INGENIERÍA DE SOFTWARE GENERATIVA/DETERMINISTA
PEDAGOGÍA CON SISTEMAS MULTIAGENTE LOCALES GESTIONANDO CARGAS COGNITIVAS/COMPUTACIONALES
Visualizações: 7DOI:
https://doi.org/10.56579/rei.v8i3.3644Palabras clave:
Educación en Ingeniería de Software, Sistemas Multiagente Locales, Carga Cognitiva, Carga Computacional, Estrategias PedagógicasResumen
La integración de sistemas inteligentes plantea importantes desafíos para la educación en desarrollo de software. Las políticas actuales suelen ignorar la complejidad de las tecnologías emergentes, como el ajuste fino de modelos con bajo consumo de memoria y la orquestación de múltiples modelos inteligentes locales, además de las significativas limitaciones de recursos computacionales. Este estudio propone un modelo pedagógico centrado en la interdependencia crítica entre el esfuerzo mental del estudiante y las demandas de procesamiento de la máquina. Los hallazgos empíricos indican que optimizar la demanda de procesamiento (por ejemplo, mediante cuantización agresiva) puede aumentar el esfuerzo mental, mientras que la complejidad de sistemas que involucran múltiples modelos inteligentes amplifica tanto las cargas cognitivas como las computacionales. El modelo busca un “punto óptimo” pedagógico, en el que una demanda de procesamiento moderada (por ejemplo, una cuantización más estable) minimice el esfuerzo mental, adaptándose al nivel de experiencia del estudiante. Su objetivo es desarrollar habilidades metacognitivas para gestionar estas compensaciones en tareas complejas, especialmente en contextos con restricciones de hardware. Las estrategias didácticas incluyen secuenciación progresiva, actividades comparativas y ejecución asíncrona para tareas con alta demanda de procesamiento, cuyos resultados son analizados mediante prácticas reflexivas. Herramientas de apoyo, como la plataforma CCLOLMAS (disponible en https://github.com/cclolmas/app), hacen visibles estas dinámicas. El objetivo es reformular los currículos, alineando la formación con las demandas de la industria y capacitando a los estudiantes para gestionar eficazmente estas compensaciones esenciales en la práctica profesional de la ingeniería de software.
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AFRIN, S. et al. Resource-Efficient & Effective Code Summarization. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2502.03617. Acesso em: 20 abr. 2025.
AHMADI, A.; SHARIF, S.; BANAD, Y. M. MCP Bridge: A Lightweight, LLM-Agnostic RESTful Proxy for Model Context Protocol Servers. [S. l.]: arXiv,
Disponível em: https://arxiv.org/abs/2504.08999. Acesso em: 20 abr. 2025.
AMERSHI, S. et al. Guidelines for Human-AI Interaction. In: CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI ’19), 2019, Glasgow. Proceedings [...]. New York: ACM, 2019. p. 1–13. DOI: 10.1145/3290605.3300233. Disponível em: https://doi.org/10.1145/3290605.3300233. Acesso em: 20 abr. 2025.
ANDREWS, G. R. Foundations of Multithreaded, Parallel, and Distributed Programming. Reading, MA: Addison-Wesley, 2000.
AYUPOV, S.; CHIRKOVA, N. Parameter-efficient finetuning of transformers for source code. [S. l.]: arXiv, 2022. Disponível em: https://arxiv.org/abs/2212.05901. Acesso em: 20 abr. 2025.
BAI, G. et al. Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models. [S. l.]: arXiv, 2024. Disponível em: https://arxiv.org/abs/2401.00625. Acesso em: 20 abr. 2025
BEN-ARI, M. Principles of Concurrent and Distributed Programming. 2. ed. Harlow: Addison-Wesley, 2006.
BIRD, S. et al. Taking Flight with Copilot: Early Insights and Opportunities of Large Language Models in Programming Education. In: ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE '22), 2022, Providence. Proceedings [...]. New York: ACM, 2022. p. 1032–1032. DOI: 10.1145/3478431.3499901.
