generative ai

A Comparative Study on Portrait Photography Implementation of Image Generative AI -Focusing on Chat GPT and Gemini-

Algorithmic Authorship, Data Sovereignty, And Intellectual Property Rights: Navigating the Intersection of Law, Science, And Society in the

Abstract The rapid convergence of artificial intelligence (AI), data science, and legal frameworks has created a profound crisis within global and domestic Intellectual Property Rights (IPR) regimes. Traditionally, copyright and patent laws were constructed around the central premise of human agency, recognizing intellectual labor as an extension of human dignity and personality. However, the rise of Generative AI platforms, machine learning models, and autonomous algorithmic systems disrupts foundational legal principles including authorship, inventiveness, originality, and infringement. This paper examines the multidisciplinary intersection of law, computer science, and social sciences regarding IPR. It deconstructs three critical dilemmas: (1) the legal status of AI-generated works and the "human author" requirement under copyright law; (2) the patentability of AI-invented subject matter and the doctrine of the "Person Having Ordinary Skill in the Art" (PHOSITA); and (3) the socio-economic implications of training data scraping, digital commons, and data sovereignty. By analyzing statutory provisions, recent judicial precedents across jurisdictions, and socio-legal frameworks, this study highlights the inadequacy of existing legal doctrines to address non-human innovation. The paper proposes a balanced normative framework incorporating a sui generis legal model for AI outputs, compulsory licensing for dataset training, and transparent algorithmic disclosure to foster technological innovation while protecting human creators and public domain integrity.

Reconfiguration of Artistic Experience through Generative AI: Focusing on Soundscape Creation

본 연구는 생성형 AI

Artificial Intelligence and Cyber Law in India: Recalibrating Legal Responsibility in The Age of Generative AI

Abstract This paper examines the challenges posed by Generative Artificial Intelligence (AI) to cyber law and legal responsibility in India. It argues that the rapid generation and dissemination of synthetic text, images, audio and video complicate traditional approaches to responsibility, particularly where developers, deployers, users and intermediaries exercise different degrees of control over AI-related risks. The study analyses the interaction of the Information Technology Act, 2000, the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, and the Digital Personal Data Protection Act, 2023, while considering data governance, intermediary liability, AI-generated digital evidence, cybersecurity, transparency and professional responsibility. It proposes a risk-sensitive, lifecycle-based framework in which legal duties correspond to the degree of control, foreseeable harm and institutional responsibility. Particular emphasis is placed on human oversight in AI-assisted adjudication, verification of AI-generated legal material, provenance of synthetic content, privacy and security by design, effective remedies, and institutional documentation through AI-use registers for high-impact applications. The paper concludes that India should promote responsible AI adoption through a human-centred cyber-law framework that balances technological innovation with privacy, authenticity, security and the integrity of legal institutions.

Generative AI Adoption for Image and Video Creation by Creators and Small Businesses in Emerging Markets: An Evidence Map and Early Platform Usage Study

Creators, freelancers, and small businesses in emerging markets are among the earliest and most intensive users of generative image and video tools, yet the academic evidence on their adoption remains fragmented across disciplines. This article contributes a two-part evidence base. The first part is a verified corpus of 850 unique academic works on generative AI adoption, assembled through 190 audited queries across OpenAlex, Crossref, and arXiv; every record is deduplicated, 838 carry a resolvable DOI, and 762 are confirmed by two or more independent bibliographic sources. Coverage analysis of the corpus quantifies a specific gap: only nine works simultaneously address emerging markets, image or video generation, and creator or small-business populations, and the intersection of video generation with small and medium-sized enterprises remains empty. The second part supplies behavioral evidence from a 45-day production window of Geramaker, a Brazilian pay-per-use platform for AI image, video, narration, and music generation, covering 2,605 accounts, 7,005 model calls, and 62 paid transactions. Adoption proves near-instant under free entry (median of one minute from signup to first output), purchase decisions compress to minutes on local instant-payment rails, and image-to-video animation of existing photographs dominates video demand. Retention, rather than activation, emerges as the binding constraint: 96% of users generate on a single calendar day. The article maps the observed drivers and inhibitors onto established adoption constructs and derives a research agenda for the under-studied intersection the corpus reveals.

