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.

Future-related studies are key in identifying and predicting emerging technologies, fostering innovation, and driving scientific progress. Games have increasingly served as a tool to engage researchers in Technology Forecasting and Foresight activities, presenting a gamified approach to gather, analyze, and synthesize information to allow future foresight. In this work, we use the Rapid Review methodology to analyze the scientific literature and explore the use of games as tools for Technology Forecasting and Foresight. We analyzed and categorized the articles by theme and type of study, identifying the areas where games can be most beneficial to guide professionals performing Technology Forecasting and Foresight, helping companies and governments better understand the future. The empirical evidence found provides a comprehensive understanding of how games can enhance foresight activities and contribute to the success of Technology Forecasting and Foresight initiatives. Our analysis also provides insights for future research about gamified strategies in Foresight.

Recent Artificial Intelligence advancements raise concerns about the future of work, particularly technological unemployment. Studies show automation's impact, but tools like ChatGPT disrupt even traditionally secure professions like programming. In this study, we reevaluate AI's effects using a method to assess the impact of Generative AI technologies on occupations to understand the potential effects of Generative AI systems on software development work. Valuable insights were obtained by gathering the view of a group of workers, primarily composed of developers who are starting their careers, regarding the impact of these technologies on the tasks they perform to provide a comprehensive understanding of the implications of Generative AI for software development. Results show that all programming tasks performed by these workers would experience some impact by Generative AI -65% of the tasks being considerably impacted, 12% moderately impacted, and 18% minimally impacted. This analysis highlights the substantial influence of Generative AI technologies on software development, mainly affecting those in the early stages of their career. The results of this work contribute to the academic community with valuable information. Policymakers can also use this information, as this work provides a comprehensive view of the impacts of Generative AI on software developers, considering their direct impact on job tasks.

Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.

Shopping tools are widely used by millions of users to find the best deals in different marketplaces worldwide. In this work, we introduce the concept of crowdsourced deals, which refers to deals found and shared by a crowd of potential consumers. We analyze various crowdsourced deal tools and highlight their features. By formalizing collaboration in certain types of bargain-hunting tools, we can develop new techniques to improve the user experience and attract more contributors to maintain an active and stable community. Understanding the concept of crowdsourced deals can also benefit organizations looking to increase their sales and analyze their online reputation. Bargain-hunting tools provide a form of marketing that we can refer to as online word-of-mouth advertising, which is crucial in today’s consumer society, similar to well-known word-of-mouth advertising.

In the process of creating a Technological Roadmap, a large amount of data must be analyzed, and future events may be spread across several document databases, making it harder to researchers retrieve all future events described in these publications in a timely fashion. Technology Roadmapping is a time-consuming and error-prone method that usually requires a group of experts to be properly executed. This work presents NERMAP, a collaborative system capable of semiautomating the Technology Roadmapping process, through the Named Entity Recognition method. The evaluation showed that NERMAP is capable of retrieving up to 83% of future events in documents. Furthermore, the system was also able to identify some future events that our validation team missed. By using the system, researchers can perform the analysis in considerably less time, thus reducing costs. Therefore, the proposed system enables a small group of researchers to analyze a large number of documents in a short time, streamlining their ability to identify, process, and analyze information in the form of roadmaps.

In recent decades, technological advances have exponentially accelerated the search for digital transformation. The use of technologies based on Artificial Intelligence (AI) is essential to achieve these goals. In this work, we investigate the future trends in the field. We conducted a systematic review and analyzed their content with a methodology based on Future-Oriented Technology Analysis (FTA) techniques. We have identified 30 future events related to digital transformation and AI and their respective feasible years. Our findings are categorized into industry fields: business process management, construction industry, digital economy, human resources, healthcare, industry, social innovation, tourism, and education. Our work allows decision-makers to draw up strategies, map the paths to be taken, and better prepare for their future needs, using AI as a tool for value aggregation.

Organizations often use one or more knowledge-based approaches in order to structure and promote their Intellectual Capital (IC), ensuring flexibility and resilience in an environment of constant change, competitiveness and innovation capabilities. In this context, Knowledge Management (KM) and Future-oriented Technology Analysis (FTA) are two research topics that have been used by organizations. However, organizations usually do not use both KM and FTA. Moreover, when they use both, they apply them as separate initiatives. Therefore, applying KM and FTA together produce many challenges. In this direction, this work presents a framework that uses FTA to improve KM processes through the use of the IC, as well the use of KM processes to improve FTA Strategies. Moreover, the proposed framework forms a virtuous circle, in which KM processes, Intellectual Capital and FTA strategies interact producing a mutual benefit.

The practice of Knowledge Management within the scope of the Brazilian Federal Government advocates achieving the strategic objectives of organizations, aiming the provision of better services to society. Such improvements can be achieved by acquiring new knowledge or transforming existing knowledge, that although belong to the organization could be equally useful to other organizations. However, internal rules of organizational confidentiality, the lack of motivating and integrative policies make these organizations closed in the aspect of the transfer of acquired knowledge to other organizations, leading to efforts and redundant spending to acquire new knowledge. In this paper, we propose the creation of a Knowledge Management service bus for Federal Government organizations, aiming the transfer of knowledge among their organizations thought software interoperability. Therefore, our proposal enables, beyond the software interoperability among the participating organizations, the creation of Knowledge Management Systems' standards.

The content creation process on the Internet changed with the emergence of Social Networks, with the volume of information overgrowing. Unfortunately, social networks have shown a high potential for disseminating hoaxes and, more recently, fake news. This fact encouraged the emergence of fact-checking organizations, usually carried out by journalism organizations. However, they have limitations to meet the enormous demand arising from the immense volume of fake news currently on the Internet. Thus, this study proposes a crowd-based solution as a way to meet this high demand for fact-checking. The proposed fact-checking process uses crowdsourcing to performing the filtering, analysis and classification of news. In this way, we can use a checking mechanism that may scale proportionally to the creation of fake news.

In the long run, technologies positively impacted work and, in general, brought benefits to society as a whole. However, history also shows that the first generations coexisting with new technologies had their jobs profoundly impacted both in terms of quantity and quality. As we enter the 4thIndustrial Revolution, computing has been one of the key technologies that impact work and its advancements must be accompanied with special care. Computation not only impacts work but is also a powerful tool to support the construction of knowledge and proposition of actions on the technologies that disrupt work. This paper proposes the development of a computer system that allows workers themselves to evaluate the technologies that will impact work in the future. We present each stage of the application of the Design Science Research methodology for developing the system. The artificial evaluation of the resulting prototype shows its utility and opportunities for improvement.

Knowing the future of work is something that we all wish to do in order to be prepared for it. Unfortunately, humans cannot predict precisely the future of anything yet. Nevertheless, in this paper we try to get as close as we can to it by looking at some trends of the future of work. This challenge is undertaken by reviewing the scientific literature on the subject. We elaborate on some insights for CSCW based on the trends of the future of work and society.