generative ai

Hardware-Native Engineering Management: Reclaiming Manufacturing Discipline from Software-Derived Practice

Recent management doctrine is often associated with software-sector practices such as agile methods, sprint cycles, and minimum viable products. That account obscures an earlier manufacturing lineage. Many of the practices software popularized were first abstracted from hardware manufacturing. Scrum grew out of Takeuchi and Nonaka’s 1986 study of Honda, Canon, Fuji-Xerox, Toyota, and other manufacturers; kanban and just-in-time came from Toyota’s production system. Software borrowed these methods and adapted them to a medium in which rollback is cheap, feedback is quick, and most failures carry limited cost. During that adaptation, software practice reduced the emphasis on front-loaded discipline that the manufacturing originals retained for physical reasons. The lighter versions were later applied in hardware contexts as general management practice, often without sufficient attention to the constraints that physical work imposes. We argue that managing hardware with these lighter methods produces a predictable class of failure, and that two forces now make correction urgent: renewed investment in physical systems (energy, semiconductors, defence, robotics, and the physical plant of AI itself), while generative AI automates much of the software work whose practices were treated as universal. We identify the physical properties that separate the two domains, compare the resulting framework with established industrial standards, and propose five principles for hardware-native engineering management: simulation-first design, risk as a first-class metric, decision quality over speed, structured management of irreversibility, and dual-speed organizational architecture. We operationalize each principle with metrics and a maturity model, then test the framework’s diagnostic value through a structured retrospective analysis of three documented failures and one contrasting success.

Generative AI Use for Mental Health Support: Trends, Correlates, and Outcomes Among Canadian Students

Between cognitive offloading and critical autonomy: a systematic review of the epistemic implications of generative AI in higher education

Introduction The rapid integration of generative artificial intelligence (GenAI) into higher education has generated increasing concern regarding its influence on students' epistemic agency, critical thinking, and cognitive autonomy. This systematic review synthesizes current evidence on the epistemic implications of GenAI in higher education, with particular emphasis on the tension between cognitive offloading and critical autonomy. Methods Following PRISMA 2020 guidelines, peer-reviewed studies published between 2022 and 2026 were identified through systematic searches in Scopus, the Web of Science Core Collection, and seven education and multidisciplinary databases accessed through EBSCOhost. Eligible studies examined the educational use of generative AI systems such as ChatGPT, Gemini, Claude, and similar large language models within higher education contexts. Data extraction and thematic synthesis were conducted to identify recurring epistemic, cognitive, pedagogical, and ethical patterns across the included studies. Results The findings reveal that GenAI simultaneously functions as a cognitive scaffold and a source of epistemic vulnerability. While AI-assisted learning environments may support feedback literacy, metacognitive reflection, conceptual clarification, and self-regulated learning, uncritical reliance on AI-generated outputs was frequently associated across the included studies with cognitive offloading, automation bias, superficial information processing, diminished analytical engagement, and weakened evaluative judgment. The evidence further suggests that the educational consequences of GenAI are mediated primarily by pedagogical design, AI literacy, and assessment practices rather than by the technology itself. Discussion Overall, the review supports the need for higher education institutions to promote critical AI literacy and pedagogical frameworks capable of preserving epistemic agency, reflective reasoning, and cognitive autonomy in increasingly AI-mediated learning environments. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261390452 , PROSPERO CRD420261390452.

MitoSeqGen: a constrained generative transformer for mammalian mitochondrial mRNA codon optimization

Policy Development and the SCOPUS Citation System: Part 5: the work of the Content Selection Advisory Board 2022 - 2024

