generative ai

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.

The Digital-Ecological Nexus: Evaluating Cyber Law, Data Protection, and Environmental Law in India's Reformative Era

The Indian legal landscape is navigating a dual transformation: the rapid digitisation of governance and an intensifying climate crisis. In the digital realm, the Digital Personal Data Protection Act, 2023 marks a fundamental shift from a consent-only regime to a framework built on Data Fiduciaries and Data Principals. As technology evolves toward decentralised systems and generative and agentic AI, however, the cyber-law framework of the Information Technology Act, 2000 faces an acute crisis of relevance, one that calls for a transition toward technological neutrality (future-proof drafting) and enhanced algorithmic clarity (AI made intelligible to humans). This paper critically evaluates the synergy between these digital frontiers and environmental law, a connection often overlooked in traditional legal discourse. It examines the ecological footprint of the digital economy, in particular the high energy consumption of data centres and the growing crisis of electronic waste, which poses a direct challenge to the constitutional guarantee of a healthy environment under Article 21. Against the backdrop of the National Education Policy 2020, the study advocates a multidisciplinary pedagogical shift that integrates green informatics into legal curricula, ensuring that future practitioners can navigate the intersection of data privacy and ecological sustainability. The paper further explores the need for legislative reforms that harmonise the requirements of the DPDP Act for data processing with environmental audits. It highlights how the integration of AI into judicial processes can streamline environmental litigation, provided such systems adhere to the principles of natural justice and preserve judicial discretion. Ultimately, the study calls for a phased, constitutionally aligned strategy that balances technological progress with constitutional morality, ensuring that India’s digital sovereignty does not come at the cost of its environmental integrity.

A Student-Centered Framework for Responsible Use of Generative AI in Higher Education

Institutional guidance on generative artificial intelligence has expanded rapidly, yet it addresses administrators, instructors, and assessment designers rather than the students who make most decisions about AI use. Students report that the boundary of acceptable use remains unclear at the moment they must act. This conceptual paper proposes ten student-facing guidelines organized around three pillars: Learning and Growth, Ethics and Integrity, and Awareness and Safety. Drawing on a structured interpretive synthesis of research published between 2023 and 2026 and major policy frameworks, the paper argues that the pillars are jointly necessary because each addresses a distinct, empirically documented failure mode, and that a portable set of guidelines may support responsible use by externalizing metacognitive prompts that unguided AI use can weaken. Compared with policy guidance, institutional frameworks, assessment scales, and AI literacy curricula, the framework is distinctive in being student-facing, course-agnostic, and actionable at the point of decision. Five empirically testable propositions are specified for their evaluation.

Modular Topology Reconstruction

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.

Custodians of the Interior Castle: Reclaiming Sacramental Realism, Canonical Order, and Mystical Purgation Against Modern Lay Ministerial Reductions

Contemporary pastoral practice within the Catholic Church is increasingly marked by the integration of lay-led spiritual renewal programs, deliverance protocols, and psychological inner-healing curricula. While initially deployed to revive nominal faith, these initiatives frequently institutionalize structural, canonical, and theological distortions. This paper analyses three widespread lay-led phenomena—Life in the Spirit Seminars (LSS), Unbound Ministry, and Little Way Healing Ministries—evaluating their operational frameworks through the combined lenses of Tridentine sacramental dogmatics, Sanjuanist mystical theology, the 1983 Code of Canon Law, and modern frameworks of Structural Isomorphism. It demonstrates how these programs: Subvert the ex opere operato efficacy of the Sacraments of Initiation. Systematically invade the internal forum in violation of Canons 220 and 240 §2. Usurp the cura animarum reserved to sacred orders under Canons 519, 129 §1, and 274 §1. Misdiagnose the mystical Passive Night of the Senses as demonic oppression, psychological trauma, or spiritual pathology. By incorporating the rigorous physical iconographies of Structural Isomorphism—such as Thermodynamic Phase Transitions, Fractal Topology (the Menger Sponge), and Resonant Wave Coupling—and comparing lay interventions to the biblical comforters of Job as analyzed by St. John of the Cross, this study argues for a clerical re-assertion of sacramental realism, canonical governance, and the classical Carmelite understanding of the Cross (2 Cor 12:7–9). The critical danger of modern lay-led reductions is not merely that they introduce theological or canonical confusions, but that they structurally operationalize the very adversarial trap St. John of the Cross identified in 'Living Flame of Love' (Stanza 3, §64). By stationing their diagnostic and emotional protocols precisely at the threshold where sense gives way to spirit, these movements intercept souls during the Passive Night of the Senses, feeding the sensory faculties with artificial activity precisely when God is attempting to introduce them into silent, contemplative union." 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.

