Agentes inteligentes no ensino de programação
uma revisão sistemática da literatura
DOI:
https://doi.org/10.47236/2594-7036.2026.v10.2114Palavras-chave:
Agentes inteligentes, Aprendizagem de programação, Inteligência artificial, Revisão sistemática, Tecnologias EducacionaisResumo
O ensino de programação, especialmente em disciplinas introdutórias, tem sido historicamente associado a elevadas taxas de evasão e dificuldades de aprendizagem, decorrentes da alta carga cognitiva e da limitação de suporte individualizado. Nesse contexto, o uso de agentes inteligentes tem sido proposto como estratégia para apoiar o processo de aprendizagem. Este estudo tem como objetivo analisar o uso desses agentes no ensino de programação no período de 2020 a 2025. A metodologia consistiu em uma revisão sistemática da literatura, conduzida nas bases ACM Digital Library, IEEE Xplore, Scopus e Web of Science, resultando em 573 registros iniciais. Após a remoção de duplicatas e aplicação dos critérios de elegibilidade, 81 estudos compuseram o corpus final. Os resultados evidenciam uma transição de sistemas tutores baseados em regras para agentes fundamentados em modelos de linguagem de grande escala, acompanhada pela ampliação de suas funções pedagógicas, com destaque para a geração de dicas, explicação de erros e suporte adaptativo. Observam-se ganhos em desempenho e engajamento dos estudantes, embora persistam limitações relacionadas à dependência excessiva e à confiabilidade das respostas geradas. Conclui-se que o uso de agentes inteligentes não se restringe a um avanço tecnológico, mas implica uma reconfiguração das práticas pedagógicas, marcada pela tensão entre a assistência automatizada e o desenvolvimento da autonomia discente.Downloads
Métricas
Referências
AHMED, Umair Z. et al. Characterizing the pedagogical benefits of adaptive feedback for compilation errors by novice programmers. In: Proceedings of the 42nd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET '20), Nova York, EUA, p. 139-150, 2020. DOI: 10.1145/3377814.3381703. DOI: https://doi.org/10.1145/3377814.3381703
AKÇAPINAR, Gökhan; SIDAN, Elif. AI chatbots in programming education: guiding success or encouraging plagiarism. Discover Artificial Intelligence, v. 4, n. 1, p. 87, 2024. DOI: 10.1007/s44163-024-00203-7. DOI: https://doi.org/10.1007/s44163-024-00203-7
ALANAZI, Manal et al. Examining the influence of ai on python programming education: an empirical study and analysis of student acceptance through TAM3. Computers, v. 14, n. 10, p. 411, 2025. DOI: 10.3390/computers14100411. DOI: https://doi.org/10.3390/computers14100411
ALENCAR, Rafaella Sampaio de et al. Integrating expert knowledge with automated knowledge component extraction for student modeling. In: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP '25), Nova York, EUA, p. 307-312, 2025. DOI: 10.1145/3699682.3728348. DOI: https://doi.org/10.1145/3699682.3728348
ALSHAIKH, Zeyad; TAMANG, Lasang Jimba; RUS, Vasile. A Socratic tutor for source code comprehension. In: International Conference on Artificial Intelligence In Education, Ifrane, Marrocos, p. 15-19, 2020. DOI: 10.1007/978-3-030-52240-7_3. DOI: https://doi.org/10.1007/978-3-030-52240-7_3
ALSHAIKH, Zeyad; TAMANG, Lasang Jimba; RUS, Vasile. Experiments with auto-generated socratic dialogue for source code understanding. In: International Conference on Computer Supported Education (CSEDU), p. 35-44, 2021. DOI: 10.5220/0010398100350044. DOI: https://doi.org/10.5220/0010398100350044
