This paper presents a randomized controlled study that evaluates the effectiveness of an Artificial Intelligence (AI)-based component designed to assess the technical quality of textbook images within the context of the Brazilian textbook program (PNLD). We adopted a parallel two-arm design with 1:1 randomization and included 76 textbook analysts. Participants completed a baseline test and a post-test after the initial assessment. We measured the primary outcome using post-test scores, while secondary outcomes evaluated analysts’ productivity and quality during textbook assessments. The current PNLD assessment process can take at least two years, involves hundreds of professionals performing manual tasks, and affects the entire educational system. One such task is assessing the technical quality of textbook images. To support this task, an AI-based system uses a convolutional neural network to solve a multiclass classification problem involving sharp, defocused-blurred, and motion-blurred images. The experiment showed that this specific AI component significantly increased productivity, while preserving quality, as the experimental group assessed substantially more images than the control group. Although the difference between pre-test and post-test results was modest, the findings indicate that the AI component can improve analysts’ ability to distinguish between different categories of image defects.

Traditional learning management systems are cloud-based and teacher-centric, limiting accessibility, flexibility, and student-centered learning. We present the Active Learning EXperience (ALEX), a decentralized learning management system aligned with Education 4.0, supporting flipped and project-based learning. ALEX leverages Web3 technologies (blockchain, InterPlanetary File System, and offline-first mechanisms) to enhance security, privacy, and access in low-connectivity environments. This study evaluated, through the case study method, ALEX's usability, performance, and pedagogical impact with nine third-year information systems students and one facilitator in an authentic project-based course at a Brazilian university, with two non-governmental organizations taking part as clients. Instruments included a custom usability questionnaire, and a pre/post-test skill assessment. Results showed significant improvements in hard and moderate improvement in soft skills, suggesting further enhancements. Future work will focus on expanding ALEX's collaborative features and scalability testing to strengthen its impact on student engagement and learning outcomes.

Resumo A Educação a distância envolve diferentes tempos e espaços nos processos pedagógicos. Dela decorrem dificuldades na vinculação de estudantes. Este artigo investiga aspectos que constituem e influenciam o isolamento e a sensação de não pertencimento dos estudantes em cursos superiores. Busca então projetar uma política pública que possa lidar com esse problema. Foi realizada revisão de literatura para o desenvolvimento de um Diagrama de Ishikawa, ferramenta utilizada para compreender o problema. Foram apontadas dificuldades tanto de vinculação com professores, pares estudantes, ambiente institucional pedagógico e técnico-administrativo, quanto de infraestrutura institucional e de condições pessoais. Foi proposto um programa que fomente cursos, oficinas e clubes com atividades regulares voltados para a vinculação social e institucional. A Teoria da Mudança, ferramenta utilizada para planejar o programa, estruturou resultados e impactos, organizando indicadores para uma avaliação.

Data analytics can support evidence-based decision-making in public policies by enabling the identification of patterns, forecasting needs, and prioritizing actions. Consequently, data-driven analysis can aid policymakers in redesigning and enhancing educational policies, such as textbook distribution for public schools. However, there is no consensus on a structured approach for descriptive analytics in this context. This study presents a descriptive approach to textual data analysis aimed at improving policy implementation and monitoring, with a focus on effort, productivity, and quality of reviews produced by textbook evaluators. Through a case study, we apply natural language processing techniques to analyze thousands of answers to rubrics during the pedagogical evaluation of a public call for literary works under the Brazilian textbook program (Programa Nacional do Livro e do Material Didático - PNLD). The PNLD is one of the most extensive textbook policies, impacting millions of students. Our findings shed light on challenges related to the effort involved and the quality of written reports in the pedagogical evaluation process. Analyzing reports, which reflect some desired and undesired behaviors of evaluators, can offer policymakers insights for making informed decisions and improving textbook programs worldwide. Our descriptive approach to textual data analysis leverages insights to enhance transparency, inform improvements, and guide policy implementation through real-time monitoring.

The quality of educational materials directly impacts students and educators, especially in underserved communities, where, for instance, textbooks often serve as the primary source for learning. This study examines how Artificial Intelligence (AI) can improve the evaluation process of textbooks distributed through the Brazilian Textbook Program. We developed an AI-powered system that relies on convolutional neural networks to classify textbook images into three categories: sharp, defocused-blurred, and motion-blurred. We experimented with frequency-domain preprocessing, including the Fourier and Haar transforms. Our findings indicate that models using Haar demonstrated greater consistency, with accuracy ranging from 69.08

Since the COVID-19 pandemic, the demand for online education has intensified, evidencing the need for educational technologies such as tutoring systems based on artificial intelligence to support student learning. As a strategy for improving educational technologies, gamification can enhance learning effectiveness by engaging students in enjoyable learning tasks. However, existing literature emphasizes the importance of tailoring gamification elements, considering factors such as the student’s gender. Neglecting such factors may lead to adverse effects stemming from stereotypes and diminishing the learning experience. Thus, we conducted a 2× 3 factorial experimental study with 122 students focusing on a gamified intelligent tutoring system for teaching logic in Brazilian higher education. Our findings revealed that gender stereotypes significantly motivated men to reject and counteract these stereotypes when perceived as a threat. In a gamified intelligent tutoring system with female stereotypes, males were motivated to alter their positions in the rankings, underscoring the impact of stereotype threat on their perception of relevance. Our results also evidenced that gamification did not impact the flow state of students and, across all scenarios, the learning performance of males consistently exceeded that of females.

