generative ai

Beyond simple structures: a bifactor-ESEM Approach to understanding student acceptance of generative AI in higher education

The Hybrid Paradox of the Modern Knowledge Worker / Das Hybrid-Paradoxon des modernen Geistesarbeiters

This bilingual paper (English/German) addresses the existential dilemma of the modern knowledge worker in the age of generative AI, balancing immense efficiency gains against the risk of stylistic flattening and factual hallucinations (as proven by recent data in The Lancet, May 2026). Written from the perspective of an avowed beginner, it proposes a symbiotic hybrid methodology where AI acts as a cognitive exoskeleton while the human retains intellectual sovereignty, aesthetic curation, and critical validation. It includes an analysis of theoretical pioneers (Mollick, Marcus, Mitchell), reflections on poetry (Rilke), a deep dive into linguistic forensics (perplexity and burstiness), and an extensive conceptual glossary.Dieser zweisprachige Aufsatz (Deutsch/Englisch) beleuchtet das existenzielle Dilemma des modernen Geistesarbeiters im Zeitalter generativer KI. Er wiegt immense Effizienzgewinne gegen die Risiken wirklicher Verflachung von Qualität und Stil ab (belegt durch aktuelle Daten in The Lancet, Mai 2026). Geschrieben aus der Perspektive eines erklärten Anfängers, wird eine symbiotische Hybrid-Methodik vorgeschlagen: Die KI agiert als kognitives Exoskelett, während der Mensch die intellektuelle Souveränität, die ästhetische Kuratierung und die kritische Validierung behält. Die Arbeit enthält eine Analyse theoretischer Vordenker (Mollick, Marcus, Mitchell), Reflexionen zur Dichtkunst (Rilke), einen Einblick in die linguistische Forensik (Perplexität und Burstiness) sowie ein umfassendes Begriffsglossar.

Teacher-Facing AI in Education: A Systematic Review of Impact and A Taxonomy of Teacher-AI Teaming

Artificial intelligence (AI) is increasingly integrated into educational practice, promising to enhance teaching and learning. Yet delegating pedagogical tasks to AI raises concerns about teacher deskilling, erosion of professional judgement, and diminished agency. Drawing on the teacher-AI teaming taxonomy, we conducted a systematic review of 103 studies of teacher-facing AI tools across educational contexts to analyse system capabilities, patterns of interactions and influences on teaching practice. Our analysis reveals that advanced technical capabilities of contemporary AI models were utilised at lower transactional teaming levels for automating narrow instructional functions. Situational and operational teaming, which supports teacher awareness and teacher-directed goal execution, are also common in Generative AI in education literature and have demonstrated complementary benefits. However, available evidence relies on student-focused measures, whereas the augmented impacts, particularly on teaching effectiveness, remain underexplored. In contrast, higher forms of teaming as praxical and synergistic teaming, which afford co-adaptation and structured co-reasoning, promoting reflective professional practice, remain rare. These findings suggest that current teacher-facing AI systems have yet to fully leverage AI to strengthen teacher agency. We conclude the paper with recommendations on how to realise such forms of teaming, which require advances not only in model capability but also in interaction design that support transparent reasoning, teacher-controllable interfaces, and sustained teacher participation in system development.

Towards Responsible Generative AI in Education: Developing an Ethical and Human-Centric Framework

This paper observes the ever-growing role of generative artificial intelligence (Gen AI) in education.It highlights the emergence of tools like ChatGPT and the usage of said tools by all stakeholders including students, teachers, administrators and educational institutions.It explores the various policies on using Gen AI in education worldwide.The study also discusses the ethical considerations with responsible use of Gen AI and advocates for the need of a policy dedicated to regulate the use of Gen AI in education.This study also proposes a human-centric framework for responsible use of Gen AI in education.

The Economics of AI Governance: Evaluating Data Sovereignty, Platform Monopolies, and the Digital Personal Data Protection Act (DPDPA)

This paper evaluates the economic and strategic implications of generative artificial intelligence architectures on market competition and digital asset ownership in 2026. Focusing on the intersection of platform economics and data governance, the study examines how modern foundation models create market asymmetries by training on vast human-generated datasets. Through an economic analysis of regulatory interventions like India's Digital Personal Data Protection Act (DPDPA), we expose the market limitations of corporate terms-of-service in addressing systemic data scraping. The paper exposes how current paradigms allow data monopolies to centralize digital capital. Finally, we propose a decentralized, private-by-default economic model that treats personal data as a high-value asset tied directly to individual digital identity, thereby shifting structural compliance costs back onto corporate developers.

From aesthetics to authenticity: a stimulus–organism–response model of audience responses to AI-generated intangible cultural heritage design

Generative AI is increasingly used in heritage visualization, yet its outputs often raise concerns about cultural authenticity. Prior studies have focused more on technical fidelity and symbolic preservation than on how audiences evaluate the authenticity of AI-generated heritage imagery. To address this gap, this study develops and tests a Stimulus-Organism-Response model using AI-generated Wuxi clay figurine images. A randomized between-subjects online experiment with 330 participants examined the associations of visual information quality, AI technical novelty, and symbol salience with perceived aesthetics, perceived authenticity, cultural identity, and acceptance intention. The theory-specified model showed that visual information quality, technical novelty, and symbol salience were positively associated with perceived aesthetics, while perceived aesthetics and symbol salience were positively associated with perceived authenticity. Perceived aesthetics and perceived authenticity were also associated with cultural identity and acceptance intention. However, the HTMT analysis indicated limited discriminant validity, particularly among visual information quality, technical novelty, symbol salience, and perceived aesthetics. A supplementary second-order model representing these four perceptions as facets of an overall perceived design quality factor showed acceptable fit and comparable downstream associations. In this alternative specification, perceived authenticity was no longer independently associated with acceptance intention. Accordingly, the construct-specific path estimates should be interpreted cautiously. Overall, the findings are consistent with the presence of a substantial holistic evaluative component in audience responses to AI-generated heritage imagery.