ALZOUBI, Omar et al. Learning recursion: insights from the ChiQat intelligent tutoring system. In: International Conference on Computer Supported Education (CSEDU), v. 2, p. 336-343, 2020. DOI: 10.5220/0009413903360343. DOI: https://doi.org/10.5220/0009413903360343
ANIKIN, Anton; SYCHEV, Oleg; DENISOV, Mikhail. Ontology reasoning for explanatory feedback generation to teach how algorithms work. In: Novelties in Intelligent Digital Systems: Proceedings of the 1st International Conference (NIDS 2021), Atenas, Grécia, p. 239, 2021. DOI: 10.3233/FAIA210100. DOI: https://doi.org/10.3233/FAIA210100
ARAÚJO, Pedro et al. Automatic personalisation of study guides in flipped classroom: a case study in a distributed systems course. In: 2020 IEEE Frontiers in Education Conference (FIE), Uppsala, Suécia, p. 1-9, 2020. DOI: 10.1109/FIE44824.2020.9274186. DOI: https://doi.org/10.1109/FIE44824.2020.9274186
BAHREHVAR, Majid; MOSHIRPOUR, Mohammad. Agile teaching: Automated student support and feedback generation. In: 2023 IEEE Frontiers in Education Conference (FIE), College Station, TX, EUA, p. 1-9, 2023. DOI: 10.1109/FIE58773.2023.10343212. DOI: https://doi.org/10.1109/FIE58773.2023.10343212
BŁAŻEJOWSKA, Gabriela et al. A study on the role of affective feedback in robot-assisted learning. Sensors, v. 23, n. 3, p. 1181, 2023. DOI: 10.3390/s23031181. DOI: https://doi.org/10.3390/s23031181
BROWN, Neil C. C. et al. Howzat? Appealing to expert judgement for evaluating human and AI next-step hints for novice programmers. ACM Transactions on Computing Education, v. 25, n. 3, art. 31, p. 1-43, 2025. DOI: 10.1145/3737885. DOI: https://doi.org/10.1145/3737885
CÁRDENAS-COBO, Jesennia et al. Applying recommendation system for developing programming competencies in children from a non-weird context. Education and Information Technologies, v. 29, n. 8, p. 9355-9386, 2024. DOI: 10.1007/s10639-023-12156-y. DOI: https://doi.org/10.1007/s10639-023-12156-y
CHANDRASEKARA, Santhush et al. Gamifying coding education for beginners: empowering learners with HTML, CSS and JavaScript. In: 2025 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, p. 1-7, 2025. DOI: 10.1109/SCSE65633.2025.11030977. DOI: https://doi.org/10.1109/SCSE65633.2025.11030977
CHANG, Chih-Kai; CHEN, Pin-Chen. Development and assessment of recommendation system based on behavioral intervention analysis. In: 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), Kanazawa, Japão, p. 208-213, 2022. DOI: 10.1109/IIAIAAI55812.2022.00049. DOI: https://doi.org/10.1109/IIAIAAI55812.2022.00049
CHAPAGAIN, Jeevan; RUS, Vasile. Automated assessment of student self-explanation in code comprehension using pre-trained language models. In: Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, v. 39, n. 28, p. 28996-29003, 2025. DOI: 10.1609/aaai.v39i28.35169. DOI: https://doi.org/10.1609/aaai.v39i28.35169
CHIANG, Yueh-Hui Vanessa; LIN, Yu-Chen; CHEN, Nian-Shing. Developing a course-specific chatbot powered by generative ai for assisting students’ learning in a programming course. In: 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), Nicosia, Chipre do Norte, Chipre, p. 182-184, 2024. DOI: 10.1109/ICALT61570.2024.00059. DOI: https://doi.org/10.1109/ICALT61570.2024.00059
CHRYSAFIADI, Konstantina; VIRVOU, Maria. PerFuSIT: personalized fuzzy logic strategies for intelligent tutoring of programming. Electronics, v. 13, n. 23, 4827, 2024. DOI: 10.3390/electronics13234827. DOI: https://doi.org/10.3390/electronics13234827
DANNATH, Jesper; DERIYEVA, Alina; PAAßEN, Benjamin. Evaluating task-level struggle detection methods in intelligent tutoring systems for programming. In: Proceedings of DELFI, 2024. DOI: 10.18420/delfi2024_07.
