AI is employed in education to personalize learning experiences, provide adaptive feedback, and automate administrative tasks. It utilizes machine learning algorithms, natural language processing, and data analytics to enhance student engagement, optimize teaching methods, and streamline educational processes, leading to more effective and personalized education.
Researchers developed the SPARRO framework, a structured approach for ethical AI integration in education, addressing challenges like AI hallucinations and plagiarism in healthcare and nursing courses. Future validation in other academic disciplines is essential.
The G7 Toolkit for Artificial Intelligence in the Public Sector outlines strategies for ethical, secure, and effective AI deployment in governments, emphasizing human rights and transparency. It includes case studies and best practices to guide responsible AI adoption globally.
NVIDIA introduces NVLM 1.0, a multimodal large language model that sets a new benchmark by excelling in both vision-language and text-only tasks, showcasing innovations in high-resolution image processing.
Researchers found that as large language models (LLMs) scale, they become less reliable, making errors even on simple tasks, and producing plausible but incorrect answers to complex questions. The study emphasizes the need for better strategies in AI development, especially for high-stakes applications.
Incorrect AI explanations, even when paired with accurate advice, can impair human reasoning and decision-making, resulting in long-term knowledge degradation.
Review highlights the critical role of explainable AI in making generative AI transparent, trustworthy, and aligned with human values.
Researchers propose revisions to trust models, highlighting the complexities introduced by generative AI chatbots and the critical role of developers and training data.
Rresearch examines the interdisciplinary challenges in building trust and trustworthiness in AI governance, proposing a "watchful trust" framework to manage risks in public sector AI deployment.
Karl de Fine Licht of Chalmers University of Technology argues that universities may be morally justified in banning student use of generative AI tools, considering ethical concerns like student privacy and environmental impact.
Research paper examines the complexities of global AI governance, proposing a cautious approach to developing an international regulatory framework that balances innovation with ethical and societal needs.
Researchers explored the challenges of aligning large language models (LLMs) with human values, emphasizing the need for stronger ethical reasoning in AI. The study highlights gaps in current models' ability to understand and act according to implicit human values, calling for further research to enhance AI's ethical decision-making.
A multiplatform computer vision system was developed to assess schoolchildren's physical fitness using smartphones. This system demonstrated high accuracy in field and lab tests, providing a reliable and user-friendly tool for fitness evaluation in educational environments.
Researchers developed a machine learning technique to predict obesity risk by analyzing sociodemographic, lifestyle, and health factors. The study, which achieved 79% accuracy, identified significant predictors like age, sex, education, diet, and smoking habits, offering valuable insights for personalized obesity prevention.
In an article published in Computers and Education: Artificial Intelligence, researchers explored various methods for generating question-answer (QA) pairs using pre-trained large language models (LLMs) in higher education. They assessed pipeline, joint, and multi-task approaches across three datasets through automated metrics, teacher evaluations, and real-world educational settings.
Researchers in the Journal of the Air Transport Research Society evaluated 12 large language models (LLMs) across aviation tasks, revealing varied accuracy in fact retrieval and reasoning capabilities. A survey at Beihang University explored student usage patterns, highlighting optimism for LLMs' potential in aviation while emphasizing the need for improved reliability and safety standards.
Researchers explored the potential of large language models (LLMs) like GPT-4 and Claude 2 for automated essay scoring (AES), showing that these AI systems offer reliable and valid scoring comparable to human raters. The study underscores the promise of LLMs in educational technology, while highlighting the need for further refinement and ethical considerations.
Researchers introduced "Chameleon," a mixed-modal foundation model designed to seamlessly integrate text and images using an early-fusion token-based method. The model demonstrated superior performance in tasks such as visual question answering and image captioning, setting new standards for multimodal AI and offering broad applications in content creation, interactive systems, and data analysis.
Researchers introduced JASCO, a pioneering model aimed at generating high-quality music samples based on text descriptions. JASCO integrates symbolic and audio conditions using a flow-matching approach, leveraging normalizing flows for realistic sample generation.
Researchers integrated large language models (LLMs) into digital audio-tactile maps (DATMs) to aid visually impaired individuals (PVIs). Using a smartphone prototype, the study showed that LLMs, like ChatGPT, provided effective verbal feedback, improving users' ability to understand and navigate digital maps independently.
In a study published in Scientific Reports, researchers used machine learning to predict upper secondary education dropout with high accuracy. By analyzing comprehensive data from kindergarten to Grade 9, the study identified key factors influencing dropout, enabling early intervention strategies to support at-risk students.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.