The rapid integration of Generative Artificial Intelligence (GenAI) into higher education presents both opportunities and challenges for assessment design, particularly within Project-Based Assessment (PBA) contexts. Traditional assessment methods often emphasise the final product in the PBA, which can now be significantly influenced or created by GenAI tools, raising concerns regarding product authenticity, academic integrity, and learning validation. This paper advocates for a reimagined assessment model for Project-Based Learning (PBL) or a capstone project that prioritises process-oriented evaluation, multi-modal and multifaceted assessment design, and ethical engagement with GenAI to enable higher-order thinking. The model also emphasises the use of (GenAI-assisted) personalised feedback by a supervisor as an observance of the learning process during the project lifecycle. A use case scenario is provided to illustrate the application of the model in a capstone project setting. The paper concludes with recommendations for educators and curriculum designers to ensure that assessment practices remain robust, learner-centric, and integrity-driven in the evolving landscape of GenAI.

Generative artificial intelligence (GenAI) is transforming education, redefining the role of trainers and coaches in learning environments. In our study, we explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches. Through qualitative interviews with professionals in education and corporate training, we identify the following key topics: trainers and coaches increasingly act as facilitators and content moderators rather than primary creators, efficiency gains allow for a stronger strategic focus but at the same time the new tools require new skills. Additionally, we analyze how the anthropomorphism of AI shapes user trust and expectations. From these insights, we derive how tools based on GenAI can successfully be implemented for trainers and coaches on an individual, organizational, systemic, and strategic level.

It is a Monday afternoon in August, and I am on the internet watching a former cable-news anchor interview a dead teenager on Substack. This dead teenager--Joaquin Oliver, killed in the mass shooting at Marjory Stoneman Douglas High School, in Parkland, Florida--has been reanimated by generative AI, his voice and dialogue modeled on snippets of his writing and home-video footage. The animations are stiff, the model's speaking cadence is too fast, and in two instances, when it is trying to convey excitement, its pitch rises rapidly, producing a digital shriek. How many people, I wonder, had to agree that this was a good idea to get us to this moment? I feel like I'm losing my mind watching it. Jim Acosta, the former CNN personality who's conducting the interview, appears fully bought-in to the premise, adding to the surreality: He's playing it straight, even though the interactions are so bizarre. Acosta asks simple questions about Oliver's interests and how the teenager died.

In today's episode, our host Zöe Schiffer is joined by WIRED's senior politics writer Jake Lahut to run through five of the best stories we published this week--from how the Trump administration is creating and sharing memes to make fun of deportations, to NASA's ambitious goal to put nuclear reactors on the moon. Then, Zöe and Jake dive into why users kind of hated OpenAI's GPT-5 release. Mentioned in this episode: OpenAI Scrambles to Update GPT-5 After Users Revolt by Will Knight The Trump Administration Is Using Memes to Turn Mass Deportation Into One Big Joke by Tess Owen Trump Family–Backed World Liberty Financial Sets Up 1.5 Billion Crypto Treasury by Joel Khalili Inside the'Whites Only' Community in Arkansas by David Gilbert Why the US Is Racing to Build a Nuclear Reactor on the Moon by Becky Ferreira Join us live in San Francisco on September 9th. Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link.

The World Wide Web (Web) emerged as a new medium in the mid-1990s. It was invented by Tim Berners-Lee at the European Organization for Nuclear Research (CERN) in 1989, but its exploding popularity was also enabled by the release of the Mosaic Web browser in 1993 and the Internet becoming commercially available in 1995. A communication revolution was launched. Roughly 30 years later, the release of ChatGPT by OpenAI in Nov. 2022 launched another revolution. High-quality generation of natural-language text, defined as the hallmark of intelligence by Alan Turing in 1950, is suddenly widely available. I wonder, however, if the generative AI (GenAI) revolution will end up devouring the Web revolution.

In this paper we present and validate a new synthetic dataset for training visual entailment models. Existing datasets for visual entailment are small and sparse compared to datasets for textual entailment. Manually creating datasets is labor-intensive. We base our synthetic dataset on the SNLI dataset for textual entailment. We take the premise text from SNLI as input prompts in a generative image model, Stable Diffusion, creating an image to replace each textual premise. We evaluate our dataset both intrinsically and extrinsically. For extrinsic evaluation, we evaluate the validity of the generated images by using them as training data for a visual entailment classifier based on CLIP feature vectors. We find that synthetic training data only leads to a slight drop in quality on SNLI-VE, with an F-score 0.686 compared to 0.703 when trained on real data. We also compare the quality of our generated training data to original training data on another dataset: SICK-VTE. Again, there is only a slight drop in F-score: from 0.400 to 0.384. These results indicate that in settings with data sparsity, synthetic data can be a promising solution for training visual entailment models.

A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Generation (RAG) system to retrieve relevant educational material and adjust explanations in real time. This builds on existing research in adaptive learning, demonstrating how student-driven feedback loops can modify AI-generated responses for improved student retention and engagement, particularly in STEM education. Preliminary findings from a study with STEM students indicate improved learning outcomes and confidence compared to traditional AI tools. This work highlights AI's potential to create dynamic, feedback-driven, and personalized learning environments through iterative refinement.

Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models (LLMs)-including OpenAI O1, OpenAI 4o, Gemini Flash (thinking), Deepseek Janus, Grok, Llama 3.2 and 4, Qwen 2 and 2.5 VL, Mistral Pixtral, and Claude 3.5 and 3.7 Sonnet-in detecting fraudulent documents. We benchmark these models against each other and prior work on document fraud detection techniques using a standard dataset with real transactional documents. Through prompt optimization and detailed analysis of the models' reasoning processes, we evaluate their ability to identify subtle indicators of fraud, such as tampered text, misaligned formatting, and inconsistent transactional sums. Our results reveal that top-performing multi-modal LLMs demonstrate superior zero-shot generalization, outperforming conventional methods on out-of-distribution datasets, while several vision LLMs exhibit inconsistent or subpar performance. Notably, model size and advanced reasoning capabilities show limited correlation with detection accuracy, suggesting task-specific fine-tuning is critical. This study underscores the potential of multi-modal LLMs in enhancing document fraud detection systems and provides a foundation for future research into interpretable and scalable fraud mitigation strategies.