A ChatGPT-based approach for questions generation in higher educationcs.AI updates on arXiv.orgon July 31, 2025 at 4:00 am arXiv:2507.21174v2 Announce Type: replace-cross
Abstract: Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a “Blind test” survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.
arXiv:2507.21174v2 Announce Type: replace-cross
Abstract: Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a “Blind test” survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes. Read More
KIX: A Knowledge and Interaction-Centric Metacognitive Framework for Task Generalizationcs.AI updates on arXiv.orgon July 31, 2025 at 4:00 am arXiv:2402.05346v3 Announce Type: replace
Abstract: People aptly exhibit general intelligence behaviors through flexible problem-solving and the ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. In contrast, artificial agents tend to be specialists, lacking such generalist behaviors. To bridge this gap, artificial agents will require understanding and exploiting critical structured knowledge representations. We introduce a metacognitive reasoning framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects, by leveraging a type space, facilitate the learning of transferable interaction concepts and promote generalization. This framework offers a principled approach for integrating knowledge into reinforcement learning and holds promise as an enabler for generalist behaviors in artificial intelligence, robotics, and autonomous systems.
arXiv:2402.05346v3 Announce Type: replace
Abstract: People aptly exhibit general intelligence behaviors through flexible problem-solving and the ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. In contrast, artificial agents tend to be specialists, lacking such generalist behaviors. To bridge this gap, artificial agents will require understanding and exploiting critical structured knowledge representations. We introduce a metacognitive reasoning framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects, by leveraging a type space, facilitate the learning of transferable interaction concepts and promote generalization. This framework offers a principled approach for integrating knowledge into reinforcement learning and holds promise as an enabler for generalist behaviors in artificial intelligence, robotics, and autonomous systems. Read More
The two people shaping the future of OpenAI’s researchMIT Technology Reviewon July 31, 2025 at 9:06 am For the past couple of years, OpenAI has felt like a one-man brand. With his showbiz style and fundraising glitz, CEO Sam Altman overshadows all other big names on the firm’s roster. Even his bungled ouster ended with him back on top—and more famous than ever. But look past the charismatic frontman and you get…
For the past couple of years, OpenAI has felt like a one-man brand. With his showbiz style and fundraising glitz, CEO Sam Altman overshadows all other big names on the firm’s roster. Even his bungled ouster ended with him back on top—and more famous than ever. But look past the charismatic frontman and you get… Read More
LLMs and Mental HealthTowards Data Scienceon July 31, 2025 at 3:01 pm Are LLMs good or bad for our mental health? It’s more complicated than that.
The post LLMs and Mental Health appeared first on Towards Data Science.
Are LLMs good or bad for our mental health? It’s more complicated than that.
The post LLMs and Mental Health appeared first on Towards Data Science. Read More
How to Benchmark LLMs – ARC AGI 3Towards Data Scienceon July 31, 2025 at 3:08 pm Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3
The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science.
Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3
The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Read More
The ONLY Data Science Roadmap You Need to Get a JobTowards Data Scienceon July 31, 2025 at 2:52 pm Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear understanding of what is required and the best resources to
The post The ONLY Data Science Roadmap You Need to Get a Job appeared first on Towards Data Science.
Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear understanding of what is required and the best resources to
The post The ONLY Data Science Roadmap You Need to Get a Job appeared first on Towards Data Science. Read More
Alibaba’s AI coding tool raises security concerns in the WestAI Newson July 30, 2025 at 10:00 am Alibaba has released a new AI coding model called Qwen3-Coder, built to handle complex software tasks using a large open-source model. The tool is part of Alibaba’s Qwen3 family and is being promoted as the company’s most advanced coding agent to date. The model uses a Mixture of Experts (MoE) approach, activating 35 billion parameters
The post Alibaba’s AI coding tool raises security concerns in the West appeared first on AI News.