EFREMOV, Aleksandr; GHOSH, Ahana; SINGLA, Adish. Zero-Shot learning of hint policy via reinforcement learning and program synthesis. In: International Conference on Educational Data Mining (EDM 2020), p. 388-394, 2020.
EILERMANN, Sebastian et al. KIAAA: An AI assistant for teaching programming in the field of automation. In: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Alemanha, p. 1-7, 2023. DOI: 10.1109/INDIN51400.2023.10218157. DOI: https://doi.org/10.1109/INDIN51400.2023.10218157
FAN, Zhiyu et al. Software engineering educational experience in building an intelligent tutoring system. In: 2025 IEEE/ACM 37th International Conference on Software Engineering Education and Training (CSEE&T), Ottawa, ON, Canadá, p. 75-86, 2025. DOI: 10.1109/CSEET66350.2025.00015. DOI: https://doi.org/10.1109/CSEET66350.2025.00015
FENG, Ty; LIU, Sa; GHOSAL, Dipak. Courseassist: pedagogically appropriate ai tutor for computer science education. In: Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 2 (SIGCSE Virtual 2024), Nova York, EUA, p. 310-311, 2024. DOI: 10.1145/3649409.3691094. DOI: https://doi.org/10.1145/3649409.3691094
FISCHER, Björn et al. Addressing misconceptions in introductory programming: Automated feedback in integrated development environments. In: International Conference on Education Technology and Computers (ICETC '23), Nova York, EUA, p. 1-8, 2023. DOI: 10.1145/3629296.3629297. DOI: https://doi.org/10.1145/3629296.3629297
FRANKFORD, Eduard et al. AI-tutoring in software engineering education. In: Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET '24), Nova York, EUA, p. 309-319, 2024. DOI: 10.1145/3639474.3640061. DOI: https://doi.org/10.1145/3639474.3640061
GALVÃO, Maria Cristiane Barbosa; RICARTE, Ivan Luiz Marques. Revisão sistemática da literatura: conceituação, produção e publicação. Logeion: Filosofia da Informação, Rio de Janeiro, v. 6, n. 1, p. 57-73, 2019. DOI: 10.21728/logeion.2019v6n1.p57-73. DOI: https://doi.org/10.21728/logeion.2019v6n1.p57-73
GUTIERREZ, Andre del Carpio; DENNY, Paul; LUXTON-REILLY, Andrew. Automating personalized parsons problems with customized contexts and concepts. In: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2024), Nova York, EUA, p. 688-694, 2024. DOI: 10.1145/3649217.3653568. DOI: https://doi.org/10.1145/3649217.3653568
HUANG, Yun et al. Supporting skill integration in an intelligent tutoring system for code tracing. Journal of Computer Assisted Learning, v. 39, n. 2, p. 477-500, 2023. DOI: 10.1111/jcal.12757. DOI: https://doi.org/10.1111/jcal.12757
JEBARA, Hadeel; NAWAHDAH, Mamoun; SAWALHA, Hadeel. An empirical evaluation of emotion-aware AI chatbots effect on engagement and programming skills. In: 2025 International Conference on Smart Learning Courses (SCME), Hebron, Palestina, p. 184-189, 2025. DOI: 10.1109/SCME62582.2025.11104881. DOI: https://doi.org/10.1109/SCME62582.2025.11104881
JIANG, Bo et al. E2A2: An encouraging and engaging assignment assistant for learning beyond answers. In: 2025 IEEE World AI IoT Congress (AIIoT), Seattle, WA, EUA, p. 507-513, 2025. DOI: 10.1109/AIIoT65859.2025.11105276. DOI: https://doi.org/10.1109/AIIoT65859.2025.11105276
KATONA, Jozsef; GYONYORU, Klara Ida Katonane. AI-based adaptive programming education for socially disadvantaged students: Bridging the digital divide. TechTrends, v. 69, n. 5, p. 925-942, 2025a. DOI: 10.1007/s11528-025-01088-8. DOI: https://doi.org/10.1007/s11528-025-01088-8
KATONA, Jozsef; GYONYORU, Klara Ida Katonane. Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes. Computers and Education: Artificial Intelligence, v. 8, 100392, 2025b. DOI: 10.1016/j.caeai.2025.100392. DOI: https://doi.org/10.1016/j.caeai.2025.100392
KAZEMITABAAR, Majeed et al. Codeaid: evaluating a classroom deployment of an llm-based programming assistant that balances student and educator needs. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24), Nova York, EUA, art. 650, p. 1-20. DOI: 10.1145/3613904.3642773. DOI: https://doi.org/10.1145/3613904.3642773
KOCH, Nadine Nicole et al. Enhanced code comprehension: individualized learning of code tracing with the feedback buddy. In: Proceedings of DELFI, 2024. DOI: 10.18420/delfi2024_16.
