Possibilidades de uso da plataforma GenIA para a construção de algoritmos: um auxílio para o desenvolvimento do pensamento computacional

Abstract

The Computational Thinking (CT) curricular component was implemented in public schools in Paraná in 2022, covering from the 8th grade of elementary school to the 1st year of high school in regular schools, and from the 6th grade to the 1st year in civicmilitary and full-time schools. Aligned with the Brazilian National Common Curricular Base (BNCC), the component has been predominantly developed using the programming languages Scratch and JavaScript, requiring students to develop skills such as algorithm creation, problem-solving, and logical organization of thought. Considering that CT is addressed in the BNCC within the scope of mathematics, this research adopts the conception proposed by Navarro, which defines CT as the integration of three conceptual nexuses: algorithmic thinking, algebraic thinking, and problem-solving. From this perspective, the potential of the GenIA platform, developed by the Research Group on Technologies in Mathematics Education (GPTEM), was investigated as a tool to support the understanding of algorithms and, consequently, the development of CT. GenIA is based on flowchart logic and offers a visual and intuitive interface. Initially designed for the creation of AI-assisted Learning Objects (LO) in mathematics, the platform has potential for broader application across other curricular areas. The study, qualitative in nature, was conducted with 54 students from two 8th-grade classes at a state school in Paraná over the course of nine lessons. The methodology employed was Educational Design Research (EDR), structured in three phases: preliminary, prototyping, and evaluation. EDR focuses on the development and refinement of educational interventions through iterative cycles of analysis and redesign. Activities were carried out both in the computer lab, with direct use of GenIA, and in the classroom, using printed flowcharts and platform-based materials. The results indicated strong student engagement, despite technical limitations related to running GenIA in a virtual machine environment. Students reported finding flowchartbased programming easier, highlighting the clarity provided by the visual sequencing of steps. The creation of math-related LO contributed not only to the logical construction of algorithms but also to the understanding of the mathematical concepts involved. These findings reinforce the potential of GenIA as a valuable tool for improving algorithm comprehension and point to further research opportunities in the use of intuitive programming in mathematics education and in strengthening CT.