Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives.

The first fully human-capable AI agents for healthcare are now being used across the country. Artificial intelligence is gaining more of parents' trust than actual doctors. That's according to a new study from the University of Kansas Life Span Institute, which found that parents seeking information on their children's health are turning to AI more than human health care professionals. The research, published in the Journal of Pediatric Psychology, also revealed that parents rate AI-generated text as "credible, moral and trustworthy." More than 100 parents ranging from 18 to 65 years old were asked to rate text generated by either a human doctor or ChatGPT (the AI chatbot made by OpenAI) under the supervision of an expert.

The news: ChatGPT can now search the web for up-to-date answers to a user's queries. Previously it was restricted to generating answers from its training data, and had limited web search capabilities. But now, ChatGPT will automatically search the web in response to queries about recent information such as sports, stocks, or news of the day, and can deliver rich multi-media results. How to use it: The feature is available now for the chatbot's paying users, but OpenAI intends to make it available for free later, even when people are logged out. It also plans to combine search with its voice features.

This study demonstrates that Large Language Models (LLMs) can transcribe historical handwritten documents with significantly higher accuracy than specialized Handwritten Text Recognition (HTR) software, while being faster and more cost-effective. We introduce an open-source software tool called Transcription Pearl that leverages these capabilities to automatically transcribe and correct batches of handwritten documents using commercially available multimodal LLMs from OpenAI, Anthropic, and Google. In tests on a diverse corpus of 18th/19th century English language handwritten documents, LLMs achieved Character Error Rates (CER) of 5.7 to 7% and Word Error Rates (WER) of 8.9 to 15.9%, improvements of 14% and 32% respectively over specialized state-of-the-art HTR software like Transkribus. Most significantly, when LLMs were then used to correct those transcriptions as well as texts generated by conventional HTR software, they achieved near-human levels of accuracy, that is CERs as low as 1.8% and WERs of 3.5%. The LLMs also completed these tasks 50 times faster and at approximately 1/50th the cost of proprietary HTR programs. These results demonstrate that when LLMs are incorporated into software tools like Transcription Pearl, they provide an accessible, fast, and highly accurate method for mass transcription of historical handwritten documents, significantly streamlining the digitization process.

Designing public spaces requires balancing the interests of diverse stakeholders within a constrained physical and institutional space. Designers usually approach these problems through participatory methods but struggle to incorporate diverse perspectives into design outputs. The growing capabilities of image-generative artificial intelligence (IGAI) could support participatory design. Prior work in leveraging IGAI's capabilities in design has focused on augmenting the experience and performance of individual creators. We study how IGAI could facilitate participatory processes when designing public spaces, a complex collaborative task. We conducted workshops and IGAI-mediated interviews in a real-world participatory process to upgrade a park in Los Angeles. We found (1) a shift from focusing on accuracy to fostering richer conversations as the desirable outcome of adopting IGAI in participatory design, (2) that IGAI promoted more space-aware conversations, and (3) that IGAI-mediated conversations are subject to the abilities of the facilitators in managing the interaction between themselves, the AI, and stakeholders. We contribute by discussing practical implications for using IGAI in participatory design, including success metrics, relevant skills, and asymmetries between designers and stakeholders. We finish by proposing a series of open research questions.

This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 11 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted a GenAI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI's impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while preserving learner agency in GenAI-enhanced educational environments.

In automated crop protection tasks such as weed control, disease diagnosis, and pest monitoring, deep learning has demonstrated significant potential. However, these advanced models rely heavily on high-quality, diverse datasets, often limited and costly in agricultural settings. Traditional data augmentation can increase dataset volume but usually lacks the real-world variability needed for robust training. This study presents a new approach for generating synthetic images to improve deep learning-based object detection models for intelligent weed control. Our GenAI-based image generation pipeline integrates the Segment Anything Model (SAM) for zero-shot domain adaptation with a text-to-image Stable Diffusion Model, enabling the creation of synthetic images that capture diverse real-world conditions. We evaluate these synthetic datasets using lightweight YOLO models, measuring data efficiency with mAP50 and mAP50-95 scores across varying proportions of real and synthetic data. Notably, YOLO models trained on datasets with 10% synthetic and 90% real images generally demonstrate superior mAP50 and mAP50-95 scores compared to those trained solely on real images. This approach not only reduces dependence on extensive real-world datasets but also enhances predictive performance. The integration of this approach opens opportunities for achieving continual self-improvement of perception modules in intelligent technical systems.

ChatGPT Search is here to try to combine the best of chatbots and web searches. OpenAI's latest feature searches the web in response to your natural language queries, delivering "fast, timely answers with links to relevant web sources." When using ChatGPT, the bot will search the web depending on what you ask. Or, if you want to manually override its decision-making, you can tap a new web search icon below the input bar. OpenAI says the feature looks for "original, high-quality content from the web," integrating it into its conversational answers.

OpenAI just launched its AI search update for ChatGPT. Three months after the company's initial announcement of a SearchGPT prototype, OpenAI's vision for the future of AI search is now available to the public. "We're focused on making ChatGPT the best place to answer any question, including live information from the web," says Adam Fry, the product lead for search on ChatGPT. Referred to by Fry now as "ChatGPT search" rather than "SearchGPT," the feature enters an increasingly crowded and contentious field of AI search options for users--with competition from smaller startups, like Perplexity, as well as tech giants, like Google with its AI Overview search results. So far in 2024, journalists have criticized both Google and Perplexity's implementations of AI search for improperly copying aspects of original work and hallucinating fake information.

"Our goal is to make ChatGPT the smartest assistant, and now we're really enhancing its capabilities in terms of what it has access to from the web," Fry tells MIT Technology Review. The feature is available today for the chatbot's paying users. While ChatGPT search, as it is known, is initially available to paying customers, OpenAI intends to make it available for free later, even when people are logged out. The company also plans to combine search with its voice features and Canvas, its interactive platform for coding and writing, although these capabilities will not be available in today's initial launch. The company unveiled a standalone prototype of web search in July.