This paper presents an educational tool designed to support active learning in engineering education by guiding and promoting the ethical use of artificial intelligence. The proposal targets undergraduate engineering students and is grounded in a real classroom context structured through university extension activities and project-based learning. The tool integrates artificial intelligence as a pedagogical support mechanism, assisting students in organizing technical reasoning, structuring disciplinary content, and communicating results, while preserving the central role of critical thinking and instructor mediation. The instructional design includes clearly defined learning activities, intended learning outcomes, assessment criteria, and ethical guidelines for the responsible use of artificial intelligence, to facilitate adoption and adaptation by other educators. Intended learning outcomes encompass the ability to analyze engineering concepts, synthesize technical information from diverse sources, and communicate effectively through written and audiovisual artifacts. The paper describes the instructional structure, activity sequence, and supporting materials required for implementation, thereby promoting straightforward replication in similar educational contexts. Classroom observations indicate increased student engagement, improved organization of technical content, and more reflective use of digital tools when artificial intelligence is explicitly framed as a learning support rather than as an autonomous content generator. The contribution emphasizes the practical, replicable nature of the proposed tool and invites the engineering education community to adapt, apply, and further evaluate the approach across diverse instructional settings.
The presence of artificial intelligence (AI) in education is an undeniable reality that affects all participants in the learning process. Geometry, as a subject with specific features and abstract concepts, presents a particularly promising field for exploring the potential of AI to enhance teaching and learning conditions. However, there is a lack of clear methodological guidance for the effective integration of AI in geometry education, highlighting the need for systematic and up-to-date reviews that synthesize current knowledge and provide practical insights for teachers and researchers. This study presents a systematic review of research on AI applications in geometry education conducted between 2023 and 2025. Following PRISMA guidelines, peer-reviewed journal articles published in English during this period were identified, resulting in a final sample of 20 studies. Thematic analysis was conducted to address key research questions, including the most studied topics, active regions of research, effective AI methods, strategies and tools, practical outcomes for teachers, and directions for future investigation. The review indicates that AI in geometry education has attracted global attention, with the most active studies originating from Europe and Asia. Research activity increased notably in 2024. The findings highlight the considerable potential of AI to support student learning through interactive modeling, automated feedback, and visualization of complex geometric concepts. At the same time, effective integration requires that teachers develop awareness and practical skills to maximize AI benefits while avoiding overreliance and superficial learning. The review provides validated methods, strategies, and tools for AI use in geometry education, concrete recommendations for teachers, and examples of practical outcomes from recent studies. These insights offer valuable guidance for educators seeking to implement AI effectively and point researchers toward emerging trends and priorities in improving geometry teaching and learning.
This research explores the potential of Brazilian folklore and literature as a source for audiovisual productions with global reach. Given the growing integration of generative artificial intelligence (AI) in academic and creative research, this study conducted a comparative analysis of four large language models (ChatGPT-4, Grok-3, DeepSeek v3, and Gemini 2.5). Methodologically, this is an applied, exploratory study with a qualitative approach, based on the collection and triangulation of data artificially generated through a standardized prompt. The results revealed that while the models consistently suggested popular figures such as Saci-Pererê and Iara, a prominent algorithmic bias toward Hollywood commercial patterns and cultural homogenization emerged. DeepSeek v3 showed greater ethnic-cultural diversity by including Indigenous and African myths , but all tools failed to access niche legends or oral traditions. It is concluded that, although AIs are valuable tools for complementing traditional folkloric research, they cannot replace it. In-depth human research remains essential to uncover the nuances and the vast range of lesser-known figures in Brazilian folklore, ensuring the authenticity and originality necessary for cultural and commercial success.
This article analyzes Brazilian academic production on Food and Nutrition Education (FNE) and teacher education based on theses and dissertations available in the Brazilian Digital Library of Theses and Dissertations, published between 2006 and 2024. The review was conducted in seven stages and used content analysis to investigate the selected studies. The results highlight the contribution of different fields of knowledge to the area, with a predominance of female researchers holding degrees in Nutrition or teacher education programs. Most studies focused on Elementary Education, particularly within the Natural Sciences curriculum, and employed participatory methodologies such as action research and ethnography. The teacher education initiatives analyzed were generally grounded in the Reference Framework for Food and Nutrition Education and the Dietary Guidelines for the Brazilian Population, fostering reflective and contextualized approaches. Thus, the present study found that teacher education in Food and Nutrition Education should promote autonomy, critical reflection, and the collective construction of knowledge, in line with contemporary educational perspectives.
Many students transitioning from secondary school to higher education face significant challenges with foundational mathematics, impacting academic success. This study investigates the one-parameter Rasch model within Item Response Theory (IRT) as a diagnostic tool to assess mathematical skills and identify weaknesses in this population. The aim of this paper is twofold: first, to conduct a literature review on standardized assessment instruments; second, to exemplify how a Rasch-based diagnostic instrument can assess mathematical proficiency. The literature review examined psychometrics and IRT, highlighting the Rasch model’s potential for analysing student responses. Microdata from the 2023 Brazilian National High School Exam (ENEM) for 160 students were used. Student responses to 45 mathematics items were analysed using the Rasch model implemented in R. The analysis included estimating item difficulty, visualising the distribution of student abilities, and comparing Rasch-generated scores with official ENEM scores derived from a three-parameter IRT model. The results suggest that the Rasch model provides valuable insights into mathematical proficiency, even with smaller sample sizes. These findings have implications for developing targeted interventions and support programmes, as identifying weaknesses allows institutions to improve support programme effectiveness and enhance student success and retention.
