This work demonstrates the development of a prototype system that could improve urban and digital accessibility for People with Disability - PwD. The research involves using an academic questionnaire to enable a wider perspective of the central subject and assessing the effectiveness of the proposed solution. The main goal is to help social integration and make the future solution implementable for smart cities through an experiment in an academic campus. The dissertation is related to technological and social issues that might be used as a resource by researchers, public or private bodies interested in more profound studies. The document recognizes the lack of studies on this subject, especially in Brazil because there are few available articles. The prototype system is designed to tackle the issues faced by persons with disability in urban settings through platforms, applications and mobile phones as it proposes a comprehensive set of solutions when addressing accessibility for PwDs. In turn, this research fosters knowledge advancements in the field of urban accessibility and provides vital recommendations for developing inclusive technologies and policies.

This research explores the potential of gamified educational applications as motivational tools for distance learning students. The study draws from the research conducted by Fernando da Silva Pontes in his dissertation, which examines the impact of gamification on student motivation and engagement in the context of distance education. The paper provides a comprehensive overview of the theoretical foundations of gamification and its application in educational settings, synthesizing existing research on the impact of gamified educational applications on student motivation, engagement, and learning outcomes. The findings highlight the significant contribution of gamification in fostering a collaborative and interactive learning environment for distance education students. The study underscores the transformative potential of gamification in addressing the challenges of student motivation and engagement in distance education, paving the way for future advancements in educational practices and research. The paper provides insights into the potential of gamification in enhancing the learning experience for distance education students, contributing to the understanding of the effectiveness of gamified educational applications in motivating and engaging students in the context of distance learning.

The rapid growing application of language models (LLMs) in education offers exciting prospects for personalized learning and interactive experiences. However, a critical challenge emerges - the risk of "hallucinations," where LLMs generate factually incorrect or misleading information. This paper proposes Comparative and Cross-Verification Prompting (CCVP), a novel technique specifically designed to mitigate hallucinations in educational LLMs. CCVP leverages the strengths of multiple LLMs, a Principal Language Model (PLM) and Auxiliary Language Models (ALMs), to verify the accuracy and educational relevance of the PLM's response to a prompt. Through a series of prompts and assessments, CCVP harnesses the diverse perspectives of various LLMs and incorporates human expertise for intricate cases. This method addresses the limitations of relying on a single model and fosters critical thinking skills in learners within the educational context. We detail the CCVP approach with examples specifically applicable to educational settings, such as geography. We also discuss its strengths and limitations, including computational cost, data reliance, and ethical considerations. We highlight its potential applications in educational disciplines, including fact-checking content, detecting bias, and promoting responsible LLM use. CCVP presents a promising avenue for ensuring the accuracy and trustworthiness of LLM-generated educational content. Further research and development will refine its scalability, address potential biases, and solidify its position as a vital tool for harnessing the power of LLMs while fostering responsible knowledge dissemination in education.

This paper presents an approach for the application of clustering algorithms to uncover computer programming learning profiles by using evidence extracted from source code metrics. A system for automatic assessment of programming activities featuring capture of source code metrics was developed, in order to build a dataset containing metrics extracted from programs developed by beginners in a computing course. The dataset was submitted to three clustering algorithms. The results were promising when clustering students according to these indicators using the K-means algorithm.