KOIKE, Kento et al. Compogram: development and evaluation of ITS for organizing programming-knowledge by visualizing behavior. In: International Conference on Human-Computer Interaction, p. 151-162, 2020. DOI: 10.1007/978-3-030-60152-2_12. DOI: https://doi.org/10.1007/978-3-030-60152-2_12
KROUSKA, Akrivi et al. Optimizing Player Engagement in an Educational Virtual Game through Fuzzy Logic-based Challenge Adaptation. In: 2023 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Grécia, p. 1-6, 2023. DOI: 10.1109/SEEDA-CECNSM61561.2023.10470784. DOI: https://doi.org/10.1109/SEEDA-CECNSM61561.2023.10470784
KURNIAWAN, Oka et al. Designing for Novice Debuggers: A Pilot Study on an AI-Assisted Debugging Tool. In: Proceedings of the 25th Koli Calling International Conference on Computing Education Research (Koli Calling '25), Nova York, EUA, art. 41, p. 1-7, 2025. DOI: 10.1145/3769994.3769997. DOI: https://doi.org/10.1145/3769994.3769997
LAI, Chien-Hung; LIN, Cheng-Yueh. Analysis of learning behaviors and outcomes for students with different knowledge levels: A case study of intelligent tutoring system for coding and learning (ITS-CAL). Applied Sciences, v. 15, n. 4, p. 1922, 2025. DOI: 10.3390/app15041922. DOI: https://doi.org/10.3390/app15041922
LEE, Michael J. Auto-generated game levels increase novice programmers' engagement. Journal of Computing Sciences in Colleges, v. 36, n. 3, p. 70-79, 2020.
LI, Chong; LEE, Xin; WU, Xiaojin. Provide personalized programming learning for individuals based on large language models. Alexandria Engineering Journal, v. 132, p. 396-406, 2025. DOI: 10.1016/j.aej.2025.10.026. DOI: https://doi.org/10.1016/j.aej.2025.10.026
LI, Wengxi et al. CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Videos. In: International Conference on Artificial Intelligence in Education, Palermo, Itália, p. 11-18, 2025. DOI: 10.1007/978-3-031-98462-4_2. DOI: https://doi.org/10.1007/978-3-031-98462-4_2
LI, Ying et al. Intelligent tutoring for large-scale personalized programming learning based on knowledge graph. In: 2023 IEEE Frontiers in Education Conference (FIE), College Station, TX, EUA, p. 1-5, 2023. DOI: 10.1109/FIE58773.2023.10342641. DOI: https://doi.org/10.1109/FIE58773.2023.10342641
LI, Yiwei et al. Beyond Test Cases: Multi-Agent Collaboration for Detecting Defects in Full-Score Code Implementations. In: International Conference on Software Engineering and Knowledge Engineering (SEKE), Pompeia, Itália, p. 124-129, 2025. DOI: 10.18293/SEKE2025-053. DOI: https://doi.org/10.18293/SEKE2025-053
LINGHU, Hongying; XIANG, Chengguan. Research on Human-Machine Hybrid Enhanced Programming Teaching Model. In: 2024 14th International Conference on Information Technology in Medicine and Education (ITME), Guiyang, China, p. 1161-1166, 2024. DOI: 10.1109/ITME63426.2024.00231. DOI: https://doi.org/10.1109/ITME63426.2024.00231
LOHR, Dominic et al. Adaptive learning systems in programming education: A prototype for enhanced formative feedback. In: Proceedings of DELFI, 2024. DOI: 10.18420/delfi2024_57.
