generative ai

Práticas Artísticas Digitais Sustentáveis na Educação Visual: Perspetivas Críticas sobre Arte Generativa e Realidade Aumentada

The rapid integration of generative artificial intelligence and augmented reality into artistic and educational contexts has intensified debates surrounding creativity, authorship, ethics, and sustainability. While these technologies offer significant potential for innovation in visual arts education, their environmental and ethical implications remain underexplored, particularly at the level of basic education.This paper examines sustainable digital art practices developed within visual education contexts, focusing on the critical integration of generative art and augmented reality. Grounded in practice-based research and art-based methodologies, the study analyses pedagogical and artistic experiences that promote co-authorship between students and artificial intelligence systems while encouraging critical digital literacy and environmental awareness.By framing generative and augmented practices as artistic processes rather than merely technological tools, the paper argues for a sustainable and ethically informed approach to digital creativity. It highlights how low-impact digital strategies, reflective use of AI systems, and contextualised augmented narratives can foster meaningful artistic engagement without reinforcing technological dependence or ecological neglect.The findings contribute to contemporary discussions on media art, digital sustainability, and education, proposing a critical framework for integrating emerging technologies into artistic practice. This approach positions digital art education as a space for ecological awareness, creative responsibility, and reflective authorship within expanded cinematic and media-art practices.

Higgs-Stiffness-Gravity (revised)

We present the fully formalized version of Higgs-Stiffness Gravity, a unified framework in which the dark sector arises from the tensorial elastic response of the Higgs condensate to spacetime curvature. By identifying the Higgs field as the order parameter for vacuum stiffness, we show that the Standard Model vacuum acts as a non-linear elastic medium whose effective bulk modulus is constrained by a tensorial homeostatic term. This naturally produces a reinforced pole (dark matter) at high curvatures and a stretch pole (dark energy) at low curvatures. The model is gauge-invariant, ghost-free, contains only one free parameter (ξ), and passes the Category Error Detector (CED) with 0/5 errors. The tensorial constraint λH(Gµν− κTµν)Σµν enforces homeostasis at the Lagrangian level, ensuring Beff R >0 and numerical stability. Comparison with DESI DR1 and Euclid mock data shows a moderate preference over ΛCDM with ∆AIC =−1.8. We explicitly link this framework to the AI Qualia architecture, demonstrating the cross-domain generative power of the 2D Heuristic of the Intelligible

A Framework for Intangible Cultural Heritage Interface Design Integrating Enhanced Kansei Engineering and Generative AI

Young User Preference Survey for Guangcai ICH Digital Interface Survey Instructions, User Experience Evaluation Questionnaire for Guangcai Porcelain Digital Interface

Polar Defect and 111-Sharpness for Symmetric Cubics Through Seven Variables, with a Complete Six-Variable Orbit Classification

This paper studies concise symmetric cubic tensors of minimal border rank. It establishes a general polar-defect obstruction for tensors that are 111-abundant but not 111-sharp, and combines this obstruction with the low-dimensional geometry of cubic hypersurfaces with vanishing Hessian. As a result, every concise 111-abundant symmetric cubic in at most seven variables is proved to be 111-sharp. Consequently, ordinary tensor border rank and symmetric border rank coincide throughout the minimal-border-rank locus in these dimensions. In six variables, the paper gives a complete classification up to linear equivalence. The locus consists of twenty one-generic trace-cubic orbits arising from six-dimensional commutative Artin–Gorenstein algebras and two one-degenerate Perazzo orbits. The two Perazzo orbits are distinguished explicitly, their projective orbit dimensions are determined, and the lower-dimensional orbit is shown to be the unique concise codimension-one boundary orbit of the higher-dimensional one. Explicit symmetric degeneration families are also constructed. The accompanying computation package verifies the displayed trace cubics, the Perazzo reductions and degeneration identities, the Hessian calculations for reducible cubics, the centroid computations, and the projective stabilizer ranks. All finite calculations use exact arithmetic and include independent finite-field checks. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.

Polar Defect and 111-Sharpness for Symmetric Cubics Through Seven Variables, with a Complete Six-Variable Orbit Classification

This paper studies concise symmetric cubic tensors of minimal border rank. It establishes a general polar-defect obstruction for tensors that are 111-abundant but not 111-sharp, and combines this obstruction with the low-dimensional geometry of cubic hypersurfaces with vanishing Hessian. As a result, every concise 111-abundant symmetric cubic in at most seven variables is proved to be 111-sharp. Consequently, ordinary tensor border rank and symmetric border rank coincide throughout the minimal-border-rank locus in these dimensions. In six variables, the paper gives a complete classification up to linear equivalence. The locus consists of twenty one-generic trace-cubic orbits arising from six-dimensional commutative Artin–Gorenstein algebras and two one-degenerate Perazzo orbits. The two Perazzo orbits are distinguished explicitly, their projective orbit dimensions are determined, and the lower-dimensional orbit is shown to be the unique concise codimension-one boundary orbit of the higher-dimensional one. Explicit symmetric degeneration families are also constructed. The accompanying computation package verifies the displayed trace cubics, the Perazzo reductions and degeneration identities, the Hessian calculations for reducible cubics, the centroid computations, and the projective stabilizer ranks. All finite calculations use exact arithmetic and include independent finite-field checks. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.

