Aplicação da Engenharia de Prompt na Elaboração de Atividades de Estimulação Cognitiva Baseadas em Pensamento Computacional para o Público Idoso
Resumo
O presente estudo tem como objetivo explorar as técnicas de engenharia de prompt para a geração de exercícios personalizados de estimulação cognitiva para a pessoa idosa, baseados nos pilares de pensamento computacional. A iniciativa contribui para as pesquisas na área de educação e inteligência artificial, auxiliando na construção de métodos e soluções que promovam o envelhecimento ativo e a inclusão digital nessa etapa da vida. Os resultados indicam que a integração das técnicas de engenharia de prompt mostrou-se eficaz, contribuindo para a melhoria na qualidade e precisão da geração dos exercícios. A identificação de estudos futuros evidencia o potencial da área, a fim de consolidar novas abordagens.Referências
BAHIA, Liege Mascarenhas; DA TRINDADE MASCARENHAS, Lívia Corrêa. 8. A promoção do envelhecimento cognitivo saudável. A COMPLEXIDADE DO ENVELHECIMENTO HUMANO: Para além da dimensão biológica, 2023.
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CATRICALÀ, Benedetta et al. Exploiting personal memories in humanoid robot serious games for mild cognitive impaired older adults. Behaviour & Information Technology, p. 1-26, 2025.
CHEN, Banghao et al. Unleashing the potential of prompt engineering for large language models. Patterns, 2025.
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EUN, Sung-Jong; KIM, Eun Joung; KIM, Jung Yoon. Development and evaluation of an artificial intelligence–based cognitive exercise game: A pilot study. Journal of environmental and public health, v. 2022, n. 1, p. 4403976, 2022.
GASPARI, Mauro; DONNICI, Margherita. Weekend in Rome: A cognitive training exercise based on planning. Bologna: Department of Computer Science and Engineering, University of Bologna, 2019.
GUO, Shuchen et al. Using generative AI and multi-agents to provide automatic feedback. arXiv preprint arXiv:2411.07407, 2024.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). Censo Demográfico 2022: Resultados da Amostra - Educação e Mobilidade. Rio de Janeiro: IBGE, 2023. Disponível em: [link]. Acesso em: 24 jul. 2025.
KIM, Jeongsim et al. Efficacy of smart speaker–based metamemory training in older adults: Case-control cohort study. Journal of medical Internet research, v. 23, n. 2, p. e20177, 2021.
LEI, Bin et al. Macm: Utilizing a multi-agent system for condition mining in solving complex mathematical problems. Advances in Neural Information Processing Systems, v. 37, p. 53418-53437, 2024.
LI, Ruijia et al. A Large Language Model-Enabled Solution for the Automatic Generation of Situated Multiple-Choice Math Questions. In: Conference Proceedings of the 28th Global Chinese Conference on Computers in Education (GCCCE 2024). 2024. p. 130-136.
LIMA, Maria R. et al. Promoting Cognitive Health in Elder Care with Large Language Model-Powered Socially Assistive Robots. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 2025. p. 1-22.
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MO, Kaijie; HU, Renfen. ExpertEase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large Language Models. In: Findings of the Association for Computational Linguistics: EMNLP 2024. 2024. p. 9080-9099.
MOON, Hyeonseok et al. Generative interpretation: Toward human-like evaluation for educational question-answer pair generation. In: Findings of the Association for Computational Linguistics: EACL 2024. 2024. p. 2185-2196.
OLIVEIRA JR, Emerson et al. Proposição de uma sequência didática baseada no pensamento computacional para idosos. In: Anais do Congresso Brasileiro de Informática na Educação-CBIE 2023–Sociedade Brasileira de Computação. CBIE 2023–Sociedade Brasileira de Computação, 2023. p. 1536-1545.
PIRES, Andressa Kroeff. Pense mais: jogo digital com treino cognitivo baseado em pensamento computacional com conteúdo de letramento digital para a pessoa idosa. 2022. Dissertação de Mestrado. Universidade Federal do Rio Grande do Norte.
RAY, Arindam; MUNSHI, Archisman. Designing a Generative AI Companion for Seniors. 2025.
RYTILAHTI, Juuso; WEERAKOON, Oshani; KAILA, Erkki. Exploring AI-driven programming exercise generation. 2024 20th International CDIO, 2024.
TSAI, Yen-Ni; CHEN, Ming-Tsung. Research on Active Aging Learning and Satisfaction in the Elderly in the age of artificial intelligence. In: E3S Web of Conferences. EDP Sciences, 2020. p. 02007.
WANG, Yi Ru et al. NEWTON: Are large language models capable of physical reasoning?. arXiv preprint arXiv:2310.07018, 2023.
WING, Jeannette M. Computational thinking benefits society. 40th anniversary blog of social issues in computing, v. 2014, p. 26, 2014.
XU, Austin; MONROE, Will; BICKNELL, Klinton. Large language model augmented exercise retrieval for personalized language learning. In: Proceedings of the 14th Learning Analytics and Knowledge Conference. 2024. p. 284-294.
XU, Songlin; ZHANG, Xinyu; QIN, Lianhui. Eduagent: Generative student agents in learning. arXiv preprint arXiv:2404.07963, 2024.
YE, Jingheng et al. Focus is what you need for chinese grammatical error correction. arXiv preprint arXiv:2210.12692, 2022.
YENDURI, Gokul et al. Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 2024.
YUN, Byung Hun et al. Development and Effectiveness of an AI Chatbot-Based Mobile Cognitive Screening and Customized Training Application for Preventing Dementia: Older Adults Living in Rural Areas of South Korea. Archives of Design Research, v. 37, n. 5, p. 77-90, 2024.