LYU, Wenhan et al. Evaluating the effectiveness of LLMs in introductory computer science education: a semester-long field study. In: Proceedings of the Eleventh ACM Conference on Learning @ Scale, Nova York, EUA, p. 63-74, 2024. DOI: 10.1145/3657604.3662036. DOI: https://doi.org/10.1145/3657604.3662036
MA, Qianou et al. How to teach programming in the AI era? Using LLMs as a teachable agent for debugging. In: International Conference on Artificial Intelligence in Education, Recife, PE, Brasil, p. 265-279, 2024. DOI: 10.1007/978-3-031-64302-6_19. DOI: https://doi.org/10.1007/978-3-031-64302-6_19
MAROUGKAS, Andreas et al. An adaptive virtual reality game for programming education using fuzzy cognitive maps and pedagogical models. Smart Learning Environments, v. 12, n. 1, p. 62, 2025. DOI: 10.1186/s40561-025-00392-3. DOI: https://doi.org/10.1186/s40561-025-00392-3
MASETTY, Sai Veda Prakash; VALLAMULLA, Sreeja. Enhancing novice programming education through AI-Driven mentorship and project-based learning. In: 2025 IEEE Integrated STEM Education Conference (ISEC), Princeton, NJ, EUA, p. 1-7, 2025. DOI: 10.1109/ISEC64801.2025.11147276. DOI: https://doi.org/10.1109/ISEC64801.2025.11147276
MASKELIŪNAS, Rytis et al. FGPE+: The mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model – An empirical evaluation. Computers, v. 12, n. 7, p. 144, 2023. DOI: 10.3390/computers12070144. DOI: https://doi.org/10.3390/computers12070144
MENTARI, Mustika et al. A study of code typing problems as start-up programming practices in Java programming learning assistant system. In: 2024 5th International Conference on Information Technology and Education Technology (ITET), Tottori, Japão, p. 45-50, 2024. DOI: 10.1109/ITET64267.2024.00017. DOI: https://doi.org/10.1109/ITET64267.2024.00017
MUEPU, Daniel M.; WATANOBE, Yutaka; AMIN, Md Faizul Ibne. A comprehensive content-based recommendation system for programming problems through multi-faceted code analysis. IEEE Access, v. 13, p. 93712-93734, 2025. DOI: 10.1109/ACCESS.2025.3574246. DOI: https://doi.org/10.1109/ACCESS.2025.3574246
NUTALAPATI, Harsha; VELMURUGAN, Sachin; TIGLAO, Nestor Michael. Coding Buddy: an adaptive AI-powered platform for personalized learning. In: 2024 International Symposium on Networks, Computers and Communications (ISNCC), Washington DC, DC, EUA, p. 1-6, 2024. DOI: 10.1109/ISNCC62547.2024.10759044. DOI: https://doi.org/10.1109/ISNCC62547.2024.10759044
OLI, Priti et al. Improving code comprehension through scaffolded self-explanations. In: International Conference on Artificial Intelligence In Education, Tókio, Japão, p. 478-483, 2023. DOI: 10.1007/978-3-031-36336-8_74. DOI: https://doi.org/10.1007/978-3-031-36336-8_74
OUYANG, Fan et al. Comparing the effects of instructor manual feedback and ChatGPT intelligent feedback on collaborative programming in China's higher education. IEEE Transactions on Learning Technologies, v. 17, p. 2173-2185, 2024. DOI: 10.1109/TLT.2024.3486749. DOI: https://doi.org/10.1109/TLT.2024.3486749
PAGE, Matthew J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, [S. l.], v. 372, n. 71, p. 1-9, 2021. DOI: 10.1136/bmj.n71. DOI: https://doi.org/10.1136/bmj.n71