In an experimental study, we observed that male-stereotyped elements, such as avatars and trophies, positively impacted men's selfefficacy using gamified tutoring systems based on artificial intelligence. Regardless of gender stereotypes, men achieved higher flow state scores than women when using these systems. Thus, we conducted a qualitative study involving Brazilian high school students to understand these effects. Our findings revealed that male students experience increased self-efficacy when game elements align with their gender, making them feel more challenged and personally connected to the system. Considering the emotional aspects of dejection, agitation, cheerfulness, and quiescence, women displayed slightly higher levels of cheerfulness in neutral and female-stereotyped environments. Moreover, student motivation varied when using a gamified platform, and there were differences in the prevention motivation within the male-stereotyped and neutral environments. These findings are relevant because they can be used to develop recommendations and guidelines for creating gamified intelligent tutoring systems that promote gender equity.

This article explores the intersection between Artificial Intelligence in Education (AIED), public policies, and the General Data Protection Regulation (GDPR) in the Brazilian context. We analyze the ethical and legal challenges faced in implementing AIED projects in public schools, focusing on protecting student data. Specifically, we present an overview of the GDPR in Brazil, highlighting the General Data Protection Law (LGPD) and public policies for learning recovery. A specific case study is discussed, showing the application of GDPR in a specific AIED project. Finally, insights, challenges, and recommendations to promote educational opportunities in the Brazilian context are discussed.

A evasão no Ensino Superior é um fenômeno preocupante. Os cursos de Licenciatura de Ciências da Natureza e Matemática apresentam indicadores alarmantes. Assim sendo, neste trabalho, teve-se como objetivo apresentar uma proposta de política pública que contribui para a diminuição da evasão nos referidos cursos. A fundamentação teórica está baseada em três eixos estruturantes: literatura sobre evasão, Teoria da Mudança (TDM) e Diagrama de Ishikawa. Em termos metodológicos, parte-se do Design Science Research (DSR), e o artefato final é um diagrama estruturado e flexível para subsidiar a gestão pública. Foram utilizadas ferramentas para a delineação do problema e para a estruturação da proposta. A partir das ferramentas metodológicas, elaborou-se um desenho para a redução da evasão no Ensino Superior. O artefato foi criado e pensado para ser empregado por gestores que possam utilizá-lo como modelo de ideia para a sua aplicação, conforme sua realidade local.

In the context of early childhood education, students need to acquire fundamental writing skills for their lifelong development. Public schools, especially in low- and middle-income countries, should monitor individual student progress to mitigate the detrimental effects of socioeconomic vulnerabilities in education. Furthermore, the volume of students often overwhelms teachers responsible for assessing handwriting texts and providing feedback. This article conducts a Systematic Literature Review (SLR) focusing on solutions for automatically evaluating students’ handwriting, discussing their performance, future research directions, and areas needing further investigation. We used a mixed-methods approach to conduct our SLR, encompassing a search across four databases (ACM Digital Library, IEEE Xplore, ScienceDirect, and SpringerLink) and employed the snowballing technique. We used the initial set of papers from the database search as the foundation for the subsequent snowballing search. Findings revealed that the studies introduced computational techniques, examined or enhanced existing methods, and developed assessment tools. These solutions predominantly employed techniques such as artificial neural networks and used available datasets comprising handwritten images, answers, or student essays. Only some studies provide low-cost solutions for automatically assessing the writing abilities of underserved public school students.

This paper presents a case study on adopting a custom design system (DS) in a research and development project, where resistance from the development team was encountered. To address this challenge, a user-centric approach was taken, treating the development team as the primary users of the design system. Through interviews with developers, their pain points were identified and addressed, resulting in a refined development process and successful design system implementation. This approach enhanced collaboration between the UX and development teams, fostering a sense of ownership among developers. The case study highlights the importance of understanding and empathizing with the development team's needs when introducing a design system. It emphasizes the value of user research within the development team and the iterative refinement of the system based on user feedback. The insights from this experience provide valuable lessons for organizations aiming to bridge the gap between UX and development, promoting the adoption and utilization of design systems.

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions.