BOONSTRA, Lee. Prompt engineering. 2024. Disponível em: [link]. Acesso em: 22 jul. 2025.
CATRICALÀ, Benedetta et al. Exploiting personal memories in humanoid robot serious games for mild cognitive impaired older adults. Behaviour & Information Technology, p. 1-26, 2025.
CHEN, Banghao et al. Unleashing the potential of prompt engineering for large language models. Patterns, 2025.
CHU, Z. et al. Llm agents for education: Advances and applications. arXiv 2025. arXiv preprint arXiv:2503.11733.
EUN, Sung-Jong; KIM, Eun Joung; KIM, Jung Yoon. Development and evaluation of an artificial intelligence–based cognitive exercise game: A pilot study. Journal of environmental and public health, v. 2022, n. 1, p. 4403976, 2022.
GASPARI, Mauro; DONNICI, Margherita. Weekend in Rome: A cognitive training exercise based on planning. Bologna: Department of Computer Science and Engineering, University of Bologna, 2019.
GUO, Shuchen et al. Using generative AI and multi-agents to provide automatic feedback. arXiv preprint arXiv:2411.07407, 2024.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). Censo Demográfico 2022: Resultados da Amostra - Educação e Mobilidade. Rio de Janeiro: IBGE, 2023. Disponível em: [link]. Acesso em: 24 jul. 2025.
KIM, Jeongsim et al. Efficacy of smart speaker–based metamemory training in older adults: Case-control cohort study. Journal of medical Internet research, v. 23, n. 2, p. e20177, 2021.
LEI, Bin et al. Macm: Utilizing a multi-agent system for condition mining in solving complex mathematical problems. Advances in Neural Information Processing Systems, v. 37, p. 53418-53437, 2024.
LI, Ruijia et al. A Large Language Model-Enabled Solution for the Automatic Generation of Situated Multiple-Choice Math Questions. In: Conference Proceedings of the 28th Global Chinese Conference on Computers in Education (GCCCE 2024). 2024. p. 130-136.
LIMA, Maria R. et al. Promoting Cognitive Health in Elder Care with Large Language Model-Powered Socially Assistive Robots. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 2025. p. 1-22.
LUCENA, Daniel; NUNES, Isabel. Pensamento Computacional como intervenção para desenvolvimento cognitivo em idosos. In: Anais dos Workshops do Congresso Brasileiro de Informática na Educação. 2019. p. 1447.
MO, Kaijie; HU, Renfen. ExpertEase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large Language Models. In: Findings of the Association for Computational Linguistics: EMNLP 2024. 2024. p. 9080-9099.
MOON, Hyeonseok et al. Generative interpretation: Toward human-like evaluation for educational question-answer pair generation. In: Findings of the Association for Computational Linguistics: EACL 2024. 2024. p. 2185-2196.
OLIVEIRA JR, Emerson et al. Proposição de uma sequência didática baseada no pensamento computacional para idosos. In: Anais do Congresso Brasileiro de Informática na Educação-CBIE 2023–Sociedade Brasileira de Computação. CBIE 2023–Sociedade Brasileira de Computação, 2023. p. 1536-1545.
PIRES, Andressa Kroeff. Pense mais: jogo digital com treino cognitivo baseado em pensamento computacional com conteúdo de letramento digital para a pessoa idosa. 2022. Dissertação de Mestrado. Universidade Federal do Rio Grande do Norte.
RAY, Arindam; MUNSHI, Archisman. Designing a Generative AI Companion for Seniors. 2025.
RYTILAHTI, Juuso; WEERAKOON, Oshani; KAILA, Erkki. Exploring AI-driven programming exercise generation. 2024 20th International CDIO, 2024.
TSAI, Yen-Ni; CHEN, Ming-Tsung. Research on Active Aging Learning and Satisfaction in the Elderly in the age of artificial intelligence. In: E3S Web of Conferences. EDP Sciences, 2020. p. 02007.
WANG, Yi Ru et al. NEWTON: Are large language models capable of physical reasoning?. arXiv preprint arXiv:2310.07018, 2023.
WING, Jeannette M. Computational thinking benefits society. 40th anniversary blog of social issues in computing, v. 2014, p. 26, 2014.
XU, Austin; MONROE, Will; BICKNELL, Klinton. Large language model augmented exercise retrieval for personalized language learning. In: Proceedings of the 14th Learning Analytics and Knowledge Conference. 2024. p. 284-294.
XU, Songlin; ZHANG, Xinyu; QIN, Lianhui. Eduagent: Generative student agents in learning. arXiv preprint arXiv:2404.07963, 2024.
YE, Jingheng et al. Focus is what you need for chinese grammatical error correction. arXiv preprint arXiv:2210.12692, 2022.
YENDURI, Gokul et al. Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 2024.
YUN, Byung Hun et al. Development and Effectiveness of an AI Chatbot-Based Mobile Cognitive Screening and Customized Training Application for Preventing Dementia: Older Adults Living in Rural Areas of South Korea. Archives of Design Research, v. 37, n. 5, p. 77-90, 2024.
Publicado
25/11/2025
Como Citar
OLIVEIRA, Cintia Reis de; NUNES, Isabel Dillmann.
Aplicação da Engenharia de Prompt na Elaboração de Atividades de Estimulação Cognitiva Baseadas em Pensamento Computacional para o Público Idoso. In: WORKSHOP DE APLICAÇÕES PRÁTICAS DE LEARNING ANALYTICS E INTELIGÊNCIA ARTIFICIAL NO BRASIL (WAPLA), 3. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1-11.
DOI: https://doi.org/10.5753/wapla.2025.15289.