PROKUDIN, Artem; SYCHEV, Oleg; DENISOV, Mikhail. Learning problem generator for introductory programming courses. Software Impacts, v. 17, p. 100519, 2023. DOI: 10.1016/j.simpa.2023.100519. DOI: https://doi.org/10.1016/j.simpa.2023.100519
RAHMAN, Md Mostafizer et al. Educational data mining to support programming learning using problem-solving data. IEEE Access, v. 10, p. 26186-26202, 2022. DOI: 10.1109/ACCESS.2022.3157288. DOI: https://doi.org/10.1109/ACCESS.2022.3157288
RISHA, Zak et al. Stepwise help and scaffolding for java code tracing problems with an interactive trace table. In: Proceedings of the 21st Koli Calling International Conference on Computing Education Research (Koli Calling '21), Nova York, EUA, art.27, p. 1-10, 2021. DOI: 10.1145/3488042.3490508. DOI: https://doi.org/10.1145/3488042.3490508
RUMP, Arthur; FEHNKER, Ansgar; MADER, Angelika. Automated assessment of learning objectives in programming assignments. In: International Conference on Intelligent Tutoring Systems, p. 299-309, 2021. DOI: 10.1007/978-3-030-80421-3_33. DOI: https://doi.org/10.1007/978-3-030-80421-3_33
SANTOS, Otávio Lube dos; CURY, Davidson. Challenging the confirmation bias: Using ChatGPT as a virtual peer for peer instruction in computer programming education. In: 2023 IEEE Frontiers in Education Conference (FIE), College Station, TX, EUA, p. 1-7, 2023. DOI: 10.1109/FIE58773.2023.10343247. DOI: https://doi.org/10.1109/FIE58773.2023.10343247
SAVITHA, N. J. et al. Guild Based Online Coding Platform with Gamification Features. In: 2025 Global Conference in Emerging Technology (GINOTECH), Pune, Índia, p. 1-5, 2025. DOI: 10.1109/GINOTECH63460.2025.11077053. DOI: https://doi.org/10.1109/GINOTECH63460.2025.11077053
SCHEZ-SOBRINO, Santiago et al. An intelligent tutoring system to facilitate the learning of programming through the usage of dynamic graphic visualizations. Applied Sciences, v. 10, n. 4, p. 1518, 2020. DOI: 10.3390/app10041518. DOI: https://doi.org/10.3390/app10041518
SHUM, Lok Cheung; ROSUNALLY, Yasmine; MUNIR, Kamran. Transforming Programming Education: The Effectiveness of Motivational Scenario-Based Design in Serious Games. IEEE Transactions on Education, v. 68, n. 4, p. 394-406, aug. 2025. DOI: 10.1109/TE.2025.3584187. DOI: https://doi.org/10.1109/TE.2025.3584187
SILVA, Isac Neto da; SOUZA, Júlio Cesar de. Inteligência Artificial e Psicologia Cognitiva: contribuições no desenvolvimento de tecnologias educacionais adaptativas na educação básica. Revista Sítio Novo, Palmas, v. 9, p. e1781, 2025. DOI: 10.47236/2594-7036.2025.v9.1781. DOI: https://doi.org/10.47236/2594-7036.2025.v9.1781
SOUSA, Ricardo Ferreira de; LAGE, Eric Fellippe Ribeiro. Educação e pandemia da covid-19: a importância das tecnologias de comunicação e informação neste cenário. Revista Sítio Novo, Palmas, v. 8, n. 4, p. 59-76, 2025. DOI: 10.47236/2594-7036.2024.v8.i4.59-76p. DOI: https://doi.org/10.47236/2594-7036.2024.v8.i4.59-76p
SYAIFUDIN, Yan Watequlis et al. Blending Android programming learning assistance system into online android programming course. In: 2021 9th International Conference on Information and Education Technology (ICIET), Okayama, Japão, p. 26-33, 2021. DOI: 10.1109/ICIET51873.2021.9419650. DOI: https://doi.org/10.1109/ICIET51873.2021.9419650
