Abstract: 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. Keywords: Expectancy-Value Theory (EVT); Validated Instrument; Generative AI; ChatGPT Introduction The recent launch of ChatGPT (Schulman et al., 2022), an advanced language model based on the Generative Pre-trained Transformer (GPT) architecture, has generated significant interest and excitement in both academic and industry circles (Agrawal et al., 2022; Chui et al., 2022; Cotton et al., 2023; Mucharraz y Cano et al., 2023). With its impressive capabilities to generate coherent and contextually appropriate responses that closely mimic human-like communication, ChatGPT has the potential to become a game changer in students' lives, influencing various aspects of their personal, social and professional experiences. The increasing prevalence of artificial intelligence (AI) in various industries has led to an unprecedented surge in the demand for AI-related skills and knowledge. Generative AI(GenAI), a subset of AI that focuses on generating new content, has shown tremendous potential in applications across numerous domains, revolutionizing the way humans interact with technology and solve complex problems (Russell & Norvig, 2016). In the field of healthcare, AI has been employed in the development of predictive models, diagnosis, and treatment planning, leading to improved patient outcomes (Topol, 2019).

Objective: To assess the performance of the OpenAI GPT API in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review datasets and compare its performance against ground truth labelling by two independent human reviewers. Methods: We introduce a novel workflow using the OpenAI GPT API for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the GPT API with the screening criteria in natural language and a corpus of title and abstract datasets that have been filtered by a minimum of two human reviewers. We compared the performance of our model against human-reviewed papers across six review papers, screening over 24,000 titles and abstracts. Results: Our results show an accuracy of 0.91, a sensitivity of excluded papers of 0.91, and a sensitivity of included papers of 0.76. On a randomly selected subset of papers, the GPT API demonstrated the ability to provide reasoning for its decisions and corrected its initial decision upon being asked to explain its reasoning for a subset of incorrect classifications. Conclusion: The GPT API has the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, the GPT API can enhance efficiency and lead to more accurate and reliable conclusions in medical research.

The ministers also agreed to further promote smooth and trustworthy cross-border data flows -- one of Japan's key goals for the two-day G7 tech meeting -- as more countries look to tighten regulations on the flow of data. How to apply rules to the use of generative AI tools is becoming a pressing issue for governments around the world in the wake of the public debut of OpenAI's ChatGPT last November. Since then, the chatbot app has demonstrated its high capacity to handle a variety of tasks, including finding and summarizing information, drafting documents and checking programing code. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic images requires a diverse underlying generative model, hence the latest works utilize diffusion models, which were shown to surpass GANs in terms of diversity. One major drawback of diffusion models, however, is their relatively slow inference time. In this paper, we present an accelerated solution to the task of local text-driven editing of generic images, where the desired edits are confined to a user-provided mask. Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space. We first convert the LDM into a local image editor by incorporating Blended Diffusion into it. Next we propose an optimization-based solution for the inherent inability of this LDM to accurately reconstruct images. Finally, we address the scenario of performing local edits using thin masks. We evaluate our method against the available baselines both qualitatively and quantitatively and demonstrate that in addition to being faster, our method achieves better precision than the baselines while mitigating some of their artifacts.

If you've ever published a blog, or posted something to Reddit, or shared content anywhere else on the open web, it's very likely you have played a part in creating the latest generation of artificial intelligence. Google's Bard chatbot, OpenAI's ChatGPT, Microsoft's OpenAI-powered version of Bing, and similar tools from the many startups now incorporating these and other AI language models--none of these clever automated writers could exist without the enormous body of text freely available on the web.

LONDON/STOCKHOLM – As recently as February, generative AI did not feature prominently in EU lawmakers' plans for regulating generative artificial intelligence technologies such as ChatGPT. The bloc's 108-page proposal for the AI Act, published two years earlier, included only one mention of the word "chatbot." References to AI-generated content largely referred to deepfakes: images or audio designed to impersonate human beings. By mid-April, however, members of European Parliament (MEPs) were racing to update those rules to catch up with an explosion of interest in generative AI, which has provoked awe and anxiety since OpenAI unveiled ChatGPT six months ago. This could be due to a conflict with your ad-blocking or security software.

This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly. Keywords: AI Policy Framework; Artificial Intelligence; ChatGPT; Ethics; Assessment 1. Introduction In recent months, there has been a growing concern in the academic settings about the use of text generative artificial intelligence (AI), such as ChatGPT, Bing and the latest, Co-Pilot integrated within the Microsoft Office suite. One of the main concerns is that students may use generative AI tools to cheat or plagiarise their written assignments and exams. In fact, a recent survey of university students found that nearly one in three students had used a form of AI, such as essay-generating software, to complete their coursework (Intelligent.com, About one-third of college students surveyed (sample size 1000) in the US have utilized the AI chatbot such as ChatGPT to complete written homework assignments, with 60% using the programme on more than half of their assignments. ChatGPT types of generative AI tools is capable of imitating human writing, with some students using it to cheat. The study found that 75% of students believe that using the programme for cheating is wrong but still do it, and nearly 30% believe their professors are unaware of their use of the tool. The study also noted that some professors are considering whether to include ChatGPT in their lessons or join calls to ban it, with 46% of students saying their professors or institutions have banned the tool for homework. This has led to calls for stricter regulations and penalties for academic misconduct involving AI.

This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs' 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students' perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students' perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.

More media outlets are using AI to write articles, if not as aggressively as others. VentureBeat editorial director Michale Nuñez tells Bloomberg his publication is using Microsoft's Bing Chat to help edit and write stories. Reporters are encouraged to slip AI-written "sentences and fragments" into articles so long as they're accurate and independently verifiable. The OpenAI-powered tech is akin to having "another person on the team," Nuñez says. VentureBeat doesn't disclose the use of AI content provided it's limited and authentic, but also doesn't intend to create whole articles using the technology. Word surfaced in January that CNET had been using AI to produce entire financial explainer articles since November.

OpenAI says ChatGPT is once again available in Italy after it addressed a series of conditions set out by regulators. The Garante data protection authority wanted OpenAI to resolve several issues by the end of this month in order to lift a temporary ban on the chatbot. "ChatGPT is available again to our users in Italy," OpenAI told the Associated Press in a statement. "We are excited to welcome them back, and we remain dedicated to protecting their privacy." Italian regulators blocked ChatGPT in March over concerns that the AI's training methods and chatbot violated the European Union's General Data Protection Regulation (GDPR).