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.

AI Transmission Kernels: Modelling Generative Systems as Operators in Human Cultural Transmission

Generative AI is commonly treated as a source of information or an intervention applied to an individual. This paper proposes a different formal object: the AI transmission kernel, defined as the conditional probability that a particular model, language and interaction configuration transforms one human epistemic variant into another during transmission. The kernel places the generative system inside the mechanism of cultural evolution rather than treating it as an exogenous exposure. It permits direct comparison between human-only, shared-model, heterogeneous-model and diversity-preserving transmission environments and separates three effects that are otherwise conflated: preservation of existing variants, directional transformation towards dominant variants, and innovation of new defensible variants. The paper outlines how kernels can be estimated from iterated human transmission chains using structurally coded explanations rather than lexical similarity, how their parameters can be linked to delayed transfer and minority-variant survival, and how model concentration changes population dynamics when the kernel is repeatedly applied. Competing explanations include simple copying, task-induced convergence and social influence. The proposal is falsifiable: if estimated kernels do not predict held-out transmission trajectories better than human-only transition models, generative AI has not been shown to act as a distinct selective operator. The framework offers a bridge between human–AI cognition and formal cultural-evolution models.

Title: Adaptive Topology for Material Design

This paper presents an innovative framework for material design utilizing adaptive topology, leveraging generative artificial intelligence to automatically generate and optimize material topologies. Traditional materials design relies heavily on trial-and-error experimentation, often leading to suboptimal material properties and manufacturing challenges. Our approach addresses these limitations by employing a dynamic, iterative process guided by computational simulations, specifically focusing on the interplay between structural integrity and desired material characteristics. We introduce a novel method for generating topology, incorporating feedback loops that continuously refine the resulting structures based on established material science principles and predictive modeling. The framework's core mechanism centers on the synergistic integration of generative AI and established simulation techniques to achieve a significant improvement in material design efficiency and predictive accuracy. This work demonstrates the potential of adaptive topology to unlock new possibilities in material science, pushing the boundaries of material design and facilitating the creation of materials with tailored properties for a diverse range of applications.

Ten-Year Panel of Japanese Municipal Finance from the Local Government Financial Settlement Survey

This R script (make_kessan10_csv.R) converts the Local Government Financial Settlement Survey (市町村別決算状況調), published by the Ministry of Internal Affairs and Communications on its annual pages of local government financial status survey materials, into machine-readable CSV. The source workbooks are print-oriented Excel files with multi-row merged headers, issued as four separate files per fiscal year (overview and expenditure, for cities and for towns and villages). The script consolidates them into long-format panels carrying fiscal year and municipality type as columns, and also writes one file per fiscal year. The output of a run over ten fiscal years (FY2015–FY2024) is deposited alongside it: all 1,741 municipalities, with 33 overview indicators and 94 expenditure items classified by purpose, giving panels of 17,410 rows each. Every municipality and every year is checked for internal consistency: the components of each expenditure category sum to that category's total, and the sum of all categories matches the total expenditure reported in the overview table. All checks passed for all ten years. Amounts are in thousands of yen, as published; blank cells are left blank rather than filled with zero. The column structure of the source data does not change over the period covered. One definitional change affects the adjusted ratio of current expenditure to current revenue: for FY2020 and FY2021 the special bonds issued for deferred tax collection are removed from current general revenue as well. Four changes of municipality occurred: Tomiya and Nakagawa became cities in FY2016 and FY2018 respectively, each receiving a new municipality code; Sasayama was renamed Tamba-Sasayama in FY2019, and Aogashima was renamed in FY2018 in the written form of its name only, both keeping their codes. The code was written with generative AI: Claude (Anthropic) was used to write and revise it. The author has verified the output and takes responsibility for the content. Version 1.1 corrects the reading of the census population change column in the overview table, where a small negative rate written with the triangle sign used in Japanese official statistics was left blank instead of being read as a number. 56 cells across the ten years were affected; no other value changed.