SYCHEV, Oleg; PENSKOY, Nikita; PROKUDIN, Artem. Generating expression evaluation learning problems from existing program code. In: 2022 IEEE International Conference on Advanced Learning Technologies (ICALT), Bucareste, Romênia, p. 183-187, 2022. DOI: 10.1109/ICALT55010.2022.00061. DOI: https://doi.org/10.1109/ICALT55010.2022.00061
SYCHEV, Oleg; PROKUDIN, Artem; DENISOV, Mikhail. Generation of code tracing problems from open-source code. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023), Nova York, EUA, p. 875-881, 2023. DOI: 10.1145/3545945.3569774. DOI: https://doi.org/10.1145/3545945.3569774
SYCHEV, Oleg; SHASHKOV, Dmitry. Mass Generation of Programming Learning Problems from Public Code Repositories. Big Data and Cognitive Computing, v. 9, n. 3, p. 57, 2025. DOI: 10.3390/bdcc9030057. DOI: https://doi.org/10.3390/bdcc9030057
TAYLOR, Andrew et al. dcc--help: Transforming the Role of the Compiler by Generating Context-Aware Error Explanations with Large Language Models. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024), Nova York, EUA, p. 1314-1320, 2024. DOI: 10.1145/3626252.3630822. DOI: https://doi.org/10.1145/3626252.3630822
THO, Pham-Duc. PERS: A Personalized Recommender System for Student-Generated Questions in Programming Courses. In: 32nd International Conference on Computers in Education (ICCE 2024): Conference Proceedings V. 1, Quezon City, Filipinas, 20424. DOI: 10.58459/icce.2024.4913. DOI: https://doi.org/10.58459/icce.2024.4913
TROUSSAS, Christos et al. Evaluating ChatGPT-driven Automated Test Generation for Personalized Programming Education. In: 2024 2nd International Conference on Foundation and Large Language Models (FLLM), Dubai, Emirados Árabes Unidos, p. 194-200, 2024. DOI: 10.1109/FLLM63129.2024.10852510. DOI: https://doi.org/10.1109/FLLM63129.2024.10852510
TROUSSAS, Christos et al. Fuzzy memory networks and contextual schemas: Enhancing ChatGPT responses in a personalized educational system. Computers, v. 14, p. 89, 2025. DOI: 10.3390/computers14030089. DOI: https://doi.org/10.3390/computers14030089
VEMULA, Srikanth. Enriching python programming education with generative ai: Leveraging large language models for personalized support and interactive learning. In: 2024 IEEE Frontiers in Education Conference (FIE), Washington, DC, EUA, p. 1-8, 2024. DOI: 10.1109/FIE61694.2024.10893561. DOI: https://doi.org/10.1109/FIE61694.2024.10893561
VESIN, Boban et al. Adaptive assessment and content recommendation in online programming courses: On the use of Elo-rating. ACM Transactions on Computing Education, v. 22, n. 3, p. 1-27, 2022. DOI: 10.1145/3511886. DOI: https://doi.org/10.1145/3511886
VIEIRA, Angela Vitoria Mota; MENEZES, Ramayana Assunção; NETTO, Jose Francisco Magalhães. A Proposal of Adaptive Learning System for Object-Oriented Programming Education. In: 2024 IEEE Frontiers in Education Conference (FIE), Washington, DC, EUA, p. 1-7, 2024. DOI: 10.1109/FIE61694.2024.10893496. DOI: https://doi.org/10.1109/FIE61694.2024.10893496
WANG, Feng Hsu. Exploring the Impact of Transparent Generative AI on Learning Outcomes Through a Human-Machine Trust Model. In: 2025 IEEE International Conference on Advanced Learning Technologies (ICALT), Changhua, Taiwan, p. 229-233, 2025. DOI: 10.1109/ICALT64023.2025.00072. DOI: https://doi.org/10.1109/ICALT64023.2025.00072
