To lower the barrier of telling appealing and effective stories, researchers have spent considerable efforts to build AI-powered tools to facilitate their creation and communication with different strategies of human-AI collaboration [29]. In these tools, AI collaborators are often powered by heuristic-based methods [57], traditional machine learning models [13], or smaller-scale deep learning models [36]. Compared to the previous techniques for AI collaborators, the recently emerging large-scale generative AI models, including the text-to-image models [62] and large language models (LLMs) [69], can achieve better performance on various data storytelling-related tasks, such as data analysis [20] and text generation [72], and enhance the communication between humans and AI with conversations. These advantages indicate their potentials to be game-changers in the research direction of human-AI collaboration for data storytelling, including improving the experience of collaborating with AI and diversifying the collaboration patterns between humans and AI [29]. After two years of the public access of these models, it is a critical time point to reflect how this research discipline progresses in the new era of large-scale generative AI models and identify future opportunities. To achieve the goal, it is essential not only to focus on how these generative AI techniques are applied in existing tools, as explored in a previous survey [17], but more importantly, to compare the human-AI collaboration patterns in the latest tools in the generative AI era with those in earlier ones. Only through this comparison can we understand the shift in human-AI collaboration paradigms, identify the value of these powerful techniques in enhancing human-AI collaboration, and propose future research directions.

The use of large language model (LLM)-powered chatbots, such as ChatGPT, has become popular across various domains, supporting a range of tasks and processes. However, due to the intrinsic complexity of LLMs, effective prompting is more challenging than it may seem. This highlights the need for innovative educational and support strategies that are both widely accessible and seamlessly integrated into task workflows. Yet, LLM prompting is highly task- and domain-dependent, limiting the effectiveness of generic approaches. In this study, we explore whether LLM-based methods can facilitate learning assessments by using ad-hoc guidelines and a minimal number of annotated prompt samples. Our framework transforms these guidelines into features that can be identified within learners' prompts. Using these feature descriptions and annotated examples, we create few-shot learning detectors. We then evaluate different configurations of these detectors, testing three state-of-the-art LLMs and ensembles. We run experiments with cross-validation on a sample of original prompts, as well as tests on prompts collected from task-naive learners. Our results show how LLMs perform on feature detection. Notably, GPT- 4 demonstrates strong performance on most features, while closely related models, such as GPT-3 and GPT-3.5 Turbo (Instruct), show inconsistent behaviors in feature classification. These differences highlight the need for further research into how design choices impact feature selection and prompt detection. Our findings contribute to the fields of generative AI literacy and computer-supported learning assessment, offering valuable insights for both researchers and practitioners.

Accordingly, education researchers and practitioners have increasingly turned to AI literacy as an important learning objective. However, the definition of AI literacy remains vague. Researchers have used the term to describe learning interventions that differ by in school contexts, learning objectives, and types of AI technologies they use. Furthermore, the research of AI literacy is shifting significantly in the wake of generative AI. Thus, it is crucial to review the field and develop a conceptual framework that captures the diverse conceptualizations of AI literacy. The concept of AI literacy and recognition of its potential significance are well-established [75, 127]. One of the pioneering works by Touretzky et al. in 2019 laid out "five big ideas" for the AI4K12 initiative: "computers perceive the world using sensors", "agents maintain models/representations of the world and use them for reasoning", "computers can learn from data", "making agents interact with humans is a substantial challenge for AI developers", and "AI applications can impact society in both positive and negative ways" [127]. This paper had a major influence on subsequent AI literacy curriculum design. The next year, another prominent work by Long and Magerko defined AI literacy as "a set

The rapid advancement of Artificial Intelligence (AI) technology is profoundly transforming human society and concurrently presenting a series of ethical, legal, and social issues. The effective governance of AI has become a crucial global concern. Since 2022, the extensive deployment of generative AI, particularly large language models, marked a new phase in AI governance. Continuous efforts are being made by the international community in actively addressing the novel challenges posed by these AI developments. As consensus on international governance continues to be established and put into action, the practical importance of conducting a global assessment of the state of AI governance is progressively coming to light. In this context, we initiated the development of the AI Governance InternationaL Evaluation Index (AGILE Index). Adhering to the design principle, "the level of governance should match the level of development," the inaugural evaluation of the AGILE Index commences with an exploration of four foundational pillars: the development level of AI, the AI governance environment, the AI governance instruments, and the AI governance effectiveness. It covers 39 indicators across 18 dimensions to comprehensively assess the AI governance level of 14 representative countries globally. The index is utilized to delve into the status of AI governance to date in 14 countries for the first batch of evaluation. The aim is to depict the current state of AI governance in these countries through data scoring, assist them in identifying their governance stage and uncovering governance issues, and ultimately offer insights for the enhancement of their AI governance systems.

Synthetic tabular data have widespread applications in industrial domains such as healthcare, finance, and supply chains, owing to their potential to protect privacy and mitigate data scarcity. However, generating realistic synthetic tabular data while preserving inter-column logical relationships remains a significant challenge for the existing generative models. To address these challenges, we propose LLM-TabFlow, a novel approach that leverages Large Language Model (LLM) reasoning to capture complex inter-column relationships and compress tabular data, while using Score-based Diffusion to model the distribution of the compressed data in latent space. Additionally, we introduce an evaluation framework, which is absent in literature, to fairly assess the performance of synthetic tabular data generation methods in real-world contexts. Using this framework, we conduct extensive experiments on two real-world industrial datasets, evaluating LLM-TabFlow against other five baseline methods, including SMOTE (an interpolation-based approach) and other state-of-the-art generative models. Our results show that LLM-TabFlow outperforms all baselines, fully preserving inter-column relationships while achieving the best balance between data fidelity, utility, and privacy. This study is the first to explicitly address inter-column relationship preservation in synthetic tabular data generation, offering new insights for developing more realistic and reliable tabular data generation methods.