BIRKMOSE, R. et al. On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2502.12923. Acesso em: 20 abr. 2025.
BRASIL. Lei nº 13.005, de 25 de junho de 2014. Aprova o Plano Nacional de Educação - PNE e dá outras providências. Diário Oficial da União: seção 1, Brasília, DF, ed. extra, p. 1, 26 jun. 2014. Disponível em: http://www.planalto.gov.br/ccivil_03/_ato2011-2014/2014/lei/l13005.htm. Acesso em: 25 abr. 2025.
BRASIL. [Ministério da Educação]. Países dos Brics discutem avaliação da educação superior. Brasília, DF: MEC, 2025. Disponível em: https://www.gov.br/mec/pt-br/assuntos/noticias/2025/abril/paises-dos-brics-discutem-avaliacao-da-educacao-superior. Acesso em: 3 maio 2025.
BRASIL. Conselho Nacional de Educação. Câmara de Educação Superior. Resolução CNE/CES nº 5, de 16 de novembro de 2016. Institui as Diretrizes Curriculares Nacionais para os cursos de graduação na área da Computação [...]. Brasília, DF: MEC, 2016. Disponível em: http://portal.mec.gov.br/docman/novembro-2016-pdf/52101-rces005-16-pdf/file. Acesso em: 25 abr. 2025.
BRASIL. Conselho Nacional de Educação. Câmara de Educação Superior. Resolução CNE/CES nº 2, de 24 de abril de 2019. Institui as Diretrizes Curriculares Nacionais do Curso de Graduação em Engenharia. Diário Oficial da União: seção 1, Brasília, DF, p. 42, 26 abr. 2019. Disponível em: http://portal.mec.gov.br/index.php?option=com_docman&view=download&alias=112091-rces002-19&category_slug=abril-2019-pdf&Itemid=30192. Acesso em: 25 abr. 2025.
BRASIL. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. Diretrizes comuns da Avaliação de Permanência dos Programas de Pós-Graduação stricto sensu: Ciclo avaliativo 2025-2028, Avaliação Quadrienal 2029. Brasília, DF: CAPES, 2025. DOI 10.21713/Diretrizescomuns. Disponível em: https://uploads.capes.gov.br/files/2025-05-02_DocumentoReferencial_FICHA.pdf. Acesso em: 3 maio 2025.
BRASIL. Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira. Relatório do 2º Ciclo de Monitoramento das Metas do Plano Nacional de Educação 2022. Brasília, DF: INEP, 2023. Disponível em: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/pne/monitoramento-continuo. Acesso em: 25 abr. 2025.
BRASIL. Ministério da Educação. Conferência Nacional de Educação (CONAE) 2024. Brasília, DF: MEC, 2024. Disponível em: https://www.gov.br/mec/pt-br/acesso-a-informacao/participacao-social/conferencias/conae-2024. Acesso em: 20 abr. 2025.
CASPERSEN, M. E.; BENNEDSEN, J. Instructional design of programming education: a learning theoretic approach. ACM SIGCSE Bulletin, v. 39, n. 4, p. 111-124, Dec. 2007. DOI: 10.1145/1345375.1345413.
CHASE, W. G.; SIMON, H. A. Perception in chess. Cognitive Psychology, v. 4, n. 1, p. 55-81, 1973. DOI: 10.1016/0010-0285(73)90004-2. Disponível em: https://doi.org/10.1016/0010-0285(73)90004-2. Acesso em: 20 abr. 2025.
CHEN, D. et al. Overthinking Leads to Errors: Mitigating Overthinking in Large Language Models through Task Difficulty Calibration. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.13367. Acesso em: 24 abr. 2025.
DENNING, P. J.; TEDRE, M. Computational Thinking. Cambridge, MA: MIT Press, 2019.
DETTMERS, T. et al. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, v. 36, 2023. Disponível em: https://proceedings.neurips.cc/paper_files/paper/2023/hash/6a4d5956f508541689d0e5c5a878e074-Abstract-Conference.html. Acesso em: 20 abr. 2025.
