Software systems are critical resources for our daily lives. Due to that, computer science students need to learn software engineering knowledge and skills to develop software systems with the expected quality, under budget, and on schedule. The size and complexity of software systems and the short time to market make it necessary to have various software developers working as a team. In this scenario, the success of a project depends on coordination, communication, and collaboration. The evolution of network, communication, and computer technologies made it possible for people from different countries to work on the same project. Therefore, software engineers must collaborate remotely with others in Global Software Engineering (GSE) projects. Hence, students must also learn how to collaborate with people from different countries, with specific expectations, and different cultures and languages. This study lists the main GSE challenges the students must be ready to deal with, how researchers have addressed GSE, and how GSE education has been approached in the last five years. Researchers focused on teaching approaches, what students experienced during GSE courses, and the importance of GSE in industry. Students had the opportunity to face unsolved GSE challenges, and GSE courses still need to be improved.

The learning game “Present of the Future” has shown great potential as an effective tool for promoting futures literacy and speculative design in various educational and professional contexts. Through interactive and collaborative scenarios, participants are encouraged to explore future possibilities and develop creative solutions to contemporary challenges. Despite its promising impact, the game still lacks a structured and systematic process for evaluating the user experience on three levels: the immediate reaction to the game experience, the retention of learning in the short and long term, and collaboration in creating future artifacts. We propose a detailed experiment and an evaluative questionnaire composed of open and closed questions to fill this gap and provide a foundation for critically analyzing these dimensions. Although the experiment has not yet been conducted, the research establishes a robust theoretical and methodological framework that will allow, in future stages, the collection and analysis of quantitative and qualitative data to complement and enrich this study. Thus, we expect that the future application of these practices will contribute to a deeper understanding of the pedagogical and collaborative effects of the game.

With the advancement of Artificial Intelligence (AI) technologies, the world has been increasingly reshaped across multiple domains - healthcare, finance, education, and creative fields. This expansion transforms individual tasks and facilitates complex collaborations where humans and machines work to- gether in unprecedented ways. Through machine learning and, more specifically, large language models (LLMs), AI now plays a crucial role in enhancing productivity. Recent technological advancements have significantly boosted the text generation capabilities of LLMs, enabling them to simulate personas that can represent groups or specific well-known individuals. By simulating expert perspectives, LLMs provide insights to guide human decision-making across various collaborative frameworks in Computer-Supported Cooperative Work (CSCW). Personas, as simulated identities, have proven helpful in group brainstorming sessions, consensus-building exercises, and virtual advisory boards, which help represent diverse viewpoints and facilitate group decisions. Among these CSCW methods, the Delphi method is a well-established approach for reaching consensus among experts through iterative feedback and structured discussions. This work introduces a framework to manage and evaluate LLM-generated personas simulating expert input in Delphi studies. Our contributions include a framework designed for evaluating LLM personas in Delphi studies using a text similarity validation technique comparing real and simulated expert opinions and an evaluation of a Delphi about Brazilian higher education, assessing the LLMs' reliability in real-world scenarios. Findings show LLMs can effectively replicate expert perspectives, enhancing AI integration in specialized decision-making.

In light of the increasing recognition of environmental and social challenges, technology-driven corporations often face significant barriers in aligning their innovation-driven goals with sustainable practices. Despite their capacity for rapid technological advancements, these companies struggle to integrate sustainability objectives into their core strategies due to competing priorities and a lack of cohesive frameworks. This study explores Knowledge Management’s (KM) capacity to facilitate and implement sustainable practices. We use the Rapid Review methodology to systematically review the literature and achieve this objective. A total of 35 articles were analyzed, examining factors related to organizational knowledge, the adoption of KM within technology-driven companies, and effective sustainable practices. The results highlight the importance of KM in facilitating and implementing sustainable practices, showing that technology-driven companies that invest in organizing their knowledge can improve their effectiveness in implementing sustainable initiatives, increase their resilience and competitiveness, and strengthen trust with stakeholders.

In recent years, artificial intelligence (AI) has advanced rapidly, transforming various sectors of society and delivering value to governments, businesses, and end users. However, these advances have also increased computational costs and training times, resulting in significant energy consumption by AI systems. This situation highlights the urgent need for regulatory discussions and proposals that guide AI development and innovation toward energy sustainability. This work reviews, organizes, and categorizes recent regulatory policies for AI energy sustainability into four dimensions for trend analysis. The results show that most identified policies concentrate on transparency and energy efficiency, indicating a higher maturity of these measures within regulatory discussions. This work seeks to raise awareness among stakeholders involved with AI and sustainability, encouraging reflections on energy consumption and efficiency to build a more sustainable future.

Traditional methods for Technology Foresight often struggle to keep up with the fast changes in technology. This work looks at how crowdsourcing can improve Technology Foresight by using collective intelligence to spot trends and tackle complex problems. The goal of this research is to overcome the limits of traditional approaches by using online platforms and new technologies like big data and machine learning to make innovation more accessible. We examine how to apply crowdsourcing effectively in Technology Foresight, discussing its benefits, challenges, and real-world uses. We used a Rapid Review (RR) approach, combined with Large Language Models (LLMs), to refine how we select and analyze research. This method helped us find important insights and categorize applications in different areas. The findings show that crowdsourcing can speed up technology forecasting by bringing in diverse viewpoints and encouraging collaboration across fields. Examples include its use in urban planning, disaster management, and open innovation platforms. Furthermore, combining crowdsourcing with advanced technologies enhances its effectiveness in foresight processes. This work offers practical advice for organizations that want to use crowdsourcing in their innovation strategies and suggests future ways to integrate new technologies into Technology Foresight.