Social and Emotional Learning in the Age of Artificial Intelligence: Toward Human Agency, Ethical Judgment, and Culturally Responsive K–12 Education

Artificial intelligence is becoming part of the informational, creative, and relational environments in which children learn and develop. This paper argues that artificial intelligence changes the contexts in which social and emotional competencies are exercised, not the fundamental importance of those competencies. Drawing on research in social and emotional learning, ecological systems theory, sociocultural theory, social cognitive theory, African relational accounts of personhood, and emerging scholarship on generative artificial intelligence in education, the paper advances a human-centered agenda for K–12 education. It introduces Social-Emotional Agency in AI-Mediated Environments (SE-AIME) as a provisional construct comprising four dimensions: reflective self-awareness, relational discernment, ethical agency, and culturally grounded participation. The paper maps these dimensions to the CASEL framework, presents five practical metaphors for learning design, outlines a developmentally responsive K–12 progression, and proposes the GUIDE heuristic for teacher decision-making. The paper also establishes safeguarding boundaries for student-facing artificial intelligence. AI systems should not function as therapists, confidants, moral authorities, disciplinary judges, or substitutes for essential human relationships. Their appropriate educational role is to provide carefully governed scaffolding for reflection, rehearsal, translation, feedback, and inquiry. The central educational goal is not merely competent AI use. It is the development of young people who can retain judgment, responsibility, cultural grounding, relational capacity, and human agency when intelligent systems participate in learning and communication. Description type: OtherTitle: AI-Assistance DisclosureLanguage: English Generative AI tools supported drafting, structural refinement, bibliographic checking, language editing, document formatting, and quality assurance under human direction and review. Professor Moustapha Diack is the human author and retains responsibility for the paper’s arguments, interpretations, source selection, accuracy, and publication decisions. AI systems are not credited as authors. Additional Description — Scope and Evidence Note Description type: OtherTitle: Scope and LimitationsLanguage: English This publication is a conceptual synthesis rather than a systematic review, clinical guide, legal analysis, or report of primary empirical research. The SE-AIME construct, developmental progression, classroom metaphors, and GUIDE teacher heuristic are provisional scholarly propositions requiring empirical, developmental, and cross-cultural validation.

Guarding the Gate: Research Integrity in Orthodontic Publishing in the Age of Generative AI

ChatGPT and epistemic dependency in African higher education

The increasing integration of generative artificial intelligence (AI) into higher education is reshaping knowledge acquisition and academic learning processes. While existing scholarship emphasizes AI-enabled educational opportunities, limited attention has examined how sustained engagement with generative AI restructures students’ epistemic relationships with knowledge, particularly within underrepresented African higher education contexts. This study investigates the relationship between ChatGPT utilization and epistemic dependency among university students in Burundi and Sierra Leone using a sequential explanatory mixed-methods comparative design integrating survey data from 452 students and qualitative interviews. Findings indicate that increased ChatGPT utilization is associated with heightened algorithmic reliance, declining verification practices, and reduced independent inquiry behaviors. Comparative evidence further demonstrates that epistemic dependency remains institutionally contingent rather than technologically uniform. The study advances scholarship on AI-mediated learning by conceptualizing generative AI as an emerging epistemic intermediary influencing knowledge validation process within digitally transforming higher education environments.