Geração automática de questões de programação baseada em templates multicamadas

• Ano de defesa: 2025
• Autor(a) principal: Silva, Abner de Santana
• Orientador(a): Aranha, Eduardo Henrique da Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
• País: BR
• Palavras-chave em Português: Computação; Geração automática de questões; Exercícios de programação; Modelo cognitivo; Templates multicamadas; Inteligência artificial generativa
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/65701

Developing programming skills in introductory courses requires frequent practice exercises, yet manually crafting questions is time-consuming and laborintensive. To address this challenge, this study investigates automatic question generation through multilayer templates supported by generative artificial intelligence, which suggests variations and provides feedback to students. A web-based tool was created that employs JSON files to define hierarchical templates and their question instances. A mixed-methods research design was adopted, including a case study with eight instructors to evaluate templateconstruction time, the number of questions generated, and perceptions gathered via questionnaires. Most instructors produced a complete template within 30 minutes, although they preferred adapting pre-existing models. The primary difficulties reported were the steep learning curve, direct JSON manipulation, and the absence of a graphical interface for visualizing layers. Future work involves expanding the template repository, developing a visual editor, and evaluating the solution with larger samples.