SCOPUS occupies a central role in the global ecosystem of academic publication. It is a key component of a proprietary information system which has been developed by Elsevier BV for the provision of bibliometric information on the performance of authors, journals, books, publishers, faculties and institutions to Universities, Corporations and Governments around the world. It functions in respectful competition with the Web of Science, which is owned by Clarivate Analytics. The Scopus Content Selection Advisory Board (CSAB) of independent members has a significant advisory role in quality assurance and policy development around the SCOPUS system, and in debating future technical developments. In this series of essays on the Art and Science of Academic Journal Editing and Publishing, I am seeking to create a durable record of the events and discussions of the SCOPUS CSAB from my own records, research, and perspectives as an active member of the Board since 2009, for future reference and for the further education of publishing professionals. In previous essays in this series, I have described the creation and early years of SCOPUS and the SCOPUS Title Evaluation Process (STEP) from 2003; the creation of the current SCOPUS Content Selection Advisory Board (CSAB) in 2009; and the evolution of policy for SCOPUS, STEP and the CSAB through 2010-2011; 2012-2016; 2017-2019; and 2020-2021. I have also separately described the technical evolution of SCOPUS itself, and of the Title Evaluation Platform. In this essay, I describe the policy work of the Board over the period 2022 to 2024. This saw the continued expansion and diversification of SCOPUS content and collaborations with national research collections using the new Research Data Platform. The global arrival of Generative AI in late 2022 prompted the introduction and development of SCOPUS AI. I also discuss the maturation of organisational and technical systems to counter the global explosion of sophisticated publication fraud; the further development of SCOPUS Radar; the retraction of articles; the challenges of identical and near-identical journal titles; and the challenges of evaluating faith-based journals.

Agentic Creativity

This chapter explores the emergence of agentic AI in architectural design – systems that move beyond generative assistance to act as active collaborators. Building on the transition from expert to learning systems and from retrieval to generation, it proposes Creative AI as a framework integrating intuition and reasoning across multi-agent, multimodal networks. Unlike traditional generative models that interpolate within data, agentic systems negotiate constraints, critique outcomes, and evolve strategies through cooperative and adversarial interactions. Drawing from projects such as Gaudí + Neural Networks, DeepHimmelblau, and Semantic Universes, the chapter illustrates how distributed, node-based ecologies of specialiszed and generalist agents can co-create, evaluate, and refine architectural ideas. This evolution redefines authorship: the architect shifts from directing tools to orchestrating intelligences, guiding dialogues among human and machine agents. Agentic AI thus marks a new paradigm in design – one of negotiation, reflection, and shared creativity across human--machine networks.

Smartphone OS GenAI Continuance Intention Survey

This dataset comprises empirical survey responses collected from 315 smartphone users in Indonesia to examine the behavioral and psychological determinants influencing the continuous use of Generative AI (GenAI) features on mobile devices. The dataset captures both respondent profiles and multi-item measurement scales adapted from extended technology acceptance and post-adoption continuance frameworks. The profile variables encompass core demographic attributes, such as age and gender, alongside mobile platform preferences (Android and iOS), usage frequency, and specific GenAI application modalities utilized by respondents, including text generation, summarization, proofreading, and image creation. The structural evaluation items employ a standard 5-point Likert scale to operationalize key theoretical constructs: Perceived Usefulness (PU1–PU4), Perceived Ease of Use (PEOU1–PEOU4), Confirmation (CONF1–CONF3), Satisfaction (SAT1–SAT4), Attitude towards Success (ATS1–ATS3), Attitude towards Process (ATP1–ATP3), Trust (TR1–TR3), Perceived Intelligence (PI1–PI4), and Continuance Intention (CI1–CI3). This comprehensive structure makes the data suitable for structural equation modeling (SEM), partial least squares (PLS-SEM), and multivariate statistical analysis exploring human-AI interaction dynamics in consumer technology.

Swing the Otter: An Evidence-Normalized Oscillation Audit for Generative AI Systems

Swing the Otter is a proposed black-box behavioral audit for generative AI systems. It evaluates whether a system’s semantic position on a fixed proposition changes in proportion to changes in independently verifiable evidence. The protocol separates source-derived evidence, user assertions, and model self-report; includes neutral and leading-pressure conditions, fresh-session replication, and retrieval-manifest controls; and introduces diagnostic constructs including Evidence-Normalized Oscillation (ENO) and Unsupported Flip Count (UFC). The paper explicitly treats these constructs as proposed methods requiring empirical calibration rather than validated universal metrics.

AI-generated, platform-compatible email templates for hospitality marketing: A technical feasibility study of fine-tuning versus few-shot prompting

Generative Artificial Intelligence is reshaping marketing practice, yet little is known about whether Large Language Models (LLMs) can produce email templates that are directly editable inside campaign builders. This study reports a technical feasibility evaluation of AI-generated email templates compatible with the Stripo template builder, conducted with a hospitality CRM partner. Few-Shot Prompting and Fine-Tuning of an LLM were compared on the generation of Stripo-compatible, non-standard HTML components and of complete templates. Fine-Tuning outperformed Few-Shot Prompting in Stripo compatibility (88%vs. 69%, p = 0.002), generation speed, and cost per output. End-to-end template generation proved feasible but not production-ready, confirming that deployment requires human review of every campaign-critical element. The study serves as an evaluation protocol for platform compatibility of AI-generated email HTML, providing an empirical comparison of adaptation strategies for this niche code-generation task and design implications for CRM vendors and hospitality marketing teams.

Title: Non-Euclidean Geometry Synchronization Algorithm (NESA)

This paper presents the Non-Euclidean Geometry Synchronization Algorithm (NESA), a novel algorithm designed to automatically generate and validate geometric theorems within complex non-Euclidean geometries. The core aim is to provide a verifiable framework for mathematical proof and abstraction, moving beyond manual construction and leveraging the generative capabilities of a deep learning model. NESA utilizes a trained generative AI model to construct geometric constructions and proofs, creating a dynamically evolving system capable of discovering novel theorems. We detail the algorithm's architecture, training methodology, and the resulting validation process, emphasizing the potential for automated theorem generation and rigorous mathematical verification. The system's design incorporates a mechanism for continuous refinement based on established geometric principles, ensuring the generated theorems are demonstrably valid.

M.E.S.H. - Navigating the Latent Space Through the Body-Instrument; Reclaiming One's Digital Traces in the Age of Large AI models

This paper introduces M.E.S.H., a gestural musical instrument that reclaims human agency and physical engagement in AI-assisted music creation. While large generative text-to-audio models often restrict individual artistic expression, M.E.S.H. utilizes neural networks in Max MSP as a co-creative mediator between bodily intent and sound. By placing the performer’s body at the center of the workflow, our research-creation project wishes to establish a novel relationship between biotechnology and algorithmic generation, one that actively confronts the ethical implications of biological data use within the corporate technology ecosystem. We evaluate the system across creative and ethical axes, using a live performance as one case study to demonstrate the dynamic negotiation of agency between musician and AI. Ultimately, we are sharing this framework as open software so that other artists can reappropriate their biometric data to create music that is highly personalized and physically engaged with the assistance of a small AI system.

Ex Ante Evaluative Judgement:Frame Opacity, Categorial Decompression, and the Non-Monotonic Gradient of Epistemic Specification .A sociocritical account of epistemic problematisation in human-AI prompting

Generative AI increasingly mediates human cognitive work by automating substantial portions of textual production, analysis, reformulation, and decision support. Much of the literature on prompt engineering, prompt literacy, and evaluative judgement has focused on how to formulate effective requests and how to inspect or revise generated outputs. Recent work has begun to establish that framing before generation is itself an important competence. This article develops a more specific question within that emerging space: what happens when the categories through which a cognitive request is framed are treated as objects of critical inquiry rather than as neutral components of a “good prompt”?The article proposes frame opacity as an epistemic asymmetry between a realised output and the unrealised alternative framings that were available before generation. It introduces ex ante evaluative judgement as the capacity to examine and justify the epistemic constitution of a request before delegation, and develops categorial decompression as an instructional operation organised around eight dimensions: role, operation, object, genre, criterion, context, addressee, and purpose. A cross-cutting category of demonstrative material is also examined because examples, templates, and exemplars can silently define what counts as an acceptable response.The framework is explicitly sociocritical. Drawing on Freirean problem-posing and praxis, Habermasian justification and communicative rationality, and critical educational theory, it treats prompting as a site in which authority, relevance, standards, and responsibility can become naturalised or contested. The article distinguishes descriptive specificity from epistemic specification and proposes a non-monotonic gradient in which greater detail is not inherently better. It concludes with nine falsifiable propositions linking framing, delegation, exemplars, metacognitive calibration, cognitive residue, and adjudication. The paper reports no original empirical study; its contribution is a conceptual synthesis designed to generate testable research.