Agentes inteligentes en la enseñanza de la programación
una revisión sistemática de la literatura
DOI:
https://doi.org/10.47236/2594-7036.2026.v10.2114Palabras clave:
Agentes inteligentes, Aprendizaje de programación, Inteligencia artificial, Revisión sistemática, Tecnologías educativasResumen
La enseñanza de la programación, especialmente en los cursos introductorios, se ha asociado históricamente con altas tasas de deserción y dificultades de aprendizaje, derivadas de la elevada carga cognitiva y de la limitación de soporte individualizado. En este contexto, se ha propuesto el uso de agentes inteligentes como estrategia para apoyar el proceso de aprendizaje. Este estudio tiene como objetivo analizar el uso de estos agentes en la enseñanza de la programación entre 2020 y 2025. La metodología consistió en una revisión sistemática de la literatura, realizada en las bases de datos ACM Digital Library, IEEE Xplore, Scopus y Web of Science, lo que resultó en 573 registros iniciales. Tras la eliminación de duplicados y a aplicación de los criterios de elegibilidad, 81 estudios compusieron el corpus final. Los resultados evidencian una transición de los sistemas tutores basados en reglas a agentes fundamentados en modelos de lenguaje a gran escala, acompañada por la ampliación de sus funciones pedagógicas, con destaque para la generación de pistas, explicación de errores y soporte adaptativo. Se observan ganancias en desempeño y compromiso de los estudiantes, aunque persisten limitaciones relacionadas con la dependencia excesiva y la confiabilidad de las respuestas generadas. Se concluye que el uso de agentes inteligentes no se limita a un avance tecnológico, sino que implica una reconfiguración de las prácticas pedagógicas, marcada por la tensión entre la asistencia automatizada y el desarrollo de la autonomía del estudiante.Descargas
Métricas
Citas
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2026 Beatriz Borges de Oliveira, Rogério Alves dos Santos Antoniassi, Alex Fernando de Araujo
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Permite el intercambio, la adaptación y el uso para cualquier fin, incluso comercial, siempre que se otorgue la debida atribución a los autores y a la Revista Sítio Novo.
Los autores declaran que el trabajo es original y que no ha sido publicado previamente, ni total ni parcialmente, salvo en servidores de preprints reconocidos, siempre que se declare, y que ningún otro manuscrito similar de su autoría se encuentra publicado ni en proceso de evaluación por otra revista, ya sea impresa o electrónica.
Declaran que no han violado ni infringido ningún tipo de derecho de propiedad de terceros, y que todas las citas en el texto son hechos verídicos o están basadas en investigaciones con exactitud científicamente comprobable.
Los autores conservan los derechos de autor de los manuscritos publicados en esta revista, permitiendo el uso irrestricto de su contenido, siempre que se cite adecuadamente la autoría original y la fuente de publicación.
