generative ai

Integrating large language models for automated structural analysis

Automated analysis for engineering structures offers considerable potential for boosting efficiency by minimizing repetitive tasks. Although AI-driven methods are increasingly common, no systematic framework yet leverages Large Language Models (LLMs) for automatic structural analysis. This paper proposes a framework that employs domain-specific prompt design and in-context learning strategies to enhance LLM problem-solving capabilities and generative stability, enabling fully automated structural analysis from descriptive text to model outputs. A small-scale benchmark dataset consisting of 20 structural analysis word problems (SAWPs) is also introduced to evaluate the performance of different LLMs within the proposed framework. The results demonstrate that the proposed approach can increase the level of automation in solving SAWPs compared with traditional methods. Quantitatively, the framework built on GPT-5.4 and GPT-4o both achieved 100% accuracy, outperforming GPT-4 (85%), Gemini 1.5 Pro (80%), and Llama-3.3 (30%) on the test examples. Furthermore, integrating domain-specific instructions enhanced performance by 30% on problems with asymmetrical structural configurations.

Toward scalable generative AI: efficient language model distillation via zero-shot rationales

Abstract This paper investigates an efficient approach for distilling Large Language Models (LLMs) into smaller, application-specific models using zero-shot Chain of Thought (CoT) rationale generation and Optimization by Prompting (OPRO). To address the challenges of deploying computationally intensive generative AI for narrow tasks or resource-constrained environments, the approach leverages LLM reasoning capabilities to generate both labels and natural language explanations for unlabeled data. By reducing reliance on human-generated annotations, the approach substantially lowers annotation requirements and prompting costs while maintaining comparable performance in the evaluated settings. We formulate distillation as a multi-task learning problem in which student models are trained to jointly predict labels and learn from teacher-generated rationales, with the goal of improving data efficiency and generalization. Building on established zero-shot Chain of Thought (CoT) prompting and the OPRO prompt optimization technique, we use teacher-generated rationales to reduce annotation token requirements and examine the associated performance and efficiency gains. Additionally, we systematically investigate how explanation properties affect distillation efficiency. Across natural language inference and question answering benchmarks, results indicate that near-optimal performance can be achieved even when rationales are provided for only a subset of the training data, and that shorter explanations are often sufficient. These findings provide practical insights into the trade-offs between rationale generation cost and student model performance. Overall, this work contributes empirical evidence on the effectiveness and cost characteristics of rationale-based distillation for training compact, task-specific language models with minimal human intervention.

Guidance for laboratory implementation, governance and continuous assurance of artificial intelligence in histopathology

Abstract Artificial intelligence (AI) is rapidly transforming histopathology, with applications ranging from workflow optimisation and quality assurance to tumour diagnosis, grading, biomarker assessment and estimation of prognosis. While numerous AI algorithms have demonstrated promising analytical and clinical performance, pathology laboratories are increasingly adopting commercially available AI systems with regulatory-approval rather than developing their own algorithms. Existing guidance largely focuses on AI development, validation and regulatory approval, with comparatively little practical direction on the local verification, governance and ongoing assurance required for safe routine clinical implementation. This paper proposes a practical framework for the clinical implementation of AI specifically within pathology laboratories. Rather than addressing AI development, it focuses on the responsibilities of laboratories adopting established AI systems into clinical practice. The framework distinguishes AI applications according to their intended clinical function, recognising that diagnostic applications, biomarker evaluation, workflow optimisation and generative AI applications require different implementation, verification, governance and quality assurance strategies. It further distinguishes algorithm validation, local verification and continuous assurance as complementary stages of implementation and advocates a function-based, risk-proportionate approach integrated within existing laboratory quality management systems. Practical recommendations are provided for workflow integration, interoperability, human oversight, user competency, performance monitoring, incident management, software updates and proportionate re-verification throughout the AI operational lifecycle. By extending implementation beyond regulatory approval, this guidance complements existing AI development and regulatory frameworks rather than replacing them. It provides a practical governance framework for pathology laboratories, professional organisations, accreditation bodies, and healthcare providers to support the safe, standardised, and sustainable integration of AI into routine histopathology while maintaining diagnostic quality, patient safety, and clinical governance.

Artificial Intelligence in Counseling and Counseling Psychology: A Counselor-Led Framework for Ethical Practice & Training

As generative artificial intelligence (AI) tools like ChatGPT become increasingly accessible, counseling psychologists are exploring their potential use in clinical practice, often without clear guidance or ethical frameworks. This paper presents a counselor-led model for integrating generative AI tools into counseling practice. Grounded in ethical principles, the model emphasizes human oversight, transparency, and client-centered care. A case study illustrates the application of this model with a veteran receiving counseling services through the Veterans Health Administration. The case demonstrates how generative AI can support foundational career counseling tasks (i.e., career exploration and career planning), while maintaining full counselor oversight. Key ethical considerations are discussed, including transparency and informed consent, data privacy and security, accuracy and bias, and human oversight and professional judgment. This work contributes to the emerging literature on AI-assisted counseling by offering an ethically grounded and practice-oriented model for counseling professionals interested in engaging with these technologies responsibly.

Analysis Capability Dynamics of E-Commerce MSMEs in Adopting Generative AI Technology for Content Strategy Efficiency Creative

Purpose: This study aims to examine how dynamic capabilities—detecting, utilizing, and transforming—mediate the adoption of Generative Artificial Intelligence (Generative AI) to optimize content marketing strategies among culinary Micro, Small, and Medium Enterprises (MSMEs) in Jatinangor. Research Method: A qualitative descriptive approach with a multiple case study design was employed to explore how culinary MSMEs integrate Generative AI into content marketing while addressing digital and resource constraints. Results and Discussion: The findings show that detecting capability develops organically through a bottom-up process, with creative staff acting as information gatekeepers. This capability is reflected in tactical budgeting for premium AI accounts and independent experimentation. Transforming capability emerges by restructuring conventional workflows into a human–AI hybrid model, in which AI generates ideas and drafts, while creative staff perform cultural and local curation. This integration reduces content production time by 50%–60% without compromising brand authenticity or local identity. Implications: Generative AI serves as a capability enhancer, increasing creative productivity and supporting digital creativity among resource-constrained MSMEs. Originality: This study contributes by explaining Generative AI adoption through a dynamic capabilities perspective and demonstrating how human–AI collaboration enables productive yet culturally authentic content marketing at the micro-business level.

Development of a rapid assessment method for responsible use of generative AI in scientific research: Application to the Ugandan research context

Abstract Objective Researchers are increasingly using generative artificial intelligence (GenAI) to support tasks such as literature review, academic writing, programming, data interpretation, and knowledge synthesis. We developed a Rapid Assessment Method for Responsible Use of Generative AI in Scientific Research (RAM-GenAI) to provide a practical approach for evaluating responsible practices in low- and middle-income settings like Uganda. Results description Rather than presenting long checklists of many individual requirements, RAM-Gen AI have five major areas of assessment. These are groups related to the principles of responsible GenAI use. They includes, transparency of AI use, verification of AI-generated outputs, data responsibility, human oversight, and reproducibility of AI-assisted research workflows. Each domain contains assessment criteria designed to identify strengths, risks, and areas requiring improvement. RAM-GenAI developed provides researchers and institutions with a practical approach for evaluating responsible GenAI practices before, during, or after AI-assisted research activities.

Experiencing AI at work:How affordances shape motivation, agency and creativity

Artificial Intelligence (AI) is increasingly embedded in organisational life, and this dissertation explores employees’ experience of AI in everyday work. It focuses on broad-application AI, commonly introduced through organisational initiatives led by HR, Learning & Development, or IT with the aim of supporting employees. Rather than approaching AI as a discrete tool with fixed effects, the dissertation examines unfolding relations in specific contexts and ask what matters in AI-inclusive work. This dissertation shows that AI applications are not merely sets of functionalities, but are inseparable from the situated context, shaping and being shaped by employees, work practices, and the organisational environment. It finds that conversational AI, using natural language instead of a menu-based interface for employee self-service, can contribute to a motivation and well-being supportive organisational environment by fostering greater autonomy, competence and relatedness. It also examines how employees interact with AI systems internal and external to their organisations (including unendorsed “shadow AI”), such as contextual search, content recommendations and generative AI in knowledge work. These interactions, shaped by past habits, present constraints and imagined futures, gradually reshape the boundaries of tasks, relationships and the meaning of work. Finally, the dissertation argues that different types of AI matter in different ways because they elicit different forms of engagement and different workplace experiences. Discriminative AI is positioned as a tool for the task, foregrounding efficiency and effectiveness, while also potentially giving rise to possible negative long-term experiences and raising questions about meaningful work. Generative AI is positioned as a medium for creative expression, foregrounding exploration, innovation, and creative actions, thereby fostering more creative and positive experiences at work. Overall, this dissertation offers insights for scholars and practitioners into emerging relations in AI-inclusive work, showing that the value and effects of AI do not reside in technology alone, but emerge through the relations among employees, AI characteristics and organisational context. In doing so, it offers a perspective that moves beyond short-term gains and highlights the longer-term value of creating work environments in which AI is experienced as useful, meaningful and supportive.

The Generation-Governance Impedance Mismatch: Protocol-Governed Systems in the AI Era

The increasing adoption of generative artificial intelligence (AI) in software development introduces a structural asymmetry: implementation generation now occurs at machine speed, while behavioral governance remains constrained by institutional deliberation. These processes are not merely mismatched in velocity; they are orthogonal in function. Generation produces executable artifacts. Governance establishes permissible behavior. This paper formalizes the generation-governance impedance mismatch as a structural property of AI-accelerated systems. We argue that conventional governance mechanisms—code review, testing, and audit processes—operate at the implementation layer and therefore cannot scale to match machine-speed generation without structural reform.

The Generation-Governance Impedance Mismatch: Protocol-Governed Systems in the AI Era

The increasing adoption of generative artificial intelligence (AI) in software development introduces a structural asymmetry: implementation generation now occurs at machine speed, while behavioral governance remains constrained by institutional deliberation. These processes are not merely mismatched in velocity; they are orthogonal in function. Generation produces executable artifacts. Governance establishes permissible behavior. This paper formalizes the generation-governance impedance mismatch as a structural property of AI-accelerated systems. We argue that conventional governance mechanisms—code review, testing, and audit processes—operate at the implementation layer and therefore cannot scale to match machine-speed generation without structural reform.

Generative AI in Higher Education: A PRISMA-ScR Scoping Review of Academic Support, Multilingual Mediation, Ethics, Dependence, and Risk

When AI meets design: the role of enhanced and impeded human–AI interaction in designers’ value co-creation

Results show that the informativeness and novelty of GAI outputs significantly and positively predict designers’ co-creation intentions, and actionable guidance for GAI application in design practice is provided.

AI-Powered Chatbots for Customer Service in Banking

This study examines the role of AI-powered chatbots in enhancing customer service within banking institutions by reviewing existing literature and proposing a conceptual framework for chatbot adoption, and highlights challenges associated with privacy, trust, cybersecurity, and ethical considerations.