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As the diversity and complexity of Artificial Intelligence (AI) systems increase, there is a growing need for advanced knowledge representation methods to enhance decision-making capabilities. Existing research indicates a gap between AI and Knowledge Management (KM), emphasizing the necessity of coordinating learning and knowledge creation processes between humans and machines. Despite the widespread use of generative AI, as seen through the growing popularity of conversational AI tools like chatbots powered by Large Language Models in recent years, the absence of a theoretical framework for effectively managing the knowledge they generate could mean missing out on significant opportunities. This work seeks to bridge this gap between KM and IA through an integrated framework that aims to apply KM to support IA chatbot applications, adapted from the Internet of Everything Integrated Knowledge Management Model (IoE IKM Model). The IoE IKM Model's original goal is to support knowledge creation in IoE applications, but here, we show how it can be adapted to bring KM to the context of AI. We accomplish this by explaining the development process of the IoE IKM Model, identifying shared aspects between IoE and AI general applications, and adapting necessary elements to establish our integrated KM framework tailored for supporting AI chatbot applications. The resulting framework is then discussed, and examples of how it can be applied to enhance human interaction with a chatbot, namely Open AI's ChatGPT. Research has been conducted to demonstrate the advantages of applying AI in KM. However, we aim to take a different approach by showing how KM can contribute to AI applications. We expect this work to be helpful for those whose professional activities may involve the usage of AI systems by providing them with the necessary tools to manage the knowledge generated by these same AI systems and by offering a Knowledge Manager's perspective on how to boost human-machine interaction. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed a growing increase in applications involving some form of Artificial Intelligence (AI). Conversational AI has gained considerable prominence among the various types of AI applications. Classified as a type of generative AI, chatbot applications such asOpenAI's ChatGPT or Google's Bard are now utilized by multiple authors as tools. Despite its many advantages, the indiscriminate use of this type of generative AI in texts can raise ethical questions about who owns authorship of a particular work, as a human author may have contributed very little to the production of certain content. The concern about authorship is paramount in producing scientific publications, such as articles, dissertations, or theses. To avoid such questioning, some authors may develop strategies to conceal the use of generative AI in their productions. This work aims to introduce the concept of Text Laundering (TL), which we name as such due to its similarity to the practice of money laundering -- applying various consecutive alterations to AI-generated t ext t o m ake i ts o rigin indeterminate. This work will also develop the concept by examining examples of similar practices in the literature employing an ad hoc search. A method for applying TL to mask the origin of a text will be proposed, and the results obtained in a simple test will be presented to determine the success of our strategy. We hope this work can contribute to discussions on preventing the fraudulent use of generative AI. Discussing TL methods does not imply endorsing them but rather exploring potential mechanisms that can be maliciously utilized to gain advantages through generative AI, identifying them, and preventing their use. AI-generated texts can harm knowledge management systems, and we highlight the need to include AI-verification tools in such systems. Such tools can enhance the trustworthiness of information in knowledge management systems, improving knowledge externalization and sharing processes. Finally, we expect to promote future research on TL techniques and the proposition of new strategies to combat fraudulent practices in scientific research. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution is causing considerable changes to the world of work. The interaction between technology and work, which takes many forms such as digitalization, automation, and augmentation, is happening quickly and broadly, impacting economic sectors left almost untouched by previous industrial revolutions. In this scenario, Higher Education Institutions (HEIs) must be able to foresee changes to prepare future professionals to match the needs of this new digital age. The professionals that are being prepared today need to learn a new set of skills related to emerging and disruptive technologies, such as Artificial Intelligence, the Internet of Things, and Big Data. In this paper, we propose a Knowledge Management model to help manage the HEIs' teaching staff knowledge about the future of work, specifically, the expected skills and competencies to be highly demanded from professionals in the future. Therefore, we performed a brief review of related work about Knowledge Management in the context of HEIs, Management of Future-oriented Knowledge, and the application of the Delphi method to studies concerning the Future of Work. Considering this previous work, we propose a Knowledge Management model that combines the European Foundation for Quality Management (EFQM) Excellence Model framework with a Delphi process that is used during the Knowledge Generation step of the Knowledge Management process. The proposed model considers that professors are experts in their areas of concentration and, as such, are capable of helping their HEIs with their knowledge that can be used to improve the courses' curricula. The model also considers that HEIs can help professors make this knowledge explicit, then store, transfer, and apply it. We provide detailed information about how to apply the model, how to deal with potential application problems and the model limitations. The proposed Knowledge Management model can help HEIs to keep up with the trends of demands of the labor market. [ABSTRACT FROM AUTHOR]