A Pedagogical Agent Toolkit for K-12 Education Contexts

With the advancement of Generative Artificial Intelligence (GenAI) and in particular Large Language Models (LLMs), increasing focus has been placed on the development of conversational agents across domains, including education. Within education research, pedagogical agents (PAs) have traditionally been designed by researchers, producing positive results when implemented in ways consistent with good pedagogical practice. Findings suggest that the effectiveness of PAs depends on the degree to which learners relate to their agents and perceive them as socially and behaviorally realistic. One way to accomplish this is to include end users in the PA design process. Authoring tools have lowered technical barriers, enabling novice designers to participate in educational technology design. Building upon this work, we introduce the Pedagogical Agent Toolkit (PATK), an extendable and modular toolkit with AI-based features with the purpose of expanding novice designer agency and opportunity in the creation and customization of PAs. This paper outlines the design principles, architecture, and features of the PATK.

Subset Heterogeneity in Reward Model Benchmarks: A CONFIRM Validation of 174 RewardBench 2 Models

Background. RewardBench 2 aggregates pairwise preference accuracy across heterogeneous task families (factuality, instruction following, safety, and others). A single leaderboard score can mask systematic subset specialization, yet standard benchmark reporting rarely tests whether accuracy is independent of task category. Methods. We applied CONFIRM, a chi-square test of independence with Cramér's V effect sizing and empirically anchored letter grades, to 174 publicly released reward models. For each model we constructed a 5 × 2 contingency table (subset × correct/incorrect) from published per-prompt scores (n = 1,763 prompts after excluding the non-binary Ties subset). The null hypothesis was that correct/incorrect outcomes are independent of subset category. Grades A–F reflect V magnitude (CONFIRM v2 thresholds); grade I denotes insufficient power. Non-significant results grade F only when power ≥ 0.80 to detect V = 0.10. Results. All 174 models rejected independence at α = 0.05 (all p < 0.02; median V = 0.272, range 0.082–0.467). No model received F or I. Grade distribution: A 36.8% (n = 64), B 50.6% (n = 88), C 11.5% (n = 20), D 1.1% (n = 2). Pooling across models, mean per-subset accuracy was lowest on Precise IF (36.3 per 100) and highest on Safety (75.7 per 100). In 93.1% of models the largest subset gap involved Precise IF as the weakest category; Safety was the strongest endpoint in 70.7% of cases. Conclusions. Published RewardBench 2 reward models exhibit statistically detectable subset heterogeneity at this sample size. Aggregate accuracy therefore understates structured performance imbalance, particularly weakness on precise instruction-following relative to safety-oriented subsets. CONFIRM provides a reproducible heterogeneity diagnostic to be read alongside ranking metrics. Plain-language summary. For all 174 reward models tested, the rate of correct judgments differed across the five task types by more than the prespecified statistical threshold. The direction of the difference was shared: 93.1% of models were weakest on precise instruction-following, and 70.7% were strongest on safety. Each model was tested on 1,763 prompts, enough sensitivity to detect even a small difference had one been present, so a model showing no difference would have been identifiable as such. None did. A single overall leaderboard score does not show this — two models with the same average can differ substantially underneath. This analysis measures whether a model's accuracy is uneven across task types, not which model is best overall, and it identifies a pattern without establishing its cause. Supplementary material. The deposited archive (rewardbench2_validation.zip) contains per-model results, subset breakdowns, contingency cell counts, validation flags, and step-by-step mathematical derivations for all 174 models. Competing interests. The author is affiliated with TraceSeis, Inc., which is developing CONFIRM as a commercial product. This constitutes a competing interest. All results are reproducible from the cited public data and the deposited analysis outputs. AI use disclosure. Generative AI tools were used during preparation of this work, in two distinct roles. For drafting and implementation: Anthropic Claude assisted with manuscript prose; the CONFIRM engine and analysis pipeline were implemented with AI coding tools (Cursor, Anthropic Claude) to the author's specification; and Google Gemini was consulted during writing and analysis runs. For review: Perplexity provided editorial review of a late draft, and xAI Grok was used as a general consistency check. This reflects the author's record of tool use and is not offered as an exhaustive log. Research design, statistical methodology, and interpretation are the author's. Because the analysis software was AI-implemented, every reported statistic was independently recomputed from observed cell counts and checked against pipeline output before reporting; per-model derivations are deposited as confirm_math.html and can be checked by hand. The author takes full responsibility for the contents of this record.