DURAN, R.; ZAVGORODNIAIA, A.; SORVA, J. Cognitive Load Theory in Computing Education Research: A Review. ACM Transactions on Computing Education, v. 22, n. 4, art. 40, p. 1–27, 2022. DOI: 10.1145/3532776. Disponível em: https://doi.org/10.1145/3532776. Acesso em: 20 abr. 2025.
ENGESTROM, Y. Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit, 1987.
FERRAG, Mohamed Amine; TIHANYI, Norbert; DEBBAH, Merouane. From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review. [S. l.]: arXiv, 28 abr. 2025. Disponível em: https://arxiv.org/pdf/2504.19678. Acesso em: 5 maio 2025.
FLORIDI, L.; TADDEO, M. What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 374, n. 2083, p. 20160360, 2016. DOI: 10.1098/rsta.2016.0360. Disponível em: https://doi.org/10.1098/rsta.2016.0360. Acesso em: 20 abr. 2025.
GAO, Xin et al. A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis. [S. l.]: arXiv, 21 abr. 2025. Disponível em: https://arxiv.org/html/2504.12322v2. Acesso em: 5 maio 2025.
GHOSH, D. P. Agentic ecosystem in engineering design: a framework for interoperable legacy tools and emergent collaboration via MCP/A2A protocols. Kolkata: Engineering
Design & Research Center, Larsen & Toubro Construction, 2025. Disponível em: https://www.researchgate.net/publication/390953589. Acesso em: 20 abr. 2025.
GHOSH, Dibya; GHOSH, Dyuti; GHOSH, Debi Prasad. Think in Arrows: A Categorical Scaffolding Framework for Robust Artificial Scientific Discovery. [S. l.]: ResearchGate, abr. 2025. Preprint. DOI: 10.13140/RG.2.2.16950.41280. Acesso em: 5 maio 2025.
GKINTONI, E. et al. Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences, v. 15, n. 2, p. 203, 2025. DOI: 10.3390/brainsci15020203. Disponível em: https://doi.org/10.3390/brainsci15020203. Acesso em: 20 abr. 2025.
HASSAN, Ahmed E. et al. Rethinking Software Engineering in the Era of Foundation Models: A Curated Catalogue of Challenges in the Development of Trustworthy FMware. In: INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING COMPANION (ICSE '24 Companion), 2024, Lisbon. Proceedings [...]. New York: ACM, 2024. p. 1010-1019. DOI: 10.1145/3663529.3663849.
HEER, J. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences, v. 116, n. 6, p. 1844-1850, 2019. DOI: 10.1073/pnas.1807180115. Disponível em: https://doi.org/10.1073/pnas.1807180115. Acesso em: 20 abr. 2025.
HMELO-SILVER, C. E. Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, v. 16, n. 3, p. 235-266, 2004. DOI: 10.1023/B:EDPR.0000034022.16470.f3.
HOC, J.-M. From human-machine interaction to human-machine cooperation. Ergonomics, v. 43, n. 7, p. 833-843, 2000. DOI: 10.1080/001401300409060. Disponível em: https://doi.org/10.1080/001401300409060. Acesso em: 20 abr. 2025.
HUSOM, E. J. et al. Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.03360. Acesso em: 20 abr. 2025.
HUTCHINS, E. Cognition in the Wild. Cambridge, MA: MIT Press, 1995.
JIA, F. et al. Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.08378. Acesso em: 20 abr. 2025.
JIANG, A. Q. et al. Mistral 7B. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2310.06825. Acesso em: 20 abr. 2025.
JOBIN, A.; IENCA, M.; VAYENA, E. The global landscape of AI ethics guidelines. Nature Machine Intelligence, v. 1, n. 9, p. 389-399, 2019. DOI: 10.1038/s42256-019-0088-2. Disponível em: https://doi.org/10.1038/s42256-019-0088-2. Acesso em: 20 abr. 2025.
