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
Generative AI can be utilized for EFL teaching to develop higher-order thinking skills, but it may also present challenges in academic integrity. Little is known about the combined influence of DeepSeek over three crucial factors of critical thinking, creativity, and self-reflection in the Iraqi EFL context. Therefore, this study seeks to bring to light how these skills are affected by the assistance of DeepSeek qualify students' impressions of the learning process. Qualitative methods and analyses: The researcher used a sequential explanatory design. The subjects of the study were 200 undergraduate students who were randomly split into two groups (experimental group [N = 100]; control group [N = 100]). Data were collected by means of pre-validated scales: Critical Thinking, Reflective thinking Creative thinking. Additionally, the researcher obtained qualitative data utilizing a semi-structured interview guide. As for the quantitative data they were analyzed with ANCOVA. As for the analyses of qualitative data thematic coding was utilized. Significant differences were found in measures of critical, creative, and reflective thinking scores between the DeepSeek group and the control group. 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
Open data portals face a persistent challenge: datasets and their metadata often remain incomprehensible to non-specialist audiences, limiting the democratic potential of open government data. While recent advances in large language models offer promising approaches to automated description generation, their application to multi-level accessible descriptions of structured datasets, particularly in non-English contexts, remains underexplored. This paper presents the design, implementation, and preliminary evaluation of a modular pipeline that generates dataset descriptions at three accessibility levels using locally deployed language models. The system integrates deterministic dataset profiling (Frictionless Data and ydata-profiling) with generative AI, employing Qwen3.5-9B for description generation and an initial two-model evaluation setup for automated quality assessment (Prometheus 2 and Mistral 7B), which was later critically examined against human expert judgement. We address practical constraints of public sector deployment, including reproducibility, digital sovereignty through local model execution, and alignment with operationalized criteria derived from German accessibility standards. Our implementation processes datasets from GovData.de, generating descriptions conforming to DIN 8581-1 (Plain Language German), DIN SPEC 33429 (Easy Language German), and Standard German for general public audiences. 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.de metadata standard.
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
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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
Purpose - Examining how the interaction between Artificial Intelligence (AI) capabilities and organizational leadership dynamics can strengthen business strategic decision-making processes.
Design/methodology/approach – A Systematic Literature Review (SLR) methodology following the PRISMA protocol was employed. Data were screened from the Scopus database up to July 2026, resulting in a final cohort of 30 English-language scholarly journal articles for qualitative analysis.
Originality - Introducing the concept of "Algorithmic Bounded Rationality" to explain AI limitations (such as data bias, contextual rigidity, and factual inaccuracies) and developing a "Triple Helix" model that emphasizes the importance of synergy between machine precision and human cognitive curation (human-in-the-loop governance).
Findings and Discussion – The effectiveness of AI is not technologically deterministic; AI capabilities—ranging from predictive analytics to generative models—require organizational mediators such as ethical leadership, a digital mindset, and strategic ambidexterity. AI acts as a cognitive prosthesis that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders. Most literature focuses on quantitative or conceptual approaches, while in-depth, case-study-based qualitative research remains very limited.
Conclusion – AI does not replace the role of executive leaders; instead, it serves as a cognitive aid. Organizations that successfully integrate AI's computational precision with ethical and responsive human leadership achieve the greatest competitive advantage.
Keywords – Artificial Intelligence, Leadership Decision Making, Dynamic Managerial Capabilities, Algorithmic Bounded Rationality, Human AI Complementarity
Alifah Widya Rachmawati, Syamsul Hadi, Eni Purnasari et al.· INTERNATIONAL JOURNAL OF ECO...· 0 citations
Generative artificial intelligence (GenAI) has sharply reduced the cost of producing fluent explanations, syntheses, analyses, code, and recommendations, but it has not reduced the intellectual work required to establish whether those outputs deserve belief or use. This asymmetry creates a plausibility-verifiability gap: an AI-generated artifact can display the linguistic and structural markers of expertise while the evidence, provenance, and limitations needed to warrant reliance remain difficult to inspect. Existing AI literacy and evaluative-judgement frameworks specify broad competencies for critical and responsible engagement, yet students and novice users still need an actionable method for evaluating a particular AI-mediated artifact. To address this gap, this conceptual paper introduces and theoretically grounds the PEARLS framework, an artifact-level verification protocol organized around six interdependent dimensions: Process, Evidence, Access, Reproducibility, Legitimacy, and Source. The framework integrates insights from epistemic cognition, epistemic vigilance, cognitive offloading, calibrated trust, evaluative judgement, and open-science principles to treat AI output as a provisional knowledge claim whose warrant must be assembled and examined. It further advances verification-driven learning as a pedagogical mechanism through which learners develop expertise by iteratively focusing consequential claims, tracing and testing their warrant, judging uncertainty and legitimacy, acting on the results, and reframing subsequent inquiry. Five interdisciplinary cases - psychology theory, educational statistics, computer science, history, and health sciences - demonstrate how the relative emphasis of the six dimensions varies with disciplinary standards and the consequences of error. The paper concludes by deriving implications for assessment design and proposing a research agenda encompassing construct validation, intervention studies, disciplinary calibration, equity, and human-AI interface design. PEARLS is therefore offered as a theoretically informed and practically usable scaffold for preserving human epistemic responsibility as knowledge production becomes increasingly AI-mediated.
