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

Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective

This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative artificial intelligence (AI) tools during short story writing. Sixty-seven Hong Kong secondary school students created generative-AI tools using open-source language models and wrote short stories with them. The study collected and analyzed the students' generative-AI tools, short stories, and written reflections on their conditions or purposes for prompting. The research identified three main themes regarding the purposes for which students prompt generative-AI tools during short story writing: a lack of awareness of purposes, overcoming writer's block, and developing, expanding, and improving the story. The study also identified common characteristics of students' activity systems, including the sophistication of their generative-AI tools, the quality of their stories, and their school's overall academic achievement level, for their prompting of generative-AI tools for the three purposes during short story writing. The study's findings suggest that teachers should be aware of students' purposes for prompting generative-AI tools to provide tailored instructions and scaffolded guidance. The findings may also help designers provide differentiated instructions for users at various levels of story development when using a generative-AI tool.Comment: 38 pages, 9 figure

Tools Do Not Create: Human Authorship in the Use of Generative Artificial Intelligence

Artistic tools, from brushes to complex algorithms, don’t create art; human artists do. The advent of generative AI tools like Midjourney, DALL-E, and Stable Diffusion has blurred this understanding, causing observers to believe these tools are the authors of the artworks they produce, even so far as to imagine that the artworks are “created” by the AI in the copyright sense of the word. Not so. The U.S. Copyright Office recently issued guidance on the copyrightability of works produced using generative AI tools. The Office has accepted the narrative that AI tools perform the steps of authorship, conceiving of the image and rendering it into existence, and denying copyright because randomly or automatically generated works lack human authorship. This interpretation of generative AI is fundamentally flawed. Contemporary visual generative AI systems can do extraordinary things, but as of yet not autonomously and not automatically. Generative AI systems are tools—highly complex, deeply technological tools to be sure, but tools none the less. And these tools require a human author or artist—the end-user of the generative AI system—to provide the inspiration and design and often the instructions and directions on how to produce the image. It is a fallacy to view AI systems as the authors of the works they generate. The process of how an end-user of a contemporary generative AI tool creates art and how a human artist goes about the same task are very similar. An artist working with a generative AI tool is no different from an artist working with a digital or analog camera or with Photoshop or another image editing and image rendering tool

Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks.Comment: 8 pages, 4 figure

AI for the Generation and Testing of Ideas Towards an AI Supported Knowledge Development Environment

New systems employ Machine Learning to sift through large knowledge sources, creating flexible Large Language Models. These models discern context and predict sequential information in various communication forms. Generative AI, leveraging Transformers, generates textual or visual outputs mimicking human responses. It proposes one or multiple contextually feasible solutions for a user to contemplate. However, generative AI does not currently support traceability of ideas, a useful feature provided by search engines indicating origin of information. The narrative style of generative AI has gained positive reception. People learn from stories. Yet, early ChatGPT efforts had difficulty with truth, reference, calculations, and aspects like accurate maps. Current capabilities of referencing locations and linking to apps seem to be better catered by the link-centric search methods we've used for two decades. Deploying truly believable solutions extends beyond simulating contextual relevance as done by generative AI. Combining the creativity of generative AI with the provenance of internet sources in hybrid scenarios could enhance internet usage. Generative AI, viewed as drafts, stimulates thinking, offering alternative ideas for final versions or actions. Scenarios for information requests are considered. We discuss how generative AI can boost idea generation by eliminating human bias. We also describe how search can verify facts, logic, and context. The user evaluates these generated ideas for selection and usage. This paper introduces a system for knowledge workers, Generate And Search Test, enabling individuals to efficiently create solutions previously requiring top collaborations of experts.Comment: 8 pages, 21 reference

On the Benefit of Generative Foundation Models for Human Activity Recognition

In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI, including Large Language Models (LLMs) and motion synthesis models, we believe that generative AI can address this data scarcity by autonomously generating virtual IMU data from text descriptions. Beyond this, we spotlight several promising research pathways that could benefit from generative AI for the community, including the generating benchmark datasets, the development of foundational models specific to HAR, the exploration of hierarchical structures within HAR, breaking down complex activities, and applications in health sensing and activity summarization.Comment: Generative AI for Pervasive Computing (GenAI4PC) Symposium within UbiComp/ISWC 202

Generative Artificial Intelligence for Rotoscoping

Rotoscoping is a commonly used animation technique that involves taking live action filmed material and sketching on top of it to produce an animation. Rotoscoping is expensive and time consuming, but can produce smooth, eye-catching, and realistic animation. This disclosure describes rotoscoping techniques based on generative AI. Frames of a live action source video are obtained. Each frame is passed through generative AI with hyperparameters tuned for rotoscoping. A text prompt (or other suitable prompt) that specifies the type of output is provided to the generative AI. Frames generated by generative AI are re-assembled into a rotoscoped video. The techniques reduce the cost of producing animation while improving its quality and appeal

Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument

This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor analysis was used to validate the constructs. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of generative AI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption

The Library & Generative AI

A demonstration of several AI tools, including ChatGPT, ChatPDF, Consensus, and more. The focus of the session is on potential student uses of the tools and related library initiatives, so we address the limits of ChatGPT as an information source. Librarians can help students learn how to use these tools responsibly and provide leadership on campus as AI is integrated into assignments