Abstract
Traditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations.

Abstract
Hybrid teaching, which integrates traditional in-person learning based on students' perspectives where online learning offers a flexible approach to education, combines the benefits of technology with face-to-face interactions. Moreover, teaching and learning in a hybrid way met several challenges for both teachers and learners, including technological problems, time management, communication difficulties, and assessment complexities. This systematic review investigates six main research questions: (1) What pedagogical frameworks are used in hybrid teaching and learning? (2) How can we enhance students' engagement in hybrid teaching and learning? (3) What is the impact of technological integration on hybrid learning scenarios, both for students and teachers? (4) How do training and support measures influence the willingness and ability of university teachers to implement hybrid teaching formats? (5) How do formative assessment and feedback methods in hybrid learning environments enable teachers to effectively monitor student progress and provide tailored support? (6) How does the implementation of hybrid learning affect student learning outcomes? This study identifies the following key themes: technological integration, pedagogical innovation, faculty support, student engagement, assessment practices, and learning outcomes. Our contribution of this literature review is related to teaching and learning by showing teachers the most appropriate way to avoid the challenges encountered when teaching in a hybrid way. These include strong technology integration, innovative pedagogical strategies, strong academic development and support, active student engagement, effective assessment practices, and positive learning outcomes.

Abstract

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.

Abstract