigating the role of chatgpt in supporting text-based programming education for students and teachers. Computers and Education: Artificial Intelligence, 5:100232.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in Software Engineering. Springer.
Yan, L., Sha, L., Zhao, L., Li, Y., Maldonado, R. M., Chen, G., Li, X., Jin, Y., and Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1):90–112.
Ala-Mutka, K. M. (2005). A survey of automated assessment approaches for programming assignments. Computer Science Education, 15(2):83–102.
Alrabah, A. and Alawini, A. (2025). Codelens: A generative ai framework for automated feedback on sql assignments. In Proceedings of the 4th International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd '25, page 29–34, New York, NY, USA. Association for Computing Machinery.
Berrezueta-Guzman, J. and Krusche, S. (2023). Recommendations to create programming exercises to overcome chatgpt. In 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T), pages 147–151.
Calatayud, V. G., Espinosa, M. P. P., and Vila, R. R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12):5467.
Castilho, G. U., Herrera, V. A., and Rodriguez, C. L. (2025). Explorando o potencial da ia generativa em ambientes virtuais de aprendizagem: Uma revisão sistemática. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, SBIE 2025, pages 113–126. Sociedade Brasileira de Computação.
Chen, E., Huang, R., Chen, H.-S., Tseng, Y.-H., and Li, L.-Y. (2023). Gptutor: A chatgpt-powered programming tool for code explanation. In Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., and Santos, O., editors, Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, volume 1831 of Communications in Computer and Information Science, pages 632–647. Springer, Cham.
Cámara, J., Troya, J., Montes-Torres, J., and Jaime, F. J. (2024). Generative ai in the software modeling classroom: An experience report with chatgpt and unified modeling language. IEEE Software, 41(06):73–81.
Denny, P., MacNeil, S., Savelka, J., Porter, L., and Luxton-Reilly, A. (2024a). Desirable characteristics for ai teaching assistants in programming education. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, page 408–414, New York, NY, USA. Association for Computing Machinery.
Denny, P., Smith IV, D. H., Fowler, M., Prather, J., Becker, B. A., and Leinonen, J. (2024b). Explaining code with a purpose: An integrated approach for developing code comprehension and prompting skills. In Proceedings of the 2024 Conference on Innovation and Technology in Computer Science Education (ITiCSE).
Dong, D. and Liang, Y. (2024). Grading programming assignments by summarization. In Proceedings of the ACM Turing Award Celebration Conference - China 2024, ACM-TURC '24, page 53–58, New York, NY, USA. Association for Computing Machinery.
Douce, C., Livingstone, D., and Orwell, J. (2005). Automatic test-based assessment of programming: a review. Journal on Educational Resources in Computing (JERIC), 5(3):Article 4–es.
Frankford, E., Sauerwein, C., Bassner, P., Krusche, S., and Breu, R. (2024). Ai-tutoring in software engineering education. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, ICSE-SEET '24, page 309–319, New York, NY, USA. Association for Computing Machinery.
Gomes, P. C. R. and Hübner, J. F. (2025). Avaliação da aprendizagem em programação com IA generativa na educação profissional e tecnológica. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1529–1539, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Hattie, J. and Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1):81–112.
Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanpää, L., and Sorva, J. (2023). Exploring the responses of large language models to beginner programmers' help requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research (ICER '23), volume 1, pages 93–105. Association for Computing Machinery.
Hou, X., Wu, Z., Wang, X., and Ericson, B. J. (2024). Codetailor: Llm-powered personalized parsons puzzles for engaging support while learning programming. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24), pages 51–62. Association for Computing Machinery.
Kaleem, M., Hassan, M. A., Anwar, Z., and Ehsan, N. (2024). A machine learning-based adaptive feedback system to enhance programming skill using computational thinking. In 2024 IEEE Global Engineering Education Conference (EDUCON). IEEE.
Keuning, H., Jeuring, J., and Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education, 19(1):1–43.
Kimmel, B., Geisert, A. L., Yaro, L., Gipson, B., Hotchkiss, R. T., Osae-Asante, S. K., Vaught, H., Wininger, G., and Yamaguchi, C. (2024). Enhancing programming error messages in real time with generative ai. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA '24, New York, NY, USA. Association for Computing Machinery.
Kitchenham, B., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman and Hall/CRC.
Kitchenham, B., Madeyski, L., and Budgen, D. (2023). Segress: Software engineering guidelines for reporting secondary studies. IEEE Transactions on Software Engineering, 49(3):1273–1298.
Lagakis, P. and Demetriadis, S. (2024). Evaai: A multi-agent framework leveraging large language models for enhanced automated grading. In Sifaleras, A. and Lin, F., editors, Generative Intelligence and Intelligent Tutoring Systems. ITS 2024. Lecture Notes in Computer Science, volume 14798, page 378–385. Springer, Cham.
Lampou, R. (2023). The integration of artificial intelligence in education: Opportunities and challenges. Review of Artificial Intelligence in Education, 4(00):e015.
Li, J., Zhao, Y., Li, Y., Li, G., and Jin, Z. (2024). Acecoder: An effective prompting technique specialized in code generation. ACM Trans. Softw. Eng. Methodol., 33(8).
