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THE TECHNICAL PROFICIENCY GAP IN ENGINEERING AND TECHNOLOGY EDUCATION IN BRAZIL: A PRE- AND POST-TEST ANALYSIS OF PYTHON FOR INDUSTRIAL AUTOMATION

LEITÃO, GABRIEL DE SOUZA; BARRETO, RAIMUNDO DA SILVA; RIBEIRO, LAURA MICHAELLA BATISTA; JUNIOR, JOSÉ CAVALCANTE LACERDA; FONTOURA, ANDERSON GADELHA; BREMGARTNER DA FROTA, VITOR; FILHO, LUÍS RICARDO ARANTES; CARVALHO, DOMINIQUE; GOUVEA, NELSON A.; SANTOS, ALYSON DE JESUS DOS; SANTOS, JECONIAS FERREIRA DOS

2026 • 18th International Conference on Education and New Learning Technologies

Resumo

Higher education in engineering and technology frequently integrates programming as a core competency. However, there is a significant discrepancy between students' perceived logical skills and the strict syntax requirements associated with industrial-grade automation. Within this context, this paper presents the initial phase of a longitudinal study involving engineering students from the fifth semester onward. The baseline diagnostic assessment (pre-test) revealed a striking proficiency paradox. Despite students reporting high confidence in their logical foundations, technical results showed critical failures in Python fundamentals: only 18% correctly identified loop/range outputs, and just 27% understood dictionary manipulation using the get() method. Most critically, only 22% correctly predicted the behavior of retry and backoff logic, which represents a cornerstone of Robotic Process Automation (RPA).The data indicate that development error rates increase proportionally with the demand for automated reasoning and an automation-oriented mindset. These results suggest that traditional academic training still presents bottlenecks in areas that are highly relevant to production-level automation, where small syntax-related misunderstandings may propagate into systemic failures. Consequently, this study describes a pedagogical intervention centered on hands-on RPA projects using tools such as Playwright, BotCity, and FastAPI. The intervention aims to strengthen industrial applicability and technical robustness in task automation using software bots increasingly adopted in modern industrial environments. A second assessment (post-test) was conducted upon completion of the initial Python/RPA module in order to evaluate the evolution of students’ technical proficiency. The results revealed an overall proficiency gain compared to the initial diagnostic stage, driven by the emphasis on robust automation structures. The central discussion of this paper emphasizes the importance of transitioning from logic-centered instruction toward a robustness-first methodology in modern engineering curricula. Keywords: Engineering Education, Python Syntax, Diagnostic Assessment, RPA, Pedagogical Gap.

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