Evaluating Technology Acceptance of Vernacular AI Interfaces: An Empirical Study Among Multilingual Engineering Students in Kasaragod

Generative Artificial Intelligence (AI) tools have become embedded in the everyday academic practice of undergraduate engineering students, yet most large language models remain optimised for standard English rather than the code-mixed, multilingual registers through which students in linguistically plural regions actually think and communicate. This study examines technology acceptance of vernacular and code-mixed AI interaction among 84 undergraduate engineering students enrolled in APJ Abdul Kalam Technological University (KTU)-affiliated institutions in Kasaragod district, Kerala, a region historically described as Saptha Bhasha Sangama Bhoomi, the confluence land of seven languages. Using a structured questionnaire grounded in the Technology Acceptance Model (Davis, 1989), the study measured Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Output Accuracy, and Linguistic Inclusion across five research hypotheses. Findings indicate that students from regional-medium secondary schooling backgrounds report significantly higher vernacular or code-mixed AI prompting than English-medium peers, chi-square(3, N = 84) = 22.91, p < .001. Perceived Usefulness correlates strongly with Perceived Ease of Use, r = .64, p < .001. Students who habitually use vernacular or code-mixed prompts report significantly higher ease of use than strictly English prompters, t(82) = 2.01, p = .048. Perceived terminological distortion is positively associated with reported reliance on AI-translated academic content, r = .27, p = .012, and native speakers of the unscripted Tulu dialect report markedly higher AI comprehension failure than speakers of scripted regional languages, t(79) = 11.60, p < .001. The results support all five hypotheses and highlight a persistent linguistic-inclusion gap in generative AI systems used within multilingual engineering classrooms. Implications for dialect-aware AI design and inclusive digital pedagogy in polyglot regions such as Kasaragod are discussed.

Large Language Models in the Acute Stroke Pathway: A Scoping Review of Applications, Evidence Maturity, and Implementation Readiness

Background. Large language models (LLMs) have been rapidly adopted in medicine since late 2022, yet their role in the time-critical acute stroke pathway—from symptom recognition and prehospital triage to emergency diagnosis, imaging-related text tasks, reperfusion decision support, and acute-phase documentation and communication—has not been systematically mapped. Existing reviews cover the whole stroke-care continuum or mix LLMs with traditional NLP, leaving the acute phase under-characterized. Objective. To map the applications, evidence maturity, and implementation readiness of LLMs across the acute stroke pathway. Methods. This scoping review follows the PRISMA-ScR guideline. We search PubMed/MEDLINE, Europe PMC (including preprints), and Google Scholar for studies published from November 2022 onward. Eligible studies center on LLMs/generative AI applied to any stage of the acute stroke pathway. Two reviewers independently screen records and chart data using a piloted form. Evidence is synthesized along two dimensions: five pathway stages (prehospital recognition/dispatch; emergency triage and differential diagnosis; imaging-related text tasks; reperfusion decision support; acute documentation and communication) and three evidence-maturity tiers (simulation/benchmark; retrospective real-world data; prospective deployment). Implementation barriers (hallucination, bias, privacy, regulation, liability, integration, cost) are thematically summarized. Registration note. This review is registered on OSF; the full protocol is available in the attached files.