Messer, M., Brown, N. C. C., Kölling, M., and Shi, M. (2023). Machine learning based automated grading and feedback tools for programming: A meta analysis. In Proceedings of ITiCSE 2023, pages 491–497. ACM.
Miao, F. and Holmes, W. (2023). Guidance for generative AI in education and research. Technical report, UNESCO, Paris, France.
Naik, A., Yin, J. R., Kamath, A., Ma, Q., Wu, S. T., Murray, C., Bogart, C., Sakr, M., and Rose, C. P. (2024). Generating situated reflection triggers about alternative solution paths: A case study of generative ai for computer-supported collaborative learning. In Artificial Intelligence in Education: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I, page 46–59. Springer-Verlag, Berlin, Heidelberg.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Paiva, J. C., Leal, J. P., and Figueira, A. (2022). Automated assessment in computer science education: A state-of-the-art review. ACM Trans. Comput. Educ., 22(3).
Pargman, T. C., McGrath, C., and Milrad, M. (2024). Towards responsible ai in education: Challenges and implications for research and practice. Computers and Education: Artificial Intelligence, page 100345.
Pathak, A., Gandhi, R., Uttam, V., Ramamoorthy, A., Ghosh, P., Jindal, A. R., Verma, S., Mittal, A., Ased, A., Khatri, C., Nakka, Y., Devansh, Challa, J. S., and Kumar, D. (2025). Rubric is all you need: Improving llm-based code evaluation with question-specific rubrics. In Proceedings of the 2025 ACM Conference on International Computing Education Research V.1, ICER '25, page 181–195, New York, NY, USA. Association for Computing Machinery.
Porayska-Pomsta, K., Holmes, W., and Nemorin, S. (2024). The ethics of ai in education. In du Boulay, B., Mitrovic, A., and Yacef, K., editors, Handbook of Artificial Intelligence in Education, pages 571–604. Edward Elgar.
Qi, L., Zamfirescu-Pereira, J., Kim, T., Hartmann, B., DeNero, J., and Norouzi, N. (2025). A knowledge-component-based methodology for evaluating ai assistants. In Proceedings of the ACM Global on Computing Education Conference 2025 Vol 1, CompEd 2025, page 78–84, New York, NY, USA. Association for Computing Machinery.
Rico-Juan, J. R., Sánchez-Cartagena, V. M., Valero-Mas, J. J., and Gallego, A. J. (2023). Identifying student profiles within online judge systems using explainable artificial intelligence. IEEE Transactions on Learning Technologies, 16(6):955–969.
Roldán-Álvarez, D. and Mesa, F. J. (2024). Intelligent deep-learning tutoring system to assist instructors in programming courses. IEEE Transactions on Education, 67(1):153–161.
Sanchez-Anguix, V., Alberola, J. M., Del Val, E., Palomares, A., and Teruel, M. D. (2023). Comparing computational algorithms for team formation in the classroom: a classroom experience. Applied Intelligence, 53(20):23883–23904.
Silva, F. G. and Aranha, E. H. S. (2025). Feedback formativo automatizado com LLMs: Desenvolvimento e análise de um sistema para aprendizagem progressiva em programação. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1159–1173, Curitiba, PR, Brasil.
Silva, T. L. d., Vidotto, K. N. S., Tarouco, L. M. R., and Silva, P. F. d. (2024). Potencialidades do uso de inteligência artificial generativa como apoio ao ensino de programação. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1942–1956, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Sokač, M., Fabijanić, M., Mekterović, I., and Mršić, L. (2025). Automated grading through contrastive learning: A gradient analysis and feature ablation approach. Machine Learning and Knowledge Extraction, 7(2):41.
Taeb, M. and Chi, H. (2021). A personalized learning framework for software vulnerability detection and education. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), pages 119–126.
Troussas, C., Krouska, A., and Virvou, M. (2023). A multilayer inference engine for individualized tutoring model: adapting learning material and its granularity. Neural Comput & Applic, 35:61–75.
Vadaparty, A., Zingaro, D., Smith IV, D. H., Padala, M., Alvarado, C., Gorson Benario, J., and Porter, L. (2024). Cs1-llm: Integrating llms into cs1 instruction. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, pages 297–303, New York, NY, USA. Association for Computing Machinery.
Wieser, M., Röder, F., Helmer, S., and Weitz, J. (2023). Investigating the role of chatgpt in supporting text-based programming education for students and teachers. Computers and Education: Artificial Intelligence, 5:100232.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in Software Engineering. Springer.
Yan, L., Sha, L., Zhao, L., Li, Y., Maldonado, R. M., Chen, G., Li, X., Jin, Y., and Gašević, D. (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
05/10/2026
Como Citar
SILVA, Francisco Genivan; DE LIMA, Rommel Wladimir; DA SILVA, Thiago Reis; ARAUJO, Mike Christian de Sousa; ARANHA, Eduardo Henrique da Silva.
AI-Based Code Assessment in Programming Education: A Systematic Review of Feedback, Reliability, and Ethics. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1516-1532.
DOI: https://doi.org/10.5753/sbie.2026.27915.
