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generative ai

2,366 papers

#generative ai Open access Sep 2026

Supplemental Tables for "Evaluating Generative AI in Civil Engineering Mechanics Education: Modeling, Free-Body Diagram Construction, and Spatial Reasoning in Statics and Dynamics"

The supplemental materials consist of Tables S1–S6, which provide summary observations and detailed problem-level evaluation results for 13 undergraduate engineering mechanics problems: eight statics problems, including a custom two-dimensional truss problem, and five dynamics problems. The tables document the performa...

Ashraf Badir, Karl Horvat, Ahmed Elshall · 0 citations
#generative ai Open access Sep 2026

Preregistration: Purposes of Generative AI Use and Computational Problem-Solving Process — A 62-Country Analysis of PISA 2025 Learning-in-a-Digital-World Log Data

Preregistration for a confirmatory analysis of PISA 2025 Learning-in-a-Digital-World (LDW) log data, deposited before the researcher had access to any unmasked estimate for the confirmatory sample. RESEARCH QUESTION. Are the purposes for which 15-year-olds use generative AI for schoolwork (summarising, preliminary rese...

Liang-Cheng Chang · 0 citations
#generative ai Open access Sep 2026

E089 Quantified-Chunk New-Post Cohort: Pre-registration with In-Flight Amendments (v1.2). Primary analysis re-scoped to P3; primary contribution a query-validity and namespace-drift taxonomy.

E089 is a pre-registered field study testing how generative AI search engines cite cold, newly published content. The study used 12 posts arranged as six topic-matched A/B pairs. One page in each pair opened with a quantified-chunk template: a front-loaded definition, named entities and at least one hard number already...

Artur Ferreira · 0 citations
#generative ai Open access Sep 2026

Reimagined worlds: Sociotechnical imaginaries of generative AI within an educational institution

Educational institutions are undergoing transition, as students, and educators increasingly adopt generative AI. Therefore, it is important to examine stakeholders' perspective and their interpretations of safe, ethical, and responsible AI. Undertaking a micro-ethnographic case study, this study contributes to the lite...

Shreelakshmi Dinesh · 0 citations
#generative ai Open access Sep 2026

Phase Transition & Acid-Base Equilibrium Model] Dynamic Phase Transition Model of Outcome Creation under Human-AI Co-Creation — Critical Boundary Formalization of Depth of Thought (D_k) via Acid-Base Equilibrium Buffer Curves —

Abstract This paper introduces an acid-base equilibrium (titration curve) model from biochemistry as an analogy and dynamic mathematical framework to explain the non-linear jump phenomenon in outcome creation during interaction with Generative AI. Traditional project management and business performance metrics have rel...

Hideo Kajino · 0 citations
#generative ai Open access Sep 2026

Conceptual Study on the Depth of Thought Model (D = O \times A \times U) and Outcome Creation Capacity in the Generative AI Era

Abstract This paper presents a conceptual approach to capturing the tectonic shift in individual outcome-creation structures driven by the widespread adoption of Generative AI. Human cognition is defined at its core as the "Depth of Thought (D)," modeled as the multiplicative product of three factors: Observation (O),...

Hideo Kajino · 0 citations
#generative ai Sep 2026

Empowered yet unethical? Understanding GenAI misuse intention in generative AI chatbot use

Purpose This study examines whether generative AI chatbot features are associated with user autonomy and competence and whether these psychological states are, in turn, associated with GenAI misuse intention, conceptualized here as an empowerment backfire mechanism. Design/methodology/approach Drawing primarily on the...

Shiu‐Wan Hung, Jyun-Hao Jian · 0 citations
#generative ai Open access Sep 2026

Conceptual Study on the Depth of Thought Model (D = O \times A \times U) and Outcome Creation Capacity in the Generative AI Era

Abstract This paper presents a conceptual approach to capturing the tectonic shift in individual outcome-creation structures driven by the widespread adoption of Generative AI. Human cognition is defined at its core as the "Depth of Thought (D)," modeled as the multiplicative product of three factors: Observation (O),...

Hideo Kajino · 0 citations
#generative ai Open access Sep 2026

Phase Transition & Acid-Base Equilibrium Model] Dynamic Phase Transition Model of Outcome Creation under Human-AI Co-Creation — Critical Boundary Formalization of Depth of Thought (D_k) via Acid-Base Equilibrium Buffer Curves —

Abstract This paper introduces an acid-base equilibrium (titration curve) model from biochemistry as an analogy and dynamic mathematical framework to explain the non-linear jump phenomenon in outcome creation during interaction with Generative AI. Traditional project management and business performance metrics have rel...

Hideo Kajino · 0 citations
#large language models Open access Sep 2026

The prose penalty in AI-assisted authorship

Generative AI based on large language models (LLMs) is increasingly used in academic, journalistic, and other professional and online writing, yet its implications for human authorship remain contested. We asked whether assignment of authorship credit depends on which task within the larger writing process a writer del...

F. Feroz, Ivar R. Hannikainen, Jim A. C. Everett et al. · 0 citations

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Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

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