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2,176 papers
In the era of artificial intelligence (AI), sustainable education requires not only the integration of advanced technologies but also the cultivation of key competencies such as critical thinking. This study investigates English major students’ engagement with generative AI tools in translation learning, focusing on th...
Generative artificial intelligence (GenAI) is increasingly embedded in the everyday work of statisticians and other quantitative scientists, yet practical experience often advances faster than formal guidance on where these tools add value and where they create new risks. This manuscript synthesizes a moderated discuss...
This study examines Technology Education Students Perceptions of ChatGPT use in distance learning, focusing on its benefits, ease of use, and risks. The study employs a descriptive quantitive approach. The participants are Technology Education students who have experience using ChatGPT for learning activities. Data are...
The growing use of Artificial Intelligence (AI), particularly generative AI, is changing practices of English teaching and learning in higher education. Yet access to AI and the capacity to use it pedagogically remain uneven across geographical and socioeconomic contexts. This qualitative comparative study examines how...
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Introduction Hate speech directed against the Mapuche people, historically perpetuated through the media's mechanisms of racialisation, has found a powerful platform for amplification on social media. This article analyses how trainee how history teachers conceptualise this phenomenon from a pedagogical perspective and...
Generative Artificial Intelligence (GenAI) has recently gained attention as a technology that supports supply chain decision-making due to its unique natural language capabilities. In this context, it is seen as a top-layer augmentation to traditional analytical and optimisation-based technologies, accelerating decisio...
This study evaluates seven generative AI platforms for student learning using the Analytic Hierarchy Process. Seven criteria were considered: accuracy, explanation quality, mathematical ability, research assistance, coding support, ease of use, and reliability. Pairwise-comparison data were collected from 500 students...
The reference for citation 4 was erroneously written as "Zarei M, Norasteh AA, Asadi A. Prevalence and risk factors of musculoskeletal injuries among elite volleyball players. Int J Sports Med. (2020) 41:845-52. doi: 10.1055/a-1182-3456". It should be "Kilic O, Maas M, Verhagen E, Zwerver J, Gouttebarge V. Incidence, a...
This study examines how generative AI is reshaping digital marketing by enabling unprecedented levels of personalization, automation, and consumer interaction, while simultaneously producing subtler, more complex forms of algorithmic manipulation that extend well beyond conventional dark patterns and threaten meaningfu...
Universities and journals have answered generative artificial intelligence (AI) with one main instrument: mandatory disclosure. Ask researchers to declare what they used, and transparency should follow. The evidence says it does not. Across 5,114 journals and 5.2 million papers, about 70% of journals now require disclo...
IMPACTMunicipal leaders and managers should consider technical, social and normative support elements before expanding generative AI use. Respondents rated task benefits, usable and reliable tools, and clear ethical and information-security guidance most highly. In exploratory item-level comparisons, AI users assigned...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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