The rapid development of LLMs has accelerated the adoption of GenAI across multiple sectors. Among current applications, GenAI-powered virtual assistants represent the most prominent and widely implemented use case in public sector. However, there is currently little research that systematically maps the functions, anticipated effects and trends of these emerging tools in public sector. Addressing this, this study maps and analyses 39 GenAI assistant deployments across European public administrations, examining their functional characteristics and anticipated public value effects. Based on publicly available documentation, the findings show that citizen-facing assistants are primarily designed for information provision and service guidance, with expected effects focused on improved availability, accessibility, convenience, responsiveness, efficiency, cost and time savings, and fairness, etc. Internal assistants mainly support knowledge retrieval and document workflows, aiming to enhance administrative efficiency, productivity, resource management, and institutional capacity and processes, etc. However, expected effects related to trust and institutional legitimacy are not consistently supported in reported outcomes, highlighting the need for deeper empirical and qualitative investigation on public value effects and for governance frameworks that enable responsible GenAI integration in public sector.
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate Are the conclusions supported by the data? Somewhat supported Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly Is the preprint likely to advance academic knowledge? Moderately likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, but it needs to be improved Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Yuchen Song· Zenodo (CERN European Organi...· 0 citations
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate Are the conclusions supported by the data? Somewhat supported Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly Is the preprint likely to advance academic knowledge? Moderately likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, but it needs to be improved Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Yuchen Song· Zenodo (CERN European Organi...· 0 citations
Description: This dataset accompanies the research article titled "When AI Improves the Text but Changes the Voice: Generative AI, Authorial Identity, and Authenticity in EFL Writing." The repository contains a comprehensive set of anonymized data and analysis materials investigating the tension between AI-mediated linguistic optimization and authorial voice. Contents include: Anonymized Corpus: Paired L2 essays (original independent versions vs. GPT-4o revised versions) from 60 advanced EFL learners. Quantitative Metrics: Linguistic features including MTLD (Measure of Textual Lexical Diversity), T-unit analysis, and stance marker frequencies. Psychological Scales: Raw and processed scores from the Authenticity Gap Scale (AGS). Analysis Scripts: R and Python code used for Linear Mixed-Effects Models (LME) and stylistic homogenization analysis. Research Instruments: The standardized prompt used for AI revision and the semi-structured interview protocol. Keywords: Generative AI, L2 Writing, Authorial Identity, Authenticity Gap, Linguistic Agency, EFL.
Pegah Merrikhi· Zenodo (CERN European Organi...· 0 citations
Qualitative results reflected a paradox in students' perceptions of AI; despite appreciating how AI facilitated skills through the generation of ideas and opportunities for safe practice, students simultaneously expressed concern about cognitive offloading and skill atrophy.
Ibtihal Murad Zangana· مجلة الفتح للبحوث التربوية و...· 0 citations
This work contributes both a reusable technical architecture and methodological insights for accessible metadata generation in open data ecosystems, with particular attention to cross-lingual prompting strategies and the limitations of the DCAT-AP.
This OSF project documents the Responsible AI-Augmented Judgment (RABJ) study, a multilevel field study examining productive cognitive friction and responsible human judgment under two active generative artificial intelligence (GenAI) conditions in higher education. The study involved 120 undergraduate students organized into 24 pre-existing teams across four course sections and two disciplinary contexts—Business and Engineering and Sciences. The repository provides the study documentation, authorized de-identified numerical data, measurement and scoring materials, technical-validation outputs, and reproducible analytical resources associated with the RABJ research program. Materials are organized to distinguish raw-like de-identified data, processed analysis-ready data, aggregate outputs, instruments and scoring documentation, and reproducibility resources. The public repository excludes direct identifiers, student-generated text, submitted student work, original institutional linkage keys, and the restricted master workbook. Public materials are released subject to institutional governance, disclosure-risk controls, and documented reuse conditions. RABJ is treated as a provisional multidimensional framework represented by a reliable overall indicator and theoretically specified domains. The study also includes neutral individual performance assessment, team-level rubric evaluation, expert content validation, numeric qualitative coding, implementation-fidelity documentation, cluster-aware analytical procedures, and exploratory analyses of performance–self-appraisal patterns. This project is intended to support transparent documentation, reproducibility, secondary analysis, methodological reuse, and future research derived from the RABJ study.
Roberto Gómez Tobías· Open Science Framework· 0 citations
This item presents a Research Topic Bank and Roadmap for English Language Education in the Generative AI Era (2026–2030), developed to support undergraduate (S1) and master’s (S2) research in English Language Education. It contains 50 undergraduate research topics and 30 master’s thesis topics organized around six interconnected areas: AI-aware assessment design, learner authorship and provenance, validity and score meaning, fairness and language equity, AI detection and academic integrity, and responsible AI governance. Each topic is linked to relevant research references and potential methodological directions. The roadmap is intended to support systematic, progressive, and context-responsive research, particularly for multilingual EFL learners and English Language Education in Indonesian higher education. The item was prepared by Abdul Syahid as a research development resource for 2026–2030.
This white paper examines the widening ideological divide between young men and young women in Generation Z, arguing that it cannot be understood solely as an organic political or sociological development. It analyzes how attention-maximizing platforms, recommendation systems, generative AI and commercialized digital intimacy can exploit mating anxieties, sexual gratification and tribal defence mechanisms, directing users into increasingly adversarial informational and relational environments. Drawing on behavioural economics, cognitive neuroscience, game theory and legal analysis, the paper traces a cross-platform pipeline extending from algorithmic rage bait and ideological priming to AI-mediated intimacy, companion systems and automated customer-relationship infrastructures. It models competition for finite human attention as a non-cooperative game in which polarizing, high-variance content becomes a commercially stable strategy—even without an explicit intention to radicalize. The paper also examines the limitations of the EU AI Act, product-liability doctrines and content-focused interventions when harm arises from optimization incentives rather than an identifiable prohibited purpose. It concludes with a strategic framework for changing those incentives, strengthening algorithmic product-liability audits and rebuilding physical, non-digitized spaces for human interaction. Its central contention is that AI does not merely reflect the generational gender fracture: under the prevailing incentives of the attention economy, it can participate in manufacturing and accelerating it.
Mariano Pablo Limongi· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel method for reconstructing complex topological structures from a set of modular building blocks, utilizing advanced pattern recognition and generative algorithms. The core principle centers on the creation of a "Semantic Topological Bridge" – an AI system that analyzes relationships between modular elements and automatically generates a bridge that connects them, enabling the formation of higher-level topological structures. This approach addresses the challenge of translating abstract topological concepts into concrete, usable representations, offering a significant advancement over traditional topological modeling techniques. The paper details the methodology, demonstrates its efficacy through illustrative examples, and explores the potential applications of this technology across diverse fields.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.
Alifah Widya Rachmawati, Syamsul Hadi, Eni Purnasari et al.· INTERNATIONAL JOURNAL OF ECO...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