The Borrowed Name: Counterfeit Sanctity, Artificial Intelligence, and the Architecture of the Final Deception

This research paper advances a novel constructive theological argument regarding the intersection of biblical eschatology and generative artificial intelligence (AI). Moving beyond traditional inquiries into the identity or chronology of the Antichrist, the author investigates the mechanism of deception described in New Testament corpora (Matthew 7, 2 Thessalonians 2, 2 Corinthians 11, and Revelation 13). Core ThesisThe paper identifies "Counterfeit Sanctity"—the weaponized mimesis of sacred language and divine invocation—as the central structural weapon of the eschatological deceiver. It argues that the final deception functions not through overt blasphemy or opposition to God, but through the sophisticated capture and impersonation of the Holy Spirit’s linguistic and phenomenological register. Technological SynthesisThe author identifies Large Language Models (LLMs) and generative heuristics as the first historical apparatus capable of realizing this mechanism at civilizational scale. By decoupling religiously fluent, spiritually authoritative speech from ontological character and pneumatic presence, generative AI allows for the manufacturing of "ownerless" sanctity. Key Contributions Exegetical Analysis: A synthesis of the "Lord, Lord" rejection in Matthew 7 with the "lying signs" of 2 Thessalonians 2. Patristic Grounding: Confirmation of the mimesis-of-the-sacred theory in the works of Irenaeus, Cyril of Jerusalem, and John Chrysostom. AI Epistemology: A structural comparison between the "disguise of light" (2 Cor. 11:14) and the output mechanics of generative systems. Practical Theology: A proposed "Pneumatological Epistemology" for the digital age, focusing on communal discernment (diakrisis), relational accountability, and the "Fruit Test" (Galatians 5).

The Borrowed Name: Counterfeit Sanctity, Artificial Intelligence, and the Architecture of the Final Deception

This research paper advances a novel constructive theological argument regarding the intersection of biblical eschatology and generative artificial intelligence (AI). Moving beyond traditional inquiries into the identity or chronology of the Antichrist, the author investigates the mechanism of deception described in New Testament corpora (Matthew 7, 2 Thessalonians 2, 2 Corinthians 11, and Revelation 13). Core ThesisThe paper identifies "Counterfeit Sanctity"—the weaponized mimesis of sacred language and divine invocation—as the central structural weapon of the eschatological deceiver. It argues that the final deception functions not through overt blasphemy or opposition to God, but through the sophisticated capture and impersonation of the Holy Spirit’s linguistic and phenomenological register. Technological SynthesisThe author identifies Large Language Models (LLMs) and generative heuristics as the first historical apparatus capable of realizing this mechanism at civilizational scale. By decoupling religiously fluent, spiritually authoritative speech from ontological character and pneumatic presence, generative AI allows for the manufacturing of "ownerless" sanctity. Key Contributions Exegetical Analysis: A synthesis of the "Lord, Lord" rejection in Matthew 7 with the "lying signs" of 2 Thessalonians 2. Patristic Grounding: Confirmation of the mimesis-of-the-sacred theory in the works of Irenaeus, Cyril of Jerusalem, and John Chrysostom. AI Epistemology: A structural comparison between the "disguise of light" (2 Cor. 11:14) and the output mechanics of generative systems. Practical Theology: A proposed "Pneumatological Epistemology" for the digital age, focusing on communal discernment (diakrisis), relational accountability, and the "Fruit Test" (Galatians 5).

Transformation of Madrasah Teachers’ Scientific Writing Competence: Integration of Artificial Intelligence, Self-Efficacy, and Demystification of Online Journal Systems Through Sustainable Publication Clinics