The emergence of generative artificial intelligence has introduced a new technological presence into religious life, capable of producing sermons, prayers, hymns, biblical commentary, devotional material, and liturgical text, and of assuming visible roles in worship through avatars and synthetic voices. This article critically examines the place of AI in Christian worship from a Christian and Reformed theological perspective, asking whether such systems can be said to mediate the Divine. It distinguishes between technological mediation, religious mediation and theological mediation, and argues that AI can mediate religious information, facilitate religious experience and assist human participation in worship, but cannot mediate the Divine in the Christological and soteriological sense in which Christian theology understands divine mediation. Drawing on Christian doctrine, Reformed theology of worship, recent scholarship on AI and religion, and empirical evidence from AI-led worship, the article develops a four-level framework for evaluating AI participation in worship: instrumental assistance, ministerial assistance, delegated religious agency and liturgical substitution. It further proposes a five-part theological matrix (agency, authority, accountability, embodiment and mediation) for assessing specific applications, and six principles for a theology of responsible AI-assisted worship. The article concludes that AI may legitimately function as an instrument within worship when its use is transparent, accountable, subordinate to Scripture and ecclesial authority and directed toward human flourishing, but that the Church should resist treating AI as a spiritual subject, an independent religious authority, a sacramental agent or an alternative mediator between God and humanity.
Godswill Ome Ufere· Zenodo (CERN European Organi...· 0 citations
The emergence of generative artificial intelligence has introduced a new technological presence into religious life, capable of producing sermons, prayers, hymns, biblical commentary, devotional material, and liturgical text, and of assuming visible roles in worship through avatars and synthetic voices. This article critically examines the place of AI in Christian worship from a Christian and Reformed theological perspective, asking whether such systems can be said to mediate the Divine. It distinguishes between technological mediation, religious mediation and theological mediation, and argues that AI can mediate religious information, facilitate religious experience and assist human participation in worship, but cannot mediate the Divine in the Christological and soteriological sense in which Christian theology understands divine mediation. Drawing on Christian doctrine, Reformed theology of worship, recent scholarship on AI and religion, and empirical evidence from AI-led worship, the article develops a four-level framework for evaluating AI participation in worship: instrumental assistance, ministerial assistance, delegated religious agency and liturgical substitution. It further proposes a five-part theological matrix (agency, authority, accountability, embodiment and mediation) for assessing specific applications, and six principles for a theology of responsible AI-assisted worship. The article concludes that AI may legitimately function as an instrument within worship when its use is transparent, accountable, subordinate to Scripture and ecclesial authority and directed toward human flourishing, but that the Church should resist treating AI as a spiritual subject, an independent religious authority, a sacramental agent or an alternative mediator between God and humanity.
Godswill Ome Ufere· Zenodo (CERN European Organi...· 0 citations
Universities are having three separate conversations about the same question: one about student devices, another about generative artificial intelligence (GenAI) in assessed work, and a third, unspoken, that leaves instructors to decide both at their own professional risk. The question underneath: does the technology serve the formation of a capacity, or do the work in the student’s place? This paper holds the three as one, at the level where curricular design happens: the programme. On a refrain-then-amplify design, a programme withholds a generative tool while a capacity is forming, then restores it to amplify that capacity once the student can direct it, judge what it returns, and answer for it. Devices are allowed where they support engaged work, excluded where they drain attention. Both sit in a single floor beneath every course, the first governed by a forming-versus-offloading criterion: whether a stretch of work forms a capacity or puts it through the tool. The programme fixes outcomes and integrity, reserving teaching method to the instructor, and places a hard-to-fake checkpoint at each refrain-to-amplify hinge. Each element has published precedent: the withholding, the taught restoration, the test. None of that work owns the movement at programme level, with a verified transition.
Manuel Torres-Sahli, Jorge Blake, Ángela Novoa‐Echaurren et al.· 0 citations
