generative ai

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.

Ai In Indian Judiciary: A Comprehensive Study Evaluating Operational Deployments, Administrative Obstacles, And Supreme Court Directives

Abstract The integration of Artificial Intelligence (AI) into the Indian judiciary represents a paradigm shift toward modernizing legal administration, enhancing processing efficiencies, and addressing massive case backlogs that have historically strained the justice delivery system. India's judiciary, burdened by one of the largest case pendency figures in the world, has increasingly turned toward digital and computational tools to reduce procedural delay without compromising the constitutional guarantees owed to litigants. This comprehensive research article evaluates operational deployments, administrative obstacles, and Supreme Court directives governing AI usage under the e-Courts framework. It examines the operational boundaries established by the Supreme Court White Paper, the active AI assistive ecosystem (including SUVAS, SUPACE, and LegRAA), legal integrity and professional accountability concerning generative AI hallucinations, and structural process engineering integrated under Phase-III. The study further situates these developments within a constitutional and comparative framework, arguing that India's cautious, human-centred model of AI adoption offers a template that other developing judiciaries may study as they confront similar backlogs, resource constraints, and linguistic diversity. The paper concludes that sustained capacity building, rigorous ethical audits, and close collaboration between the legal profession and technologists will determine whether AI ultimately strengthens, rather than erodes, public confidence in the administration of justice.

Sustainable Artificial Intelligence and Green Data Centres: The Need of the Hour for Environmentally Responsible Digital Transformation

Abstract Artificial Intelligence (AI) has emerged as a transformative technology driving digital innovation across healthcare, finance, education, manufacturing, transportation, and smart cities. However, the rapid advancement of deep learning, foundation models, and generative AI has substantially increased the computational demands placed on modern data centers. The growing dependence on Graphics Processing Units (GPUs), high-performance computing clusters, and cloud-based infrastructures has resulted in extreme electricity consumption, increased greenhouse gas emissions, intensive water usage for cooling, and higher operational costs. These environmental challenges have made sustainability a critical consideration in the future development of AI systems. Sustainable Artificial Intelligence (Sustainable AI) and Green Data Centers have emerged as complementary approaches for reducing the environmental footprint of AI while maintaining computational efficiency and service quality. Sustainable AI focuses on developing computationally efficient algorithms, optimizing model architectures, and minimizing energy consumption throughout the AI lifecycle. Green Data Centers support these objectives by integrating energy-efficient hardware, renewable energy sources, intelligent cooling technologies, virtualization, carbon-aware workload scheduling, and AI-driven resource management. Together, these approaches enable environmentally responsible digital transformation by reducing carbon emissions, improving energy efficiency, and enhancing resource utilization. This paper presents a comprehensive review of Sustainable Artificial Intelligence and Green Data Centers, examining recent technological advancements, sustainability challenges, industry practices, and emerging research trends. It proposes an integrated conceptual framework that combines Green AI techniques with sustainable data center infrastructure to achieve environmentally responsible AI deployment. The paper also discusses key performance indicators, including Power Usage Effectiveness (PUE), Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE), and renewable energy utilization, for evaluating sustainable AI infrastructures. Finally, future research directions are identified to support the development of carbon-neutral AI ecosystems aligned with the United Nations Sustainable Development Goals (SDGs).

Sustainable Artificial Intelligence and Green Data Centres: The Need of the Hour for Environmentally Responsible Digital Transformation

Abstract Artificial Intelligence (AI) has emerged as a transformative technology driving digital innovation across healthcare, finance, education, manufacturing, transportation, and smart cities. However, the rapid advancement of deep learning, foundation models, and generative AI has substantially increased the computational demands placed on modern data centers. The growing dependence on Graphics Processing Units (GPUs), high-performance computing clusters, and cloud-based infrastructures has resulted in extreme electricity consumption, increased greenhouse gas emissions, intensive water usage for cooling, and higher operational costs. These environmental challenges have made sustainability a critical consideration in the future development of AI systems. Sustainable Artificial Intelligence (Sustainable AI) and Green Data Centers have emerged as complementary approaches for reducing the environmental footprint of AI while maintaining computational efficiency and service quality. Sustainable AI focuses on developing computationally efficient algorithms, optimizing model architectures, and minimizing energy consumption throughout the AI lifecycle. Green Data Centers support these objectives by integrating energy-efficient hardware, renewable energy sources, intelligent cooling technologies, virtualization, carbon-aware workload scheduling, and AI-driven resource management. Together, these approaches enable environmentally responsible digital transformation by reducing carbon emissions, improving energy efficiency, and enhancing resource utilization. This paper presents a comprehensive review of Sustainable Artificial Intelligence and Green Data Centers, examining recent technological advancements, sustainability challenges, industry practices, and emerging research trends. It proposes an integrated conceptual framework that combines Green AI techniques with sustainable data center infrastructure to achieve environmentally responsible AI deployment. The paper also discusses key performance indicators, including Power Usage Effectiveness (PUE), Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE), and renewable energy utilization, for evaluating sustainable AI infrastructures. Finally, future research directions are identified to support the development of carbon-neutral AI ecosystems aligned with the United Nations Sustainable Development Goals (SDGs).

