Avaliação Human-Centered de Sistemas de Recomendação Baseados em IA: Uma Análise Comparativa entre Domínios
Resumo
Introdução e objetivo: Este estudo analisa como sistemas de recomendação baseados em IA são percebidos sob uma perspectiva centrada no humano, considerando a influência do domínio de uso e do custo emocional do erro na desejabilidade de explicações. Metodologia: Foi conduzida uma investigação qualitativa comparativa entre Spotify (entretenimento) e Duolingo (educação), estruturada a partir do framework RE4HCAI e complementada por mapas de empatia. Resultados: Os resultados indicam que a percepção da IA é mediada pelo contexto de uso e pelas respostas emocionais associadas ao erro, e não apenas pelo grau de transparência técnica. Conclui-se que a explicabilidade deve ser calibrada conforme o domínio e o impacto emocional da interação.
Palavras-chave:
sistemas de recomendação baseados em IA, inteligência artificial centrada no humano, inteligência artificial explicável, custo emocional do erro, engenharia de requisitos
Referências
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Ahmad, K., Abdelrazek, M., Arora, C., Baniya, A. A., Bano, M., and Grundy, J. (2023). Requirements engineering framework for human-centered artificial intelligence software systems. Applied Soft Computing, 143:110455.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information fusion, 58:82–115.
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Gheewala, S., Xu, S., and Yeom, S. (2025). In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends. Neural Computing and Applications, 37(17):10875–10947.
Gray, D., Brown, S., and Macanufo, J. (2010). Gamestorming: A playbook for innovators, rulebreakers, and changemakers. ”O’Reilly Media, Inc.”.
Jadon, A. and Patil, A. (2024). A comprehensive survey of evaluation techniques for recommendation systems. In International Conference on Computation of Artificial Intelligence & Machine Learning, pages 281–304. Springer.
Liao, Q. V., Gruen, D., and Miller, S. (2020). Questioning the ai: informing design practices for explainable ai user experiences. In Proceedings of the 2020 CHI conference on human factors in computing systems, pages 1–15.
Minh, D., Wang, H. X., Li, Y. F., and Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55(5):3503–3568.
Mohamed, A., Abdelqader, K., and Shaalan, K. (2025). Explainable artificial intelligence: A systematic review of progress and challenges. Intelligent Systems with Applications, page 200595.
Raza, S., Rahman, M., Kamawal, S., Toroghi, A., Raval, A., Navah, F., and Kazemeini, A. (2024). A comprehensive review of recommender systems: Transitioning from theory to practice. arXiv preprint arXiv:2407.13699.
Ricci, F., Rokach, L., and Shapira, B. (2021). Recommender systems: Techniques, applications, and challenges. Recommender systems handbook, pages 1–35.
Santos, B., Lima, M., Ribeiro, M., and Conte, T. (2025). Teaching requirements engineering for human-centered ai: A classroom experience with the re4hcai framework. In Simpósio Brasileiro de Qualidade de Software (SBQS), pages 540–550. SBC.
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6):495–504.
Vilone, G. and Longo, L. (2020). Explainable artificial intelligence: a systematic review. arXiv preprint arXiv:2006.00093.
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., and Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Frontiers in psychology, 15:1387089.
Zhang, K., Xie, Y., He, Y., and Wang, J. (2025). Emotional influences on user continuous use intention in recommended news apps: A study of algorithm appreciation and fatigue within the cognition-affect-conation framework. Acta Psychologica, 256:105002.
Ahmad, K., Abdelrazek, M., Arora, C., Baniya, A. A., Bano, M., and Grundy, J. (2023). Requirements engineering framework for human-centered artificial intelligence software systems. Applied Soft Computing, 143:110455.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information fusion, 58:82–115.
Bauer, C., Said, A., and Zangerle, E. (2024). Evaluation perspectives of recommender systems: Driving research and education (dagstuhl seminar 24211). Dagstuhl Reports, 14(5):58–172.
Cooper, A. et al. (2004). The inmates are running the asylum: Why high-tech products drive us crazy and how to restore the sanity, volume 2. Sams Indianapolis.
Duricic, T., Kowald, D., Lacic, E., and Lex, E. (2023). Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks. Frontiers in big data, 6:1251072.
Gheewala, S., Xu, S., and Yeom, S. (2025). In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends. Neural Computing and Applications, 37(17):10875–10947.
Gray, D., Brown, S., and Macanufo, J. (2010). Gamestorming: A playbook for innovators, rulebreakers, and changemakers. ”O’Reilly Media, Inc.”.
Jadon, A. and Patil, A. (2024). A comprehensive survey of evaluation techniques for recommendation systems. In International Conference on Computation of Artificial Intelligence & Machine Learning, pages 281–304. Springer.
Liao, Q. V., Gruen, D., and Miller, S. (2020). Questioning the ai: informing design practices for explainable ai user experiences. In Proceedings of the 2020 CHI conference on human factors in computing systems, pages 1–15.
Minh, D., Wang, H. X., Li, Y. F., and Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55(5):3503–3568.
Mohamed, A., Abdelqader, K., and Shaalan, K. (2025). Explainable artificial intelligence: A systematic review of progress and challenges. Intelligent Systems with Applications, page 200595.
Raza, S., Rahman, M., Kamawal, S., Toroghi, A., Raval, A., Navah, F., and Kazemeini, A. (2024). A comprehensive review of recommender systems: Transitioning from theory to practice. arXiv preprint arXiv:2407.13699.
Ricci, F., Rokach, L., and Shapira, B. (2021). Recommender systems: Techniques, applications, and challenges. Recommender systems handbook, pages 1–35.
Santos, B., Lima, M., Ribeiro, M., and Conte, T. (2025). Teaching requirements engineering for human-centered ai: A classroom experience with the re4hcai framework. In Simpósio Brasileiro de Qualidade de Software (SBQS), pages 540–550. SBC.
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6):495–504.
Vilone, G. and Longo, L. (2020). Explainable artificial intelligence: a systematic review. arXiv preprint arXiv:2006.00093.
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., and Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Frontiers in psychology, 15:1387089.
Zhang, K., Xie, Y., He, Y., and Wang, J. (2025). Emotional influences on user continuous use intention in recommended news apps: A study of algorithm appreciation and fatigue within the cognition-affect-conation framework. Acta Psychologica, 256:105002.
Publicado
19/07/2026
Como Citar
NUNES, Ana Beatriz Maciel; LIMA, Márcia Sampaio.
Avaliação Human-Centered de Sistemas de Recomendação Baseados em IA: Uma Análise Comparativa entre Domínios. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 132-142.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.21929.