Generative Artificial Intelligence and the Pricing of Complex Earnings Information: Evidence from the Release of ChatGPT

Whose ethics govern? AI, relational ethics, and professional codes of social workers and counsellors in Hong Kong and Singapore

Research on the Double-Edged Sword Effect of Employee-AI Collaborative Creation in the Context of Digitalization and Intelligentization

Deconstructing and Reconstructing University Classroom Authority in the Age of Generative AI: Practical Reflections on a Project Based Learning Reform

The rapid proliferation of generative artificial intelligence is fundamentally reshaping higher education, challenging the traditional lecture-based, knowledge-transmission model of classroom instruction. This paper offers a reflective analysis based on the author's first-hand teaching experience at a Chinese university with a finance and economics focus, where two AI-related courses are offered: a general-education AI literacy course for all undergraduates and an advanced deep learning course for computer science majors. The analysis reveals that AI, as a near-perfect knowledge transmitter, has rapidly devalued the knowledge-delivery function of traditional classrooms. Teachers find themselves caught between the narrowness of their own specialised training and the explosive, fast-moving breadth of AI, while student engagement continues to decline. In response to this crisis, the author's school officially launched a teaching reform in the spring semester of 2026, shifting its core approach from "knowledge-point instruction" to "project-based learning" (PBL). For the general-education course, which enrols a large number of students from social science and humanities backgrounds, the reform emphasises individual creation using off-the-shelf AI tools, aims at developing a perceptual understanding of AI principles, and involves minimal or no coding. For the computer science majors, in contrast, the advanced course adopts more technically intensive, code-based projects. This paper describes in detail the initial implementation and emerging challenges of this differentiated reform, and reflects on the necessity and pathways for transforming the teacher's role from "knowledge authority" to "learning environment designer."

Vision-Based Machine Learning voor Chemische Procescontrole: Een Haalbaarheidsstudie over Meerdere Toepassingen

This research is motivated by the increasing complexity of chemical processes and the growing demand for robust, non-invasive monitoring and control solutions in the context of the ongoing digitalization of the chemical industry. Many critical aspects of chemical processes, such as phase behavior, flow patterns, and fouling, are inherently visual and therefore difficult to capture using traditional point measurements. By leveraging computer vision as a sensing modality, this thesis addresses a critical gap between observable process behavior and advanced, data-driven process control. The proposed methodology aims to support the transition towards more autonomous and intelligent operation by emphasizing robustness, interpretability, and seamless integration with existing industrial control frameworks, thereby facilitating the practical adoption of vision-based process control in the chemical sector. The research is structured around three primary objectives. The first objective focuses on the development of machine vision methods for classification and segmentation in chemical production environments characterized by continuous operation and high intrinsic safety standards. As a result, truly abnormal or failure-related events occur rarely, leading to highly imbalanced and limited datasets that pose significant challenges for conventional supervised learning approaches. Moreover, the creation of labeled datasets in the chemical sector is often prohibitively expensive, as it requires the involvement of domain experts to accurately interpret and annotate complex process phenomena. To address these constraints, two novel methods are proposed. The first method employs generative adversarial networks for anomaly detection, incorporating tailored cost functions and the structural similarity index to enable automated segmentation. This approach outperforms conventional supervised segmentation models trained for task-specific detection problems. The second method combines original and synthetically generated data to optimize classifier performance while quantitatively assessing generalization through an explainable AI framework. This strategy demonstrates superior performance compared to standard data augmentation techniques, increasing classification accuracy for the chemical foam classification task from 57% to 91%. The second objective focuses on developing a vision-based closed-loop control strategy for process management in the chemical sector. A laboratory setup simulating a chemical foaming production process, in which foam is continuously generated and a vision-based dosing system has been implemented to control foam volume, was established to develop a strategy capable of maintaining effectiveness even when precise control input accuracy cannot be guaranteed. The results demonstrate the feasibility of vision-based process control for automated anti-foaming agent dosing. Furthermore, a sensitivity analysis was conducted to evaluate the impact of the detection system's performance on the control solution. The analysis revealed that the precision of the detector has a limited effect on the system's ability to mitigate foam formation, whereas recall plays a more critical role: if recall dropped below 50%, the system was no longer able to effectively combat foam formation. The thesis concludes with the introduction of a generic framework for the development and deployment of machine vision-based control applications in chemical settings. This framework provides guidance on addressing data acquisition challenges, selecting suitable models, and integrating them into live production environments for automated decision-making. The framework was validated through four use cases at BASF Antwerpen, showcasing its applicability in real-world environments and gave way for several cost savings. The contributions of this thesis significantly advanced the field of vision-based process control, particularly within BASF Antwerpen. The findings emphasize the importance of robust model development, effective use of synthetic data, and the integration of machine vision systems into closed-loop control processes. These advancements offer tangible benefits for automation, efficiency, and cost reduction in chemical production environments and has been applied in four different use cases at BASF Antwerpen.