Alibaba has released a new AI coding model called Qwen3-Coder, built to handle complex software tasks using a large open-source model. The tool is part of Alibaba’s Qwen3 family and is being promoted as the company’s most advanced coding agent to date. The model uses a Mixture of Experts (MoE) approach, activating 35 billion parameters
The post Alibaba’s AI coding tool raises security concerns in the West appeared first on AI News. Read More
Privacy Artifact ConnecTor (PACT): Embedding Enterprise Artifacts for Compliance AI Agentscs.AI updates on arXiv.orgon July 30, 2025 at 4:00 am arXiv:2507.21142v1 Announce Type: cross
Abstract: Enterprise environments contain a heterogeneous, rapidly growing collection of internal artifacts related to code, data, and many different tools. Critical information for assessing privacy risk and ensuring regulatory compliance is often embedded across these varied resources, each with their own arcane discovery and extraction techniques. Therefore, large-scale privacy compliance in adherence to governmental regulations requires systems to discern the interconnected nature of diverse artifacts in a common, shared universe.
We present Privacy Artifact ConnecT or (PACT), an embeddings-driven graph that links millions of artifacts spanning multiple artifact types generated by a variety of teams and projects. Powered by the state-of-the-art DRAGON embedding model, PACT uses a contrastive learning objective with light fine-tuning to link artifacts via their textual components such as raw metadata, ownership specifics, and compliance context. Experimental results show that PACT’s fine-tuned model improves recall@1 from 18% to 53%, the query match rate from 9.6% to 69.7% when paired with a baseline AI agent, and the hitrate@1 from 25.7% to 44.9% for candidate selection in a standard recommender system.
arXiv:2507.21142v1 Announce Type: cross
Abstract: Enterprise environments contain a heterogeneous, rapidly growing collection of internal artifacts related to code, data, and many different tools. Critical information for assessing privacy risk and ensuring regulatory compliance is often embedded across these varied resources, each with their own arcane discovery and extraction techniques. Therefore, large-scale privacy compliance in adherence to governmental regulations requires systems to discern the interconnected nature of diverse artifacts in a common, shared universe.
We present Privacy Artifact ConnecT or (PACT), an embeddings-driven graph that links millions of artifacts spanning multiple artifact types generated by a variety of teams and projects. Powered by the state-of-the-art DRAGON embedding model, PACT uses a contrastive learning objective with light fine-tuning to link artifacts via their textual components such as raw metadata, ownership specifics, and compliance context. Experimental results show that PACT’s fine-tuned model improves recall@1 from 18% to 53%, the query match rate from 9.6% to 69.7% when paired with a baseline AI agent, and the hitrate@1 from 25.7% to 44.9% for candidate selection in a standard recommender system. Read More
Automated Testing: A Software Engineering Concept Data Scientists Must Know To SucceedTowards Data Scienceon July 30, 2025 at 4:01 pm Why you should read this article Most data scientists whip up a Jupyter Notebook, play around in some cells, and then maintain entire data processing and model training pipelines in the same notebook. The code is tested once when the notebook was first written, and then it is neglected for some undetermined amount of time
The post Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science.
Why you should read this article Most data scientists whip up a Jupyter Notebook, play around in some cells, and then maintain entire data processing and model training pipelines in the same notebook. The code is tested once when the notebook was first written, and then it is neglected for some undetermined amount of time
The post Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science. Read More
Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’AI Newson July 30, 2025 at 2:05 pm Meta CEO Mark Zuckerberg has laid out his blueprint for the future of AI, and it’s about giving you “personal superintelligence”. In a letter, the Meta chief painted a picture of what’s coming next, and he believes it’s closer than we think. He says his teams are already seeing early signs of progress. “Over the
The post Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’ appeared first on AI News.
Meta CEO Mark Zuckerberg has laid out his blueprint for the future of AI, and it’s about giving you “personal superintelligence”. In a letter, the Meta chief painted a picture of what’s coming next, and he believes it’s closer than we think. He says his teams are already seeing early signs of progress. “Over the
The post Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’ appeared first on AI News. Read More
