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.

Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Gemini, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine–machine collaboration models to streamline and accelerate Delphi execution time by leveraging the extensive knowledge of LLMs. We then applied one of these models—the Iconic Minds Delphi—to run Delphi questionnaires focused on the future of work and higher education in Brazil. Therefore, we prompted ChatGPT to assume the role of well-known public figures from various knowledge areas. To validate the effectiveness of this approach, we asked one of the emulated experts to evaluate his responses. Although this individual validation was not sufficient to generalize the approach’s effectiveness, it revealed an 85% agreement rate, suggesting a promising alignment between the emulated persona and the real expert’s opinions. Our work contributes to leveraging Artificial Intelligence (AI) in Futures Research, emphasizing LLMs’ potential as collaborators in shaping future visions while discussing their limitations. In conclusion, our research demonstrates the synergy between Delphi and LLMs, providing a glimpse into a new method for exploring central themes, such as the future of work and higher education.

Knowledge Management (KM) plays a fundamental role in technology-based organizations by enabling them to effectively manage and utilize their knowledge resources in dynamic and fast-evolving environments. While KM has been extensively explored across various industries, its application within technology-driven sectors requires further examination due to the unique challenges these organizations face, such as rapid technological advancements, knowledge silos, information overload, and high employee turnover. Knowledge is a key driver of innovation, decision-making, and competitive advantage, so a deeper understanding of how KM operates in these contexts is essential. This study conducts a rapid literature review to investigate the benefits, challenges, and evaluation metrics associated with Knowledge Management in technology-based organizations. The findings reveal that well-implemented KM strategies enhance innovation capacity, operational efficiency, and more effective decision-making processes. However, despite its advantages, Knowledge Management implementation is often hindered by cultural barriers, reluctance to share knowledge, and difficulties aligning KM initiatives with overarching strategic goals. Overcoming these obstacles requires targeted investments in digital infrastructure, leadership support, and promoting a strong knowledge-sharing culture. We also identify a set of potential metrics for assessing KM effectiveness, including operational efficiency indicators, employee and customer satisfaction levels, patent data, knowledge stock, and structural capital. While these metrics build upon insights from the literature, further research is necessary to validate their applicability and reliability as measures of KM success in rapidly changing technology environments. Despite these challenges, Knowledge Management presents transformative potential for organizations seeking to leverage knowledge as a strategic asset, driving continuous innovation, improving organizational performance, and sustaining a competitive edge in an increasingly knowledge-intensive economy. By providing a structured understanding of KM’s role, challenges, and possible success metrics, this study offers valuable insights for researchers and practitioners, guiding them in refining Knowledge Management strategies to enhance adaptability, collaboration, and long-term resilience in technology-driven industries.

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.