Perceptions of the use of AI code generation tools ("vibe coding") and low-code platforms regarding technical debt and software architecture among advanced students in university software development programs in Cúcuta, 2026
Keywords:
Generative Artificial Intelligence, Low-Code, Software Architecture, Technical Debt, Vibe CodingAbstract
The accelerated growth of generative artificial intelligence tools applied to software development and the expansion of low-code platforms have transformed programming, documentation, and software construction dynamics in both academic and professional environments. This article aims to characterize the perception of advanced university students related to software development programs in Cúcuta regarding the use of AI-based code generation tools (vibe coding) and low-code platforms, as well as their relationship with technical debt and software architecture decisions during 2026. The research adopts a mixed-method approach with a descriptive scope and a non-experimental cross-sectional design. Data collection includes structured surveys, semi-structured interviews, and documentary review of authorized technical evidence. The study expects to identify the most frequently used tools, perceived benefits in productivity and learning, and risks associated with maintainability, code comprehension, technological dependency, component integration, and architectural sustainability. The findings will support the formulation of guidelines for the responsible adoption of generative AI and low-code tools in university environments related to software engineering.
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