The Test and Measurement domain, known for its strict requirements for accuracy and efficiency, is increasingly adopting Generative AI technologies to enhance the performance of data analysis, automation, and decision-making processes. Among these, Large Language Models (LLMs) show significant promise for advancing automation and precision in testing. However, the evaluation of LLMs in this specialized area remains insufficiently explored. To address this gap, we introduce the Test and Measurement Intelligence Quotient (TMIQ), a benchmark designed to quantitatively assess LLMs across a wide range of electronic engineering tasks. TMIQ offers a comprehensive set of scenarios and metrics for detailed evaluation, including SCPI command matching accuracy, ranked response evaluation, Chain-of-Thought Reasoning (CoT), and the impact of output formatting variations required by LLMs on performance. In testing various LLMs, our findings indicate varying levels of proficiency, with exact SCPI command match accuracy ranging from around 56% to 73%, and ranked matching first-position scores achieving around 33% for the best-performing model. We also assess token usage, cost-efficiency, and response times, identifying trade-offs between accuracy and operational efficiency. Additionally, we present a command-line interface (CLI) tool that enables users to generate datasets using the same methodology, allowing for tailored assessments of LLMs. TMIQ and the CLI tool provide a rigorous, reproducible means of evaluating LLMs for production environments, facilitating continuous monitoring and identifying strengths and areas for improvement, and driving innovation in their selections for applications within the Test and Measurement industry.

This study examines how large language models categorize sentences from scientific papers using prompt engineering. We use two advanced web-based models, GPT-4o (by OpenAI) and DeepSeek R1, to classify sentences into predefined relationship categories. DeepSeek R1 has been tested on benchmark datasets in its technical report. However, its performance in scientific text categorization remains unexplored. To address this gap, we introduce a new evaluation method designed specifically for this task. We also compile a dataset of cleaned scientific papers from diverse domains. This dataset provides a platform for comparing the two models. Using this dataset, we analyze their effectiveness and consistency in categorization.

Recent work in Generative AI enables the stylization of 3D models based on image prompts. However, these methods do not incorporate tactile information, leading to designs that lack the expected tactile properties. We present TactStyle, a system that allows creators to stylize 3D models with images while incorporating the expected tactile properties. TactStyle accomplishes this using a modified image-generation model fine-tuned to generate heightfields for given surface textures. By optimizing 3D model surfaces to embody a generated texture, TactStyle creates models that match the desired style and replicate the tactile experience. We utilize a large-scale dataset of textures to train our texture generation model. In a psychophysical experiment, we evaluate the tactile qualities of a set of 3D-printed original textures and TactStyle's generated textures. Our results show that TactStyle successfully generates a wide range of tactile features from a single image input, enabling a novel approach to haptic design.

Large language models (LLMs) have distinct and consistent stylistic fingerprints, even when prompted to write in different writing styles. Detecting these fingerprints is important for many reasons, among them protecting intellectual property, ensuring transparency regarding AI-generated content, and preventing the misuse of AI technologies. In this paper, we present a novel method to classify texts based on the stylistic fingerprints of the models that generated them. We introduce an LLM-detection ensemble that is composed of three classifiers with varied architectures and training data. This ensemble is trained to classify texts generated by four well-known LLM families: Claude, Gemini, Llama, and OpenAI. As this task is highly cost-sensitive and might have severe implications, we want to minimize false-positives and increase confidence. We consider a prediction as valid when all three classifiers in the ensemble unanimously agree on the output classification. Our ensemble is validated on a test set of texts generated by Claude, Gemini, Llama, and OpenAI models, and achieves extremely high precision (0.9988) and a very low false-positive rate (0.0004). Furthermore, we demonstrate the ensemble's ability to distinguish between texts generated by seen and unseen models. This reveals interesting stylistic relationships between models. This approach to stylistic analysis has implications for verifying the originality of AI-generated texts and tracking the origins of model training techniques.

Large Language Models (LLMs) have proven immensely beneficial in education by capturing vast amounts of literature-based information, allowing them to generate context without relying on external sources. In this paper, we propose a generative AI-powered GATE question-answering framework (GATE stands for Graduate Aptitude Test in Engineering) that leverages LLMs to explain GATE solutions and support students in their exam preparation. We conducted extensive benchmarking to select the optimal embedding model and LLM, evaluating our framework based on criteria such as latency, faithfulness, and relevance, with additional validation through human evaluation. Our chatbot integrates state-of-the-art embedding models and LLMs to deliver accurate, context-aware responses. Through rigorous experimentation, we identified configurations that balance performance and computational efficiency, ensuring a reliable chatbot to serve students' needs. Additionally, we discuss the challenges faced in data processing and modeling and implemented solutions. Our work explores the application of Retrieval-Augmented Generation (RAG) for GATE Q/A explanation tasks, and our findings demonstrate significant improvements in retrieval accuracy and response quality. This research offers practical insights for developing effective AI-driven educational tools while highlighting areas for future enhancement in usability and scalability.