generative ai

Cognitive Enhancement or Social Atrophy? Generative AI, Neurodiversity, and Human Flourishing

Abstract Debates on the cognitive impact of Generative AI often assume a homogeneous model of human cognition and focus on individual performance. AI’s epistemic authority in contemporary life raises concerns about the social conditions for democratic inquiry and human flourishing. Drawing on Dewey’s pragmatist concept...

Connoisseurship : A Critical Interpretivist Exploration of the methodological landscape

This developmental paper offers methodological musings at the point when we want to embark on a journey of empirically researching a philosophically complex and methodologically elusive concept: connoisseurship. The three authors are interested in connoisseurship for different reasons and thus from different perspectiv...

Mitigating artificial intelligence-related animal welfare risks in animal farming

In animal farming, artificial intelligence (AI) is increasingly promoted as a means to enhance productivity, reduce greenhouse gas emissions, and improve animal welfare. However, discussion of the animal welfare risks arising from these technologies, particularly emerging generative AI and computer vision tools, remain...

A Study on the Use of Generative AI in the Revision of Urban Master Plans

Conclusions and the Human Future

This final chapter argues that the future of AI in screen industries will be determined less by long-range predictions than by near-term governance, organizational choices and explicit values. It critiques glossy futurism and Artificial General Intelligence fixation for masking current impacts of applied systems on lab...

GAIDeT – Making AI Use in Research Visible, Comparable and Reusable

This poster was presented at the 30th Annual International Conference on Science and Technology Indicators (STI-ENID 2026), held in Antwerp, Belgium, 9–11 September 2026. The poster presents GAIDeT as an approach for moving from narrative generative AI-use disclosures towards structured, machine-readable research infor...

User-Grounded Fidelity Probes: A Diagnostic Method for Evaluating Generative-AI Capability from Running Code and Field Expertise

University teaching and development lack a shared vocabulary for deciding which generative-AI model fits which use. This paper proposes user-grounded fidelity probes (here, probe means a diagnostic task whose answer the user already holds, not linear probing of internal representations): a diagnostic method that measur...

Assessment design under imperfect information: generative AI, disclosure, and student response in higher education

Artificial Intelligence and Machine Learning for Business Management

Artificial Intelligence and Machine Learning for Business Management is an academic textbook that presents an integrated understanding of artificial intelligence (AI), machine learning (ML), business analytics, managerial decision-making, strategy, digital transformation, responsible AI, governance, sustainability, and...

Autonomous SSE: When Stable Completion Replaces Core Preservation in Human–AI Systems From Observed Setting Deviations in Generative Video to Autonomous AI Operations: A Theoretical Extension

This study extends the Stability Substitution Effect (SSE) to autonomous AI systems and proposes the concept of Autonomous Stability Substitution Effect (Autonomous SSE). The central argument is that Autonomous Completion and Autonomous Core Preservation are not equivalent. A system may continue to produce a stable and...

Reproducibility Code for Memory Kinetics and State-Dependent Contact in Finite Stochastic Predictors

This MIT-licensed software archive supports the preprint “Memory Kinetics and State-Dependent Contact in Finite Stochastic Predictors” by Satoshi Kawasaki, Independent Researcher. It contains the Python source required to replay the finite Markov mechanism comparisons, preserved historical helper code, the portable rep...

Theoretical Engineering

This paper introduces Theoretical Engineering, a framework that systematically applies engineering management (version control, exception handling) and design (interface layering, decoupling) to theoretical construction and thinking management, with the ultimate goal of achieving recursive automation of theoretical inq...