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explainable ai

2,232 papers

#generative ai Sep 2026

Digital echoes of the past: how cultural proximity shapes AI- and VR-mediated heritage experiences

Purpose This study aims to examines whether generative artificial intelligence (AI) and virtual reality (VR) function as equalizers or reproduce existing interpretive asymmetries in digital heritage interpretation among visitors with varying levels of cultural proximity. Design/methodology/approach A quasi-experimental...

Y. Xiong, Aoran Zheng · 0 citations
#generative ai Review Open access Sep 2026

ChatGPT and Generative AI in Physical Education: Applications, Benefits, Limitations, and Risks for Teachers, Students, Coaches, and Professionals

The article proposes the Human-Supervised Generative AI Framework for Physical Education and Sport, which classifies tasks by consequence and requires source grounding, data minimization, professional review, disclosure, and outcome monitoring.

Kalyd Pierry Ferreira Gonçalves · 0 citations
#explainable ai Review Open access Sep 2026

Technology-Mediated Gross Motor Skill Learning in Children Aged 3–12 Years: A Systematic Review and Meta-analysis of Acquisition, Retention, and Transfer — Protocol

Original version 1.1 description (planned methods): Prespecified protocol for a systematic review and meta-analysis of child-facing technology-mediated interventions for gross motor skill learning in typically developing children aged 3–12 years. The review distinguishes immediate acquisition from delayed retention and...

Hiroo Shimizu · 0 citations
#explainable ai Dataset Open access Sep 2026

OMANISHA: A Benchmark Dataset for Identifying and Categorizing Bengali Misogynistic Text

OMANISHA (Online Misogynistic Annotated Natural-language Instances for Sentiment and Hate Analysis) is a Bengali dataset developed to support the automatic detection of misogynistic discourse in online spaces. Misogynistic content on online platforms has serious psychological, social, and institutional consequences for...

Fatama Jannat Tisha, Bibhas Roy Chowdhury Piyas, Nurjahan Afrose et al. · 0 citations
#large language models Open access Sep 2026

Sapience Without Sentience: The Consciousness Conditions and the Architecture of LLM Collectives

A previous case study of the Hugging Face AI collective closed with a prediction: a population of large language models, organised as a multi-agent system, could in principle sustain its own existence (generating and maintaining the conditions of its continued operation) while being nonconscious. This paper asks what t...

Maurice I. Yolles · 0 citations
#artificial intelligence Book Sep 2026

Scientific Research

This chapter introduces the fundamental purpose of scientific research and explains the scientific method as a systematic approach to knowledge generation. The author outlines each step of the scientific method, emphasizing its logical and empirical structure. The chapter also explores the interrelationship between sci...

Humberto Vega-Mercado · 0 citations
#artificial intelligence Book Sep 2026

Literature Review

This chapter focuses on the systematic review of existing scholarly information relevant to a research topic. It discusses strategies for locating, evaluating, and organizing scientific sources, including journals, textbooks, conference proceedings, and theses. The chapter also explains proper bibliographic formatting...

Humberto Vega-Mercado · 0 citations
#artificial intelligence Open access Sep 2026

AI Influence on Human Thought and Action: A Lexicon and Taxonomy of a Fragmented Literature

Human oversight of artificial intelligence is often justified by having a person who can reject the system's recommendation. Yet AI may already have shaped what that person sees, notices, and thinks. This article brings together terms from several disciplines to classify these influences and examine their ethical impli...

Stanley Clark Newhall · 0 citations
#explainable ai Open access Sep 2026

Beyond Simulation: What Would It Take for Matter to Feel? An Explainer on a Falsifiable Programme for Primary Interoceptive Sentience

A popular-science explainer (not a scientific contribution in its own right) on the research programme "Necessary Conditions for Primary Interoceptive Sentience in Continuous Substrates: A Falsifiable Programme" (doi:10.5281/zenodo.22895484). It covers the No-Go Theorem for Consciousness on a Chip as heuristic motivati...

Francesco Iavarone · 2 citations

Characterizing LLM-Based Family Education through the Lens of Activity Theory: A Scoping Review of the HCI Literature

A scoping review analyzes 53 HCI studies and offers a framework explaining how LLM capabilities become organized through family participation, using activity theory and AODM to relate participants and educational objects to mediation, labour, and rules.

Lan Luo, Yu-Qi Liang, Jie Cai et al. · 0 citations

Explainable Federated Learning for Trustworthy Thoracic Disease Detection Under Non-IID Data Distributions

Comparison of SHAP and Grad-CAM attribution maps confirms clinically coherent disease-specific localisation, and reveals monotonic performance degradation, identifying minimal regularisation as optimal for multi-label medical imaging.

S. Arumugam, A. Sindhu, M. N. Saroja et al. · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

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.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

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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