Chimie organique : réactions et mécanismes en fiches visuelles

Guide visuel des réactions et mécanismes en chimie organique, conçu pour les étudiants de Licence 3. Apprendre la chimie organique commence par la constitution d'un répertoire de réactions : réactifs, conditions, régiosélectivité, stéréochimie. Cette étape absorbe l'essentiel du temps de travail et laisse peu de place à ce qui compte vraiment — comprendre pourquoi une transformation se déroule ainsi. Ce guide a été conçu pour raccourcir cette phase de mémorisation et libérer du temps pour le raisonnement mécanistique. Chaque réaction est présentée sur une fiche visuelle au format homogène : schéma réactionnel, flèches de déplacement électronique, conditions opératoires, stéréochimie et points de vigilance. Contenu de cette version (v1.1)Réactions des alcènes et des alcynes : additions électrophiles, hydroboration, époxydation, dihydroxylation, coupure oxydante, hydrogénation catalytique. Document évolutif : d'autres familles de réactions seront ajoutées dans les versions ultérieures, déposées sur le même enregistrement Zenodo. Public viséÉtudiants de Licence 3 de chimie et parcours associés ; utile également en approfondissement de L2, en révision de M1, et pour tout travail en autonomie. AvertissementRessource pédagogique réalisée par un étudiant. Il ne s'agit pas d'une publication officielle de l'Université Côte d'Azur, qui n'en a ni validé ni approuvé le contenu. En cas de divergence avec le cours, c'est l'enseignement officiel qui fait foi. Les erreurs éventuelles relèvent de la seule responsabilité de l'auteur. Utilisation de l'intelligence artificielleUn assistant d'intelligence artificielle a été utilisé de manière substantielle lors de la préparation de ce document : résolution des exercices, rédaction des explications et mise en forme. L'auteur a relu, corrigé, réorganisé et enrichi l'ensemble du matériel, et enassume la responsabilité scientifique. Cette mention figure par souci de transparence : elle n'exonère pas le lecteur de son propre esprit critique, que ce guide cherche précisément à cultiver. Signaler une erreur : Ce guide est appelé à évoluer. Toute remarque, correction ou suggestion est bienvenue et sera créditée dans les versions ultérieures : jessedare7@gmail.com ENGLISH SUMMARY A visual study guide to organic reactions and mechanisms, written in French for third-year undergraduate chemistry students. Each reaction is presented as a single-page visual sheet: reaction scheme, curly-arrow electron pushing, conditions, stereochemistry and common pitfalls. Version 1.1 covers alkene and alkyne reactions; further reaction families will be added in later versions. Student-created educational resource; not an official publication of Université Côte d'Azur. Generative AI was used substantially in preparation; all content was reviewed, corrected, reorganised and improved by the author.

Regulatory Gap: Why Model Risk Management Is Structurally Ill-Suited to Govern AI-Driven Code Transformation

Model Risk Management (MRM), set out in the Federal Reserve's SR 11-7 and OCC Bulletin 2011-12, governs quantitative models in banking. AI vendors now offer tools for rewriting production code, including COBOL-to-Java modernization, raising a scope question for the estimation-oriented definition of a model. Through structured assumption-violation mapping, this paper identifies five structural gaps in relying on MRM alone: definition, validation, documentation, monitoring, and third-party risk management. Classifying an underlying LLM as a model leaves a separate task of specifying assurance for the particular software transformation it produces. The paper proposes a complementary Transformation Risk Management (TRM) framework organized around behavioral provenance, scoped functional-equivalence certification, transformation audit trails, rollback architecture, and concentration risk assessment. August 2026 update: The March public version identified the boundary between model risk management and AI-driven code transformation before SR 26-2 was issued on 17 April 2026. The revised guidance expressly excludes generative and agentic AI and points institutions to other risk-management and governance practices. The update preserves the March five-gap analysis and TRM proposal, records that subsequent scope response, and distinguishes the resolved scope-clarification question from the remaining transformation-governance questions.

Generative AI chatbots could take over our emotional lives: The potential and challenges of generative AI for health globally

Predicting Teachers' Behavioral Intention to Adopt Generative AI in Teaching: An Integrated TAM-UTAUT Regression Model

Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.

Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.

The Effects of Self-Learning Generative AI NPCs on Player Game Immersion and Loyalty: The Mediating Role of Game Immersion

With the growing use of generative artificial intelligence in digital games, whether AI-driven non-player characters (NPCs) can improve player experience and further shape continued play intention has become an important question for game design and user research. This study examines whether self-learning generative AI NPCs influence game loyalty through game immersion. A quasi-experimental between-subjects design and a retrospective questionnaire were used to compare players who had interacted with self-learning generative AI NPCs in the past three months with players who had only interacted with traditional scripted NPCs. Based on 195 valid responses, the AI NPC group reported significantly higher game immersion and game loyalty than the traditional NPC group. Mediation analysis using PROCESS Model 4 further showed that game immersion significantly mediated the relationship between NPC type and game loyalty. The findings suggest that the value of generative AI NPCs lies not only in the technology itself, but also in whether adaptive and natural interaction can enhance immersion and thereby relate to continued play and recommendation intention. Given the non-random grouping and self-reported data, the findings should be interpreted cautiously.