Abstract
Text readability is vital for effective communication and learning, especially for those with lower information literacy. This research aims to assess Llama 3's ability to grade readability and compare its alignment with established metrics. For that purpose, we create a new dataset of article lead sections from English and Simple English Wikipedia, covering nine categories. The model is prompted to rate the readability of the texts on a grade-level scale, and an in-depth analysis of the results is conducted. While Llama 3 correlates strongly with most metrics, it may underestimate text grade levels.

Abstract
The underrepresentation of women in informatics academia remains a significant issue. In response, Higher Education Institutions have been implementing measures to promote gender balance. To evaluate the prevalence of current recruiting, promotion and retention practices, as well as to identify new strategies, we conducted a survey with 57 respondents, mostly from EU countries. The respondents reported various measures adopted by their Higher Education Institutions (HEI) and the percentages of female representation. Our findings show that women are typically more represented at Ph.D. level compared to senior positions, such as associate or assistant professors. Our study reveals that some institutions are already implementing numerous effective practices to achieve gender balance in the recruitment, retention, and promotion of women in informatics academia, which can serve as exemplary models. Our results provide valuable insights for institutions aiming to enhance gender balance in informatics within academia. © 2025 The Editor(s) (if applicable) and The Author(s).

Abstract
Women are seriously underrepresented in Informatics and STEM areas in general. In this chapter, we raise awareness around some of the key issues and problems that women and other minority groups may face and identify good practices to improve gender balance—and diversity in general—in academia. Our goal is to cover the entire academic career: we start with the recruitment and application evaluation, discuss retainment of female talent, including work life balance, as well as promotion, including strategies to battle the glass ceiling and sticky floor phenomena. In each section, we also provide examples of actions that have been deployed. © 2025 The Editor(s) (if applicable) and The Author(s).

Abstract

Abstract
In an era characterized by rapid proliferation of scientific publications and overwhelming volumes of digital content, researchers, students, and faculty members face significant challenges in identifying literature relevant to their academic pursuits. This saturation of information has heightened the need for advanced Recommender Systems within university libraries, tailored specifically for navigating and discovering scientific literature. This paper proposes leveraging insights from librarians' direct interactions with users to adapt existing Recommender Systems, augmented with NLP and LLMs, to better serve the specific needs of academic researchers. It should streamline the research process by delivering precise, relevant, and personalized literature recommendations, centered on a curated database of bibliographic information.

Abstract
A investigação sobre o feedback e a autorregulação da aprendizagem tem granjeado interesse com a explosão da Inteligência Artificial e os desafios que coloca à educação e, em particular, à avaliação dos estudantes. Contudo, há mais de 30 anos que se estudam esses processos para compreender como os estudantes regulam a sua própria aprendizagem ao nível motivacional, cognitivo e metacognitivo. Ao assumirem um papel proativo na geração e utilização do feedback estão a avaliar o seu próprio trabalho, o que tem implicações na forma como os professores organizam a avaliação e o apoio na aprendizagem.  Este artigo elabora sobre os desafios múltiplos que se colocam à IA na avaliação digital da aprendizagem, no que respeita ao feedback e a autorregulação bem como na investigação sobre a avaliação digital. Após a discussão desses conceitos e de modelos enquadradores bem como a sua conexão com a avaliação digital conclui-se que é crucial considerar equipas multidisciplinares na investigação com IA e minimizar ou eliminar situações que podem introduzir enviesamentos em termos de género, etnias, culturas e estatutos económico ou social.