Ai In Indian Judiciary: A Comprehensive Study Evaluating Operational Deployments, Administrative Obstacles, And Supreme Court Directives

Abstract The integration of Artificial Intelligence (AI) into the Indian judiciary represents a paradigm shift toward modernizing legal administration, enhancing processing efficiencies, and addressing massive case backlogs that have historically strained the justice delivery system. India's judiciary, burdened by one of the largest case pendency figures in the world, has increasingly turned toward digital and computational tools to reduce procedural delay without compromising the constitutional guarantees owed to litigants. This comprehensive research article evaluates operational deployments, administrative obstacles, and Supreme Court directives governing AI usage under the e-Courts framework. It examines the operational boundaries established by the Supreme Court White Paper, the active AI assistive ecosystem (including SUVAS, SUPACE, and LegRAA), legal integrity and professional accountability concerning generative AI hallucinations, and structural process engineering integrated under Phase-III. The study further situates these developments within a constitutional and comparative framework, arguing that India's cautious, human-centred model of AI adoption offers a template that other developing judiciaries may study as they confront similar backlogs, resource constraints, and linguistic diversity. The paper concludes that sustained capacity building, rigorous ethical audits, and close collaboration between the legal profession and technologists will determine whether AI ultimately strengthens, rather than erodes, public confidence in the administration of justice.

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.

Cultivating AI literacy among high school students through generative AI as a collaborative partner

Despite growing emphasis on AI literacy education, empirical research examining how secondary students engage with and apply AI concepts in authentic project-based learning environments remains limited, particularly among students from historically underserved communities. This mixed-methods study examined African American high school students’ experiences in an AI-enhanced mobile app design project in which generative AI served as a collaborative design partner. Grounded in AI literacy frameworks, the program combined foundational AI instruction with a four-month project-based experience focused on designing AI-enhanced mobile applications. Qualitative data included focus group interviews, student design artifacts, presentations, and researcher field notes, while quantitative data included pre- and post-AI literacy assessments and surveys. Analysis of student design artifacts and presentations revealed that participants demonstrated emerging understanding of AI concepts by proposing AI-enabled features such as recommendation systems, chatbots, and information verification tools within their mobile app designs. Survey results indicated improvements in professional skills, particularly in collaboration, communication, and problem-solving. Knowledge assessment results showed modest but non-significant changes in students’ conceptual AI knowledge, with variation in individual learning trajectories. The study also identified several instructional challenges, including differences in students’ technological readiness and tensions surrounding AI use, ranging from overreliance to avoidance of AI use due to concerns about academic penalties. These findings highlight the potential of AI-enhanced project-based learning to support students’ development and application of AI literacy while underscoring the need for instructional scaffolding to guide students’ effective and ethical use of AI as a collaborative partner in K-12 learning environments.

Generative AI Use Profiles in Latin American Higher Education: A Cross-Sectional Comparison of Two Cohorts

article/ investigation contribution

Generative AI and the risk to environmental justice

Fractal Soul: Mapping Sanjuanist Mystical Purifications Through Analogical Structural Isomorphism with Isomorphic Physics

This methodological working paper introduces a rigorous framework of Analogical Structural Isomorphism to map the precise, highly ordered interior geography of the soul’s passive purification as articulated by St. John of the Cross. Grounded in the doctrinal foundations of Thomistic hylomorphism (ST\ I,\ Q.76,\ A.1) and Divine Simplicity (ST\ I,\ Q.3), this study transcends mere poetic metaphor. It positions the laws of the physical universe as lower-resolution pedagogical scaffoldings—the "fingerprints of the Logos"—that mirror the higher-resolution spiritual laws governing the interior life. AI / ASSISTIVE TECHNOLOGY DECLARATIONIn accordance with emerging academic transparency standards and ecclesiastical guidelines for published scholarship, the author discloses the use of generative artificial intelligence (LLM) tools during the preparation of this working paper. AI technology was utilized strictly in an assistive capacity for structural drafting, cross-referencing of canonical texts, comparative stylistic refinement, and formatting optimization. All theological synthesis, canonical interpretations, doctrinal evaluations, and final text selections remain entirely the original work and responsibility of the author.

Examining Student Dependence on Generative AI tools in Programming Education

Programming students are no longer only learning to write code; they are also learning in environments where AI tools can explain, debug, and generate code alongside them. This shift creates a tension for programming education: the same tools that can make learning more accessible may also encourage dependence when students use them as substitutes for their own reasoning. Using a conceptual and narrative review of recent literature, this paper examines student dependence on generative AI tools in programming education. Central to this review is an examination of learning outcomes, independent programming ability, self-regulated learning, critical thinking, problem solving, learner characteristics, and instructional design in AI-supported programming environments. Students learning to code are increasingly using AI tools to answer questions, explain concepts, help debug, and make information easier to access, but their use can also create problems. Students who rely heavily on them, especially when instructors provide little instruction, may spend less time thinking through problems on their own or reflecting on their solutions. From the literature, we see that the impact of AI is more dependent on the learner, the learning environment, and the use of the tools than on the technology. Existing studies also have important weaknesses, including heavy use of self-reported measures, small sample sizes, correlational research, and limited evidence about what sustained AI use could mean for independent thinking, problem solving, and programming development over time. More data is needed to understand these longer-term effects.