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2,807 papers
Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite this, learning from those abductive signals is largely ignored in artificial intelligence. While explainable AI (XAI) increasingly draws on...
Chain-of-thought (CoT) monitoring is only meaningful if written reasoning causally constrains the answer. We introduce continuation-based causal testing, an ablation-patch intervention that perturbs one reasoning step, truncates the chain, and forces the model to continue from the corrupted prefix. It measures how load...
An AI-Augmented Active Aging Framework that explains how conversational AI affordances, responsiveness, personalization, accessibility, and availability, interact with personal, social, environmental, and system-level conversion factors and argues that the primary value of conversational AI lies not in its ability to s...
Purpose: This study validated and refined a context-specific framework explaining meaningful use behavior of PictureThis artificial intelligence (AI) for plant identification among undergraduate students in Ugandan public universities. Unlike an earlier analysis of predictors of behavioral intention from the same docto...
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INTRODUCTION: Due to the development of the Industrial Internet of Things (IIoT), it has been possible to make the predictive maintenance of the manufacturing industry using data. Nevertheless, recent developments in manufacturing systems are prone to higher levels of disruptions caused by sensor degradation, data anom...
This study develops a comprehensive understanding of AI leadership by examining how traditional leadership theories and psychological constructs explain effective leadership behaviors in AI-intensive organizational contexts by synthesizing theories from leadership studies, cognitive psychology, organizational behavior,...
Biostatistics literacy is essential for evidence-based medicine, yet no instrument specifically measures this competency in medical students. This study aimed to develop a measure of self-perceived biostatistics literacy and conduct an initial psychometric evaluation in samples comprising predominantly early-stag...
Background Population aging and the growing burden of chronic diseases have increased the need for more accessible and equitable primary healthcare. Artificial intelligence (AI)-assisted hierarchical diagnosis and treatment has been increasingly introduced into primary care to support patient guidance, triage, follow-u...
Introduction This study examined whether perceived stress and rumination help explain why postgraduate students with higher artificial intelligence (AI) anxiety also report greater academic burnout. Methods We analysed data from a cross-sectional online questionnaire survey conducted among postgraduate students in Guan...
Niche construction emphasizes the active aspect of life forms that alter their environment via feedback loops in which that environment inevitably changes their behavior, structure, and future evolution. We argue that this powerful dynamic is general, extending far beyond typical applications in ecology and evolutionar...
The findings suggest that detailed AI explanations do not necessarily improve AI-augmented medical decisions even though they can increase trust in AI recommendations and the importance of AI recommendation quality in clinical decision making is underscored.
Tourists’ responses to the adoption and implementation of AI-based travel solutions have received considerable scholarly attention, yet investors’ reactions to such adoption decisions remain insufficiently understood. Drawing on multiple intelligences and tourism innovation archetypes theories, this study develops a co...
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
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