KALYUGA, S. et al. The expertise reversal effect. Educational Psychologist, v. 38, n. 1, p. 23-31, 2003. DOI: 10.1207/S15326985EP3801_4. Disponível em: https://doi.org/10.1207/S15326985EP3801_4. Acesso em: 20 abr. 2025.
KIM, S. et al. SqueezeLLM: Dense-and-Sparse Quantization. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2306.07629. Acesso em: 20 abr. 2025.
KIM, Y. et al. One ruler to measure them all: Benchmarking multilingual long-context language models. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2503.01996. Acesso em: 20 abr. 2025.
KOTALWAR, N.; GOTOVOS, A.; SINGLA, A. Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2406.05053. Acesso em: 20 abr. 2025.
KUMAR, K. et al. LLM Post-Training: A Deep Dive into Reasoning Large Language Models. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2502.21321. Acesso em: 20 abr. 2025.
LEINONEN, J. et al. Using Large Language Models to Enhance Programming Education. In: ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH (ICER '23), 2023, Chicago. Proceedings [...]. New York: ACM, 2023. p. 1–15. DOI: 10.1145/3568813.3600141.
LI, R. et al. StarCoder: may the source be with you! [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2305.06161. Acesso em: 25 abr. 2025.
LIN, Kevin et al. Sleep-time Compute: Beyond Inference Scaling at Test-time. [S. l.]: arXiv, 17 abr. 2025. Preprint. Disponível em: https://arxiv.org/html/2504.13171v1. Acesso em: 3 maio 2025.
LIU, J.; SHA, C.; PENG, X. An empirical study of parameter-efficient fine-tuning methods for pre-trained code models. In: IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE), 38., 2023, Kirchberg. Proceedings [...]. Piscataway: IEEE, 2023. p. 397–408. DOI: 10.1109/ASE56229.2023.00041. Acesso em: 20 abr. 2025.
LIU, Na et al. From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models. [S. l.]: arXiv, 30 jan. 2024. Disponível em: https://arxiv.org/pdf/2401.02777. Acesso em: 5 abr. 2025.
LIU, Y. et al. AgentBench: Evaluating LLMs as Agents. [S. l.]: arXiv, 2025b. Disponível em: https://arxiv.org/abs/2308.03688. Acesso em: 20 abr. 2025.
LUAN, H. et al. Who Needs to Learn Programming Nowadays? The Impact of AI on Programming Education. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2305.10881. Acesso em: 25 abr. 2025.
LUO, J. et al. Large Language Model Agent: A Survey on Methodology, Applications and Challenges. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2503.21460. Acesso em: 20 abr. 2025.
MA, Hao et al. Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning. [S. l.]: arXiv, 22 fev. 2025. Disponível em: https://arxiv.org/html/2410.06101v2. Acesso em: 5 maio 2025.
MARJANOVIC, S. V. et al. DeepSeek-R1 Thoughtology: Let’s about LLM reasoning. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.07128. Acesso em: 20 abr. 2025.
Educational Psychologist, v. 38, n. 1, p. 43-52, 2003. DOI: 10.1207/S15326985EP3801_6. Disponível em: https://doi.org/10.1207/S15326985EP3801_6. Acesso em: 20 abr. 2025.MAYER, R. E.; MORENO, R. Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, v. 38, n. 1, p. 43-52, 2003. DOI: 10.1207/S15326985EP3801_6. Disponível em: https://doi.org/10.1207/S15326985EP3801_6. Acesso em: 20 abr. 2025.
MHLANGA, D. The Role of Artificial Intelligence in Enhancing Education Quality in Developing Countries. Sustainability, v. 15, n. 15, p. 11608, 2023. DOI: 10.3390/su151511608.
MISHRA, P.; KOEHLER, M. J. Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, v. 108, n. 6, p. 1017–1054, Jun. 2006. DOI: 10.1111/j.1467-9620.2006.00684.x. Acesso em: 20 abr. 2025.