Teachers’ scientific publication competence remains a major challenge in implementing Sustainable Professional Development (Pengembangan Keprofesian Berkelanjutan/PKB), particularly in madrasah education, where limited publication literacy, insufficient mentoring, and low digital scholarly competence often hinder publication outcomes. This study aimed to evaluate the effectiveness of a Sustainable Scientific Publication Clinic in improving madrasah teachers’ scientific publication competence through continuous individualized mentoring and ethical integration of digital scholarly tools. The study employed a mixed-methods approach using an explanatory sequential design involving 30 teachers from Madrasah Tsanawiyah (MTs) and Madrasah Aliyah (MA). Quantitative data were collected using a pretest–posttest scientific publication competence questionnaire, while qualitative data were obtained through observations and semi-structured interviews with ten purposively selected participants. The intervention was implemented over three months through academic writing workshops, individualized coaching, ethical use of Generative Artificial Intelligence (AI), Mendeley training, and Open Journal System (OJS) publication mentoring. Paired Sample t-test results showed a significant increase in the mean competency score from 2.26 to 4.36 (p < 0.001), with a very large effect size (Cohen’s d = 2.91) and an N-Gain of 77%. Furthermore, 73.33% of participants successfully submitted manuscripts, while 26.67% achieved article acceptance. Qualitative findings indicated that continuous mentoring, ethical AI utilization, and digital scholarly tools reduced writing barriers, strengthened self-efficacy, and improved participants’ readiness to complete the publication process. These findings suggest that the Sustainable Scientific Publication Clinic provides an effective, technology-supported, and output-oriented mentoring model for strengthening teachers’ scientific publication competence and supporting sustainable professional development.

Generative AI Adoption and Self-Reported Academic Integrity Risk: A Student Taxonomy in Latin American Higher Education

Generative Artificial Intelligence (GenAI) is reshaping technology-mediated learning environments in higher education, yet the structural heterogeneity of student adoption patterns—particularly across multimodal dimensions beyond text—remains empirically under-characterized. This study develops an empirically derived, data-driven taxonomy of GenAI adoption among university students, identifying distinct user profiles and their disciplinary and ethical risk implications. A quantitative, cross-sectional design was employed with a disciplinarily quota-balanced sample of 3415 students from eight Ecuadorian public universities, stratified across seven areas of knowledge according to the UNESCO classification. K-means cluster analysis on five continuous multimodal variables (text generation, mathematical problem-solving, programming, image generation, and music generation) yielded four distinct profiles: Passive (44.3%), Artist (24.4%), Technical (18.3%), and Comprehensive (13%). Profile membership showed a significant structural association with academic discipline (χ2 = 517.85; Cramér’s V = 0.225). Profiles differed substantially in their self-reported propensity for intellectual delegation to AI systems, with the Comprehensive profile reporting the highest levels (η2 = 0.114, 95% CI [0.092, 0.139]). These findings suggest that ethical risk in AI-mediated academic environments is not uniformly distributed but structurally associated with whether outputs are verifiable or directly presentable, with implications for differentiated AI literacy programs and institutional governance frameworks in higher education.

Algorithmic Authorship, Data Sovereignty, And Intellectual Property Rights: Navigating the Intersection of Law, Science, And Society in the

Abstract The rapid convergence of artificial intelligence (AI), data science, and legal frameworks has created a profound crisis within global and domestic Intellectual Property Rights (IPR) regimes. Traditionally, copyright and patent laws were constructed around the central premise of human agency, recognizing intellectual labor as an extension of human dignity and personality. However, the rise of Generative AI platforms, machine learning models, and autonomous algorithmic systems disrupts foundational legal principles including authorship, inventiveness, originality, and infringement. This paper examines the multidisciplinary intersection of law, computer science, and social sciences regarding IPR. It deconstructs three critical dilemmas: (1) the legal status of AI-generated works and the "human author" requirement under copyright law; (2) the patentability of AI-invented subject matter and the doctrine of the "Person Having Ordinary Skill in the Art" (PHOSITA); and (3) the socio-economic implications of training data scraping, digital commons, and data sovereignty. By analyzing statutory provisions, recent judicial precedents across jurisdictions, and socio-legal frameworks, this study highlights the inadequacy of existing legal doctrines to address non-human innovation. The paper proposes a balanced normative framework incorporating a sui generis legal model for AI outputs, compulsory licensing for dataset training, and transparent algorithmic disclosure to foster technological innovation while protecting human creators and public domain integrity.