WANG, Haoming et al. Impact of AI-agent-supported collaborative learning on the learning outcomes of university programming courses. Education and Information Technologies, v. 30, p. 17717-17749, 2025. DOI: 10.1007/s10639-025-13487-8. DOI: https://doi.org/10.1007/s10639-025-13487-8
WIGGINS, Joseph B. et al. Exploring novice programmers' hint requests in an intelligent block-based coding environment. In: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE 2021), Nova York, EUA, p. 52-58, 2021. DOI: 10.1145/3408877.3432538. DOI: https://doi.org/10.1145/3408877.3432538
WIJAYA, Owen Christian; PURWARIANTI, Ayu. An interactive question-answering system using large language model and retrieval-augmented generation in an intelligent tutoring system on the programming domain. In: 2024 11th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA), Cingapura, p. 1-6, 2024. DOI: 10.1109/ICAICTA63815.2024.10763263. DOI: https://doi.org/10.1109/ICAICTA63815.2024.10763263
WU, Zhiqiang; WAN, Shuhui. A knowledge-driven approach to AI-based personalized test paper creation in programming education. International Journal of Knowledge Management (IJKM), v. 21, n. 1, 2025. DOI: 10.4018/IJKM.369825. DOI: https://doi.org/10.4018/IJKM.369825
YANG, Albert C. M. et al. Enhancing python learning with PyTutor: efficacy of a ChatGPT-Based intelligent tutoring system in programming education. Computers and Education: Artificial Intelligence, v. 7, p. 100309, 2024. DOI: 10.1016/j.caeai.2024.100309. DOI: https://doi.org/10.1016/j.caeai.2024.100309
ZABALA, Eric; NARMAN, Husnu S. Development and evaluation of an AI-enhanced python programming education system. In: 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Yorktown Heights, NY, EUA, p. 787-792, 2024. DOI: 10.1109/UEMCON62879.2024.10754661. DOI: https://doi.org/10.1109/UEMCON62879.2024.10754661
ZHAO, Yanni et al. Exploration of Computer Programming Teaching Reform Based on Large Language Models. In: 2025 11th International Conference on Computing and Artificial Intelligence (ICCAI), Kyoto, Japão, p. 359-363, 2025. DOI: 10.1109/ICCAI66501.2025.00062. DOI: https://doi.org/10.1109/ICCAI66501.2025.00062
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2026 Beatriz Borges de Oliveira, Rogério Alves dos Santos Antoniassi, Alex Fernando de Araujo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Permite o compartilhamento, adaptação e uso para quaisquer fins, inclusive comerciais, desde que feita a devida atribuição aos autores e à Revista Sítio Novo.
Os autores declaram que o trabalho é original, não foi previamente publicado em parte ou no todo, exceto em servidores de preprints reconhecidos, desde que declarado, e que nenhum outro manuscrito similar de sua autoria está publicado ou em processo de avaliação por outro periódico, seja impresso ou eletrônico.
Declaram que não violaram nem infringiram nenhum tipo de direito de propriedade de outras pessoas, e que todas as citações no texto são fatos verdadeiros ou baseados em pesquisas de exatidão cientificamente considerável.
Os autores mantêm os direitos autorais dos manuscritos publicados nesta revista, permitindo o uso irrestrito de seu conteúdo, desde que citada a autoria original e a fonte da publicação.