NAGEL, M. et al. A White Paper on Neural Network Quantization. [S. l.]: arXiv, 2021. Disponível em: https://arxiv.org/abs/2106.08295. Acesso em: 20 abr. 2025.
NI, J. et al. From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.13471. Acesso em: 24 abr. 2025.
NIELSEN, J. Usability Engineering. Boston: Academic Press, 1993.
NORMAN, D. A. The psychology of everyday things. New York: Basic Books, 1988.
OPENAI. A practical guide to building agents. OpenAI Business Guides and Resources, 2025. Disponível em: https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf. Acesso em: 20 abr. 2025.
PAAS, F. et al. Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, v. 38, n. 1, p. 63-71, 2003. DOI: 10.1207/S15326985EP3801_8. Disponível em: https://doi.org/10.1207/S15326985EP3801_8. Acesso em: 20 abr. 2025.
PAHUNE, S.; AKHTAR, Z. Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models. Information, v. 16, n. 2, 87, 2025. DOI: 10.3390/info16020087. Acesso em: 24 abr. 2025.
PERRY, N. et al. Do Users Write More Insecure Code with AI Assistants? [S. l.]: arXiv, 2022. Disponível em: https://arxiv.org/abs/2211.03622. Acesso em: 25 abr. 2025.
PICCOLO, F. et al. Exploring the Impact of Large Language Models on Novice Programming. In: ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE '24), 2024, Portland. Proceedings [...]. New York: ACM, 2024. p. 100–106. DOI: 10.1145/3626252.3630901.
PU, X. et al. ThoughtTerminator: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.13367. Acesso em: 24 abr. 2025.
QIAN, Y.; LEHMAN, J. Students' Misconceptions and Other Difficulties in Introductory Programming: A Literature Review. ACM Transactions on Computing Education (TOCE), v. 18, n. 1, p. 1-24, 2017. DOI: 10.1145/3077608.
RAY, Partha Pratim. A Survey on Model Context Protocol: Architecture, State-of-the-art, Challenges and Future Directions. [S. l.]: TechRxiv, 2025. Preprint. DOI: 10.36227/techrxiv.174495492.22752319. Acesso em: 5 maio 2025.
RETZLAFF, M. et al. Evaluating Large Language Models for HCI. [S. l.]: arXiv, 2024. Disponível em: https://arxiv.org/abs/2402.01811. Acesso em: 20 abr. 2025.
ROZIERE, B. et al. Code Llama: Open Foundation Models for Code. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2308.12950. Acesso em: 25 abr. 2025.
SHUTE, V. J. Focus on Formative Feedback. Review of Educational Research, v. 78, n. 1, p. 153-189, 2008. DOI: 10.3102/0034654307313795.
SIDDIQ, M. A. et al. Generative AI in Software Engineering Education: A Systematic Literature Review. [S. l.]: arXiv, 2024. Disponível em: https://arxiv.org/abs/2401.10841. Acesso em: 25 abr. 2025.
SPIESS, C. et al. AutoPDL: Automatic Prompt Optimization for LLM Agents. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.04365. Acesso em: 24 abr. 2025.
SWELLER, J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, v. 4, n. 4, p. 295-312, 1994. DOI: 10.1016/0959-4752(94)90003-5. Acesso em: 20 abr. 2025.
SWELLER, J. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, v. 22, n. 2, p. 123-138, 2010. DOI: 10.1007/s10648-010-9128-5. Acesso em: 20 abr. 2025.
-5. Acesso em: 20 abr. 2025.
SWELLER, J.; CHANDLER, P. Why some material is difficult to learn. Cognition and Instruction, v. 12, n. 3, p. 185-233, 1994. DOI: 10.1207/s1532690xci1203_1. Acesso em: 20 abr. 2025.
SWELLER, J.; VAN MERRIENBOER, J. J. G.; PAAS, F. Cognitive Architecture and Instructional Design. Educational Psychology Review, v. 10, n. 3, p. 251-296, 1998. DOI: 10.1023/A:1022193728205. Acesso em: 20 abr. 2025.