O objetivo com este artigo é avaliar, de maneira qualitativa e quantitativa, o processo de validação e de análise de atributos, que compõe a etapa de Triagem do Programa Nacional do Livro e do Material Didático. O processo atual precisa de melhorias, considerando que é impactado pela expansão gradual da quantidade e variedade de materiais inscritos, o que gera riscos de redução da qualidade nas entregas e de aumento do tempo para conclusão das tarefas. Esse problema é discutido com base na identificação de causas documentadas em um diagrama de Ishikawa e na apresentação de evidências.

A área de Inteligência Artificial (IA) tem potencial para melhorar o ensino e a aprendizagem, por exemplo, por meio da análise de dados produzidos em ambientes educacionais. Além disso, também pode agravar a desigualdade, pois exige que alunos e instrutores tenham acesso à infraestrutura (smartphones ou computadores) exigida pela maioria dessas ferramentas para gerar e analisar dados. No entanto, o acesso a tal infraestrutura não é uma realidade para muitos estudantes ao redor do mundo. Para lançar luz sobre esse problema, este artigo investiga, por meio de um Estudo de Mapeamento Sistemático (MS), iniciativas que permitem uma análise de dados mais inclusiva usando IA na educação, especialmente em cenários com poucos recursos de conectividade. Identificamos que essas iniciativas são escassas e estão focadas na primeira fase da tarefa de análise de dados: a coleta de dados. Com base nos resultados do MS, propomos um conjunto de recomendações para os pesquisadores oferecerem direções para uma análise mais inclusiva de dados educacionais usando IA.

Este artigo apresenta resultados preliminares de um estudo que investigou as necessidades de professores e gestores na área educacional por meio da coleta de dados e análise de conteúdo, utilizando o ChatGPT como uma ferramenta de suporte. Os dados foram coletados por meio de entrevistas individuais semiestruturadas, transcritas e anonimizadas, e posteriormente inseridas no ChatGPT para análise. Os resultados revelaram insights sobre desafios enfrentados pelos profissionais da educação e destacaram o potencial do ChatGPT como uma ferramenta eficaz para aprimorar a prática da Interação Humano-Computador (IHC). Esses achados ressaltam a importância da integração entre inteligência artificial e expertise humana na pesquisa em IHC.

The Crowdsourcing model has been a paramount tool in modern public management. This research addresses the theories that gave risen to the model, as well as successful cases applied in the public sector, discussing the challenges that public Crowdsourcing projects may face, and the perspectives that point to how society’s collaboration with public affairs should be in the coming years. The reflections this research brings shall serve to improve public participation and to discuss how Information Technology can help bringing citizens and government closer together.

A implementação do Ensino de Computação na Educação Básica busca favorecer o desenvolvimento do Pensamento Computacional e estimular a construção do raciocínio lógico, crítico, criativo e resolução de problemas, habilidades essenciais para o progresso acadêmico e professional dos estudantes. No entanto, isso ainda é um desafio nas escolas brasileiras. Essa investigação tem por objetivo relatar a experiência de utilização da plataforma MIT App Inventor com 46 estudantes do 8º ano do Ensino Fundamental II. Para isso, foi realizada uma pesquisa quantitativa com abordagem descritiva na avaliação de 32 projetos desenvolvidos pelos estudantes para dispositivos móveis. Como resultados, observou-se desempenho significativos em critérios como construção de telas, nomeação de componentes e procedimentos, eventos, criação de variáveis, o uso de strings, operadores aritméticos, relacionais e expressões lógicas.

One factor that impacts the quality of Brazilian education is the quality of books and other didactic materials freely distributed throughout the country to public schools, thanks to the Brazilian National Textbook Program. The current evaluation process may take at least two years to complete, involving hundreds of people, and the final result may impact the entire educational system. One of the first activities of the process is to validate and triage the editorial quality attributes of textbooks. However, the validation and triage process needs improvement, considering the gradual expansion of the quantity and variety of materials that currently affect it. This generates risks of reduced quality and timely deliveries. This paper provides a comprehensive critical analysis of the validation and triage process based on the Policy Design Arc framework of Harvard’s Kennedy School of Government. We identified causes that affect the quality of deliveries and the time required to conclude tasks. We also propose a theory of change for digital transformation, defining strategies to address the causes of problems, outputs, outcomes, and impacts. Therefore, we have gradually implemented our theory of change in the validation and triage process.

This article discusses the opportunities and challenges of applying Artificial Intelligence in Education (AIED) unplugged in Brazil's post-pandemic context from a Public Policy that considers the national reality. In this context, an intelligent platform is being adopted to support learning recovery, focusing on developing writing skills at elementary school. To meet this objective, AI algorithms were developed with computer vision for transcribing hand-written texts and natural language processing techniques for correcting texts in Portuguese and giving feedback to students to improve their writing skills.