Generative artificial intelligence has been rapidly incorporated into higher education, often without pedagogical regulation compatible with the cognitive and ethical demands of technically sensitive fields. In the teaching of Reinforced Concrete Structures, this scenario raises concerns regarding the development of structural reasoning, the traceability of decisions, and epistemic delegation in tasks involving high technical responsibility. In this context, the present study investigates the pedagogical, epistemic, and ethical impacts of the guided use of ChatGPT in solving a reinforced concrete beam design task, compared to a condition without algorithmic mediation. The analysis combined quantitative and qualitative methods, based on a rubric composed of seven procedural criteria and the evaluation of AI interaction logs. Although the mean performance in the no algorithmic mediation condition (6.07/10.0) was higher than in the GPT-Oriented condition (4.70/10.0), no statistically significant difference was identified (p > 0.05). However, qualitative analysis revealed systematic weaknesses under algorithmic mediation, including cumulative error propagation, normative inconsistencies, loss of reasoning traceability, and episodes of epistemic delegation. It is concluded that an appropriate educational response does not lie in prohibiting the technology, but in its regulated and progressive integration, proposing the Progressive Structural Mediation Model with AI (MESP-IA) as a theoretical-pedagogical contribution to the teaching of structurally sensitive disciplines.

pedagogical impacts of algorithmic mediation

Authors

DOI:

https://doi.org/10.35699/2237-5864.2026.64326

Keywords:

higher education learning, epistemic delegation, artificial intelligence, engineering education, algorithmic mediation

Abstract

Generative artificial intelligence has been rapidly incorporated into higher education, often without pedagogical regulation compatible with the cognitive and ethical demands of technically sensitive fields. In the teaching of Reinforced Concrete Structures, this scenario raises concerns regarding the development of structural reasoning, the traceability of decisions, and epistemic delegation in tasks involving high technical responsibility. In this context, the present study investigates the pedagogical, epistemic, and ethical impacts of the guided use of ChatGPT in solving a reinforced concrete beam design task, compared to a condition without algorithmic mediation. The analysis combined quantitative and qualitative methods, based on a rubric composed of seven procedural criteria and the evaluation of AI interaction logs. Although the mean performance in the no algorithmic mediation condition (6.07/10.0) was higher than in the GPT-Oriented condition (4.70/10.0), no statistically significant difference was identified (p > 0.05). However, qualitative analysis revealed systematic weaknesses under algorithmic mediation, including cumulative error propagation, normative inconsistencies, loss of reasoning traceability, and episodes of epistemic delegation. It is concluded that an appropriate educational response does not lie in prohibiting the technology, but in its regulated and progressive integration, proposing the Progressive Structural Mediation Model with AI (MESP-IA) as a theoretical-pedagogical contribution to the teaching of structurally sensitive disciplines.

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Author Biographies

  • Matheus Goulart Mena Barreto, Federal University of Santa Maria

    Mestrando em Arquitetura e Urbanismo e graduado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM).

  • Enzo Dantas Rossi, Federal University of Santa Maria

    Mestrando em Arquitetura e Urbanismo e graduado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM).

  • Gabriela Meller, Federal University of Pelotas

    Doutora e mestra em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM), onde também concluiu Licenciatura para Educação Profissional. Graduada em Engenharia Civil pela Universidade Regional do Noroeste do Estado do Rio Grande do Sul (UNIJUÍ). Professora adjunta do Centro de Engenharias da Universidade Federal de Pelotas (UFPel).

  • Débora Bretas Silva, Federal University of Santa Maria

    Doutora em Engenharia Civil pela Universidade Federal do Rio Grande do Sul (UFRGS), Mestra e graduada em Engenharia Civil pela Universidade Federal do Pampa (Unipampa). Professora assistente do Departamento de Arquitetura e Urbanismo da Universidade Federal de Santa Maria (UFSM).

  • Eduardo Cesar Pachla, Federal University of Santa Maria

    Engenheiro Civil pela Universidade Federal do Pampa (UNIPAMPA), com especializações em Gestão de Projetos e Projetos de Estruturas. Mestre em Engenharia, com ênfase em Tecnologia dos Materiais, pela UNIPAMPA e Doutor em Engenharia Civil, com ênfase em Construção Civil, pela Universidade Federal do Rio Grande do Sul (UFRGS).

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Published

2026-09-14

Issue

Vol. 16 (2026)

Section

Seção especial: IA nos processos de ensino-aprendizagem

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Copyright (c) 2026 Matheus Goulart Mena Barreto, Enzo Dantas Rossi, Gabriela Meller, Débora Bretas Silva, Eduardo Cesar Pachla

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How to Cite

BARRETO, Matheus Goulart Mena; ROSSI, Enzo Dantas; MELLER, Gabriela; SILVA, Débora Bretas; PACHLA, Eduardo Cesar. Generative artificial intelligence has been rapidly incorporated into higher education, often without pedagogical regulation compatible with the cognitive and ethical demands of technically sensitive fields. In the teaching of Reinforced Concrete Structures, this scenario raises concerns regarding the development of structural reasoning, the traceability of decisions, and epistemic delegation in tasks involving high technical responsibility. In this context, the present study investigates the pedagogical, epistemic, and ethical impacts of the guided use of ChatGPT in solving a reinforced concrete beam design task, compared to a condition without algorithmic mediation. The analysis combined quantitative and qualitative methods, based on a rubric composed of seven procedural criteria and the evaluation of AI interaction logs. Although the mean performance in the no algorithmic mediation condition (6.07/10.0) was higher than in the GPT-Oriented condition (4.70/10.0), no statistically significant difference was identified (p > 0.05). However, qualitative analysis revealed systematic weaknesses under algorithmic mediation, including cumulative error propagation, normative inconsistencies, loss of reasoning traceability, and episodes of epistemic delegation. It is concluded that an appropriate educational response does not lie in prohibiting the technology, but in its regulated and progressive integration, proposing the Progressive Structural Mediation Model with AI (MESP-IA) as a theoretical-pedagogical contribution to the teaching of structurally sensitive disciplines.: pedagogical impacts of algorithmic mediation. Revista Docência do Ensino Superior, Belo Horizonte, v. 16, p. 1–22, 2026. DOI: 10.35699/2237-5864.2026.64326. Disponível em: https://periodicos.ufmg.br/index.php/rdes/article/view/64326. Acesso em: 12 oct. 2026.