TANTITHAMTHAVORN, C. et al. MLOps, LLMOps, FMOps, and Beyond. IEEE Software, v. 42, n. 1, p. 26-32, jan./fev. 2025. DOI: 10.1109/MS.2024.3477014.
TATE, T. et al. ChatGPT and the Future of University Assessment. [S. l.]: EdArXiv, 2023. DOI: 10.35542/osf.io/562k3. Disponível em: https://edarxiv.org/562k3/. Acesso em: 25 abr. 2025.
THOMAS, J. W. A review of research on project-based learning. San Rafael, CA: Autodesk Foundation, 2000. Disponível em: http://www.bie.org/research/study/review_of_project_based_learning. Acesso em: 25 abr. 2025.
TRUONG, Q.-T. et al. Towards Automated Testing of LLM-based Applications: A Survey. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2311.18119. Acesso em: 20 abr. 2025.
VAN MERRIENBOER, J. J. G.; SWELLER, J. Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, v. 17, n. 2, p. 147-177, 2005. DOI: 10.1007/s10648-005-3951-0. Acesso em: 20 abr. 2025.
WANG, H. et al. Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability. [S. l.]: arXiv, 2025a. Disponível em: https://arxiv.org/abs/2504.09639. Acesso em: 20 abr. 2025.
WANG, Hongru et al. OTC: Optimal Tool Calls via Reinforcement Learning. [S. l.]: arXiv, 21 abr. 2025b. Disponível em: https://arxiv.org/html/2504.14870v1. Acesso em: 5 maio 2025.
WOOD, D.; BRUNER, J. S.; ROSS, G. The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, v. 17, n. 2, p. 89-100, 1976. DOI: 10.1111/j.1469-7610.1976.tb00381.x. Acesso em: 20 abr. 2025.
WU, T. et al. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework. [S. l.]: arXiv, 2023. Disponível em: https://arxiv.org/abs/2308.08155. Acesso em: 20 abr. 2025.
XIA, S. et al. Unlocking Deep Thinking in Language Models: Cognition Engineering through Inference Time Scaling and Reinforcement Learning. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2504.13828. Acesso em: 24 abr. 2025.
XINOGALOS, S. Cognitive Load in Computer Science Education: A Systematic Literature Review. IEEE Transactions on Learning Technologies, v. 14, n. 5, p. 648-664, Sept.-Oct. 2021. DOI: 10.1109/TLT.2021.3122001.
XU, W. Human-Centered AI. Boca Raton: CRC Press, 2019.
YAO, Z. et al. ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [S. l.]: arXiv, 2022. Disponível em: https://arxiv.org/abs/2206.01861. Acesso em: 20 abr. 2025.
YETISTIREN, B. et al. A Systematic Literature Review on the Use of Large Language Models in Software Engineering. [S. l.]: arXiv, 2024. Disponível em: https://arxiv.org/abs/2402.18551. Acesso em: 25 abr. 2025.
ZHANG, J.; NORMAN, D. A. Representations in distributed cognitive tasks. Cognitive Science, v. 18, n. 1, p. 87-122, 1994. DOI: 10.1207/s15516709cog1801_3. Acesso em: 20 abr. 2025.
ZHANG, T. et al. LLM-Orchestrator: Orchestrating Collaborative Large Language Models for Automated Software Engineering. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2402.16831. Acesso em: 20 abr. 2025.
ZHAO, P.; YAUN, X. GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models. [S. l.]: arXiv, 2025. Disponível em: https://arxiv.org/abs/2501.12956. Acesso em: 20 abr. 2025.
ZIMMERMAN, B. J. Becoming a self-regulated learner: An overview. Theory Into Practice, v. 41, n. 2, p. 64-70, 2002. DOI: 10.1207/s15430421tip4102_2. Acesso em: 20 abr. 2025.
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