Unraveling generative AI in BBC News: application, impact, literacy and governance

Abstract Purpose This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies. Design/methodology/approach This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI. Findings By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI. Originality/value Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.Abstract Purpose This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies. Design/methodology/approach This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI. Findings By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI. Originality/value Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm

Generative AI

Open Access funding enabled and organized by Projekt DEAL.Ludwig-Maximilians-Universität München (1024

Peningkatan kompetensi pembelajaran dengan generative AI guru SMKN 1 Rembang Purbalingga: to be or not to be future of learning

Abstrak Saat ini muncul teknologi generative AI yang dapat dimanfaatkan dalam pembelajaran. Pengabdian ini bertujuan untuk mengetahui peningkatan pengetahuan generative AI guru SMK N 1 Rembang Purbalingga, peningkatan keterampilan generative AI guru SMK N 1 Rembang Purbalingga pada tanggal 24 Juni 2024, Kebermanfaatan pengabdian masyarakat, dan kepuasan mitra pengabdian. Metode yang digunakan adalah pretest, ceramah, tanya jawab, demonstrasi, penugasan, dan posttest. Jenis tes yang digunakan adalah pilihan ganda sedangkan data kebermanfaatan dan kepuasan mitra pengabdian didapatkan menggunakan instrumen non tes, kuesioner. Hasil pengabdian ini menunjukan bahwa adanya peningkatan generative AI guru SMK N 1 Rembang Purbalingga dari median 5 menjadi median 9. Keterampilan guru juga meningkat dimana guru dapat membuat akun, menginstall generative AI di chrome extension dan dapat mengoperasikan generative AI. Pengabdian selanjutnya sebaiknya dilakukan pelatihan lebih lanjut tentang teknologi AI lainnya dapat diadakan untuk terus memperbarui pengetahuan dan keterampilan guru, sehingga mereka dapat mengikuti perkembangan teknologi yang terus berkembang. Kata kunci: generative AI; guru; keterampilan; pengetahuan Abstract Currently, generative AI technologies are emerging which can be used in learning. This service aims to determine the increase in generative AI knowledge of SMK N 1 Rembang Purbalingga teachers, the increase in generative AI skills of SMK N 1 Rembang Purbalingga teachers, the usefulness of community service, and the satisfaction of service partners. The methods used are pretest, lecture, question and answer, demonstration, assignment, and posttest. The type of test used is multiple choice, while data on the usefulness and satisfaction of service partners is obtained using non-test instruments, questionnaires. The results of this service show that there has been an increase in the generative AI of SMK N 1 Rembang Purbalingga teachers from a median of 5 to a median of 9. Teacher skills have also increased where teachers can create accounts, install generative AI in the chrome extension and can operate generative AI. Further service should include further training on other AI technologies to continuously update teachers' knowledge and skills, so that they can keep up with developments in technology that continue to develop. Keywords: generative AI; knowledge; teacher; skill

Generative AI utilization: How developers utilize Generative AI

After the deployment of generative AI, many organizations have been quick to find a way to implement it into their work processes in the hope that it will increase efficiency. There are however challenges that come with generative AI, and it is important that organizations attempting to adapt generative AI are aware of the challenges and the opportunities, to prevent common issues and increase efficiency. This study attempts to answer the research problem “how do software developers utilize generative AI?” to establish the most effective use cases, how to ensure quality and how users get motivated into using it. This is a qualitative study where we have conducted semi-structured interviews with employees in consultant companies with technical knowledge, like developers, because they have a more natural curiosity towards new technology and can in turn provide better insights that other non-technical professions can provide. After analyzing the data from the interviews, we were able to identify some key factors that managers should consider when implementing generative AI themselves. We have identified how important it is with knowledge work, as intrinsic motivation is the main driving factor for generative AI utilization, and the more an employee knows about the possibilities of generative AI, the more they are willing to use it. We identified two categories of use modes, explore, and accelerate, which depending on the use case, has different requirements for accuracy. Accelerate mode is when users use generative AI to complete simple, but time-consuming tasks. In these cases, it is very important that there are security measures in place to ensure that the work being completed is correct and according to company standards. In explore mode, generative AI is used for inspiration and discussions, and nothing it creates is used directly, and therefore the accuracy is not as important. Too many rules and regulations with generative AI decreases employee’s motivation and increases the time spent on the task. Because of legal and privacy issues, developers can share very little of the code base with generative AI, and without that contextual understanding, generative AI provides subpar answers. A solution to this is integrated large language models that are trained on company data and do not share this data. Solutions like these have a much better contextual understanding of the project and remove the ability for the users to commit mistakes. The findings in this thesis suggest more research into specific types of large language models, as well as studying these principles on other professions, to establish how they affect employees from less technical professions

Generative AI utilization: How developers utilize Generative AI

After the deployment of generative AI, many organizations have been quick to find a way to implement it into their work processes in the hope that it will increase efficiency. There are however challenges that come with generative AI, and it is important that organizations attempting to adapt generative AI are aware of the challenges and the opportunities, to prevent common issues and increase efficiency. This study attempts to answer the research problem “how do software developers utilize generative AI?” to establish the most effective use cases, how to ensure quality and how users get motivated into using it. This is a qualitative study where we have conducted semi-structured interviews with employees in consultant companies with technical knowledge, like developers, because they have a more natural curiosity towards new technology and can in turn provide better insights that other non-technical professions can provide. After analyzing the data from the interviews, we were able to identify some key factors that managers should consider when implementing generative AI themselves. We have identified how important it is with knowledge work, as intrinsic motivation is the main driving factor for generative AI utilization, and the more an employee knows about the possibilities of generative AI, the more they are willing to use it. We identified two categories of use modes, explore, and accelerate, which depending on the use case, has different requirements for accuracy. Accelerate mode is when users use generative AI to complete simple, but time-consuming tasks. In these cases, it is very important that there are security measures in place to ensure that the work being completed is correct and according to company standards. In explore mode, generative AI is used for inspiration and discussions, and nothing it creates is used directly, and therefore the accuracy is not as important. Too many rules and regulations with generative AI decreases employee’s motivation and increases the time spent on the task. Because of legal and privacy issues, developers can share very little of the code base with generative AI, and without that contextual understanding, generative AI provides subpar answers. A solution to this is integrated large language models that are trained on company data and do not share this data. Solutions like these have a much better contextual understanding of the project and remove the ability for the users to commit mistakes. The findings in this thesis suggest more research into specific types of large language models, as well as studying these principles on other professions, to establish how they affect employees from less technical professions

AI Plus Other Technologies? The Impact of ChatGPT and Creativity Support Systems on Individual Creativity

The emergence of generative artificial intelligence (AI) has triggered a massive technological surge. Software and systems increasingly incorporate generative AI as a fundamental component of their applications. Unfortunately, there is a lack of awareness of the interaction between generative AI and other tools and their consequences and causes. In this research, we explored the impact of the concurrent use of generative AI and creativity support systems (CSS) on users’ creativity. In addition, by categorizing the stimuli provided by the CSS into high and low relatedness, we further investigated the effects of using generative AI with various CSS. By focusing on the interaction effect between generative AI and CSS, this research not only sheds light on the broader implications of generative AI but also serves as a guiding framework for the evolution of future CSS and furthering the enhancement of individual creativity

Design Principles for Generative AI Applications

Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative AI applications that address unique characteristics of generative AI UX and offer new interpretations and extensions of known issues in the design of AI applications. Each principle is coupled with a set of design strategies for implementing that principle via UX capabilities or through the design process. The principles and strategies were developed through an iterative process involving literature review, feedback from design practitioners, validation against real-world generative AI applications, and incorporation into the design process of two generative AI applications. We anticipate the principles to usefully inform the design of generative AI applications by driving actionable design recommendations.Comment: 34 pages, 4 figures. To be published in CHI 202

Revolutionary Applications of Generative AI in Higher Education Institutes (HEIs) and its Implications

In recent decades, there has been a notable transformation in educational procedures due to technological breakthroughs, particularly in artificial intelligence (AI). In recent times, there has been a noteworthy advancement and acceptance of generative artificial intelligence (AI) models, specifically exemplified by the emergence of Generative Pre-trained Transformers (GPT). Within the overarching category of Generative AI, various AI tools and technologies facilitate the production of computer-generated text, images, and other forms of digitized media. This paper comprehensively analyzes the concepts and implications of the discourse surrounding Generative AI. By adopting a position that advocates for the acceptance rather than the opposition of Generative AI, this study offers valuable insights for educators and researchers in higher education learners. The findings presented here contribute significantly to understanding Generative AI as a transformative force in reforming education. This study investigates the potential consequences of generative artificial intelligence (AI) technology on higher education, specifically focusing on the significant transformative shifts that may occur within higher education institutions (HEIs). This article examines three primary objectives: The benefits and use cases of generative AI in higher education institutions (HEIs) The influence or disruption of generative AI in the education sectors The developing obstacles and opportunities associated with its implementation The authors contribute to the extant research study by presenting a comprehensive model elucidating the manifestation of generative artificial intelligence (AI) in higher education and its impacts on libraries. Additionally, they offer insightful recommendations for effectively managing this phenomenon. The paper\u27s concluding discussion delves into the prospective ramifications of generative artificial intelligence (AI) within higher education institutions (HEIs), as well as the obstacles and risks that AI presents, particularly in the context of higher education

From Experiment to Design: Weaving Generative AI into the Fabric of Courses

In 2023 instructors often responded to generative AI in the form of activities that aimed to develop AI skills/literacies and to mitigate academic dishonesty by engaging with generative AI “out in the open.” These activities suggested a larger question about how curricula and disciplines should integrate generative AI holistically, moving from curiosity/experiment to strategy/design. This session explores learning-centered design for integrating generative AI at the course level and beyond. As an example, we\u27ll examine how an online graduate/professional writing course wove generative AI \u27into the fabric\u27 of its curricular design and pedagogical dynamics