Artificial Intelligence Design Research Articles

Managing workplace AI risks and the future of work.

Artificial intelligence (AI)-the field of computer science that designs machines to perform tasks that typically require human intelligence-has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well-being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders....

The Canadian AIDA and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 1

Abstract On June 16, 2022, the Canadian government introduced Bill C- 27, sponsored by the Minister of Innovation, Science and Industry, to update Canada’s federal privacy legal landscape. As earthshaking as that legislation was to the privacy regime in Canada, the impact of Bill C-27 was not limited to privacy regulation. Notably, Bill C-27 also introduced the Artificial Intelligence and Data Act (“AIDA”), which aims to introduce regulations in Canada regarding the design, development, and use of artificial intelligence (“AI”) systems. As we have previously written, while the AIDA is Canada’s first potential law aimed explicitly at regulating AI, it is in many cases influenced by the European Union’s then-proposed Regulation (EU) 2024/1689 (the “EU AI Act”) introduced on April 21, 2021. Time has since elapsed, and now AIDA - with the November 2023 introduction of new proposed (and substantive) amendments - and the July 2024 publication of the final EU Council-approved EU AI Act, have become increasingly aligned: good news for organizations in the AI industry. There do, however, continue to exist differences between the two items of legislation which can present traps for the unwary.

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 (SDGs-6) and Environmental Protection Agency (EPA) objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists' and Clinicians' Perspectives on AI Augmentation and Automation.

Artificial intelligence (AI) holds immense potential for enhancing clinical and administrative health care tasks. However, slow adoption and implementation challenges highlight the need to consider how humans can effectively collaborate with AI within broader socio-technical systems in health care. In the example of intensive care units (ICUs), we compare data scientists' and clinicians' assessments of the optimal utilization of human and AI capabilities by determining suitable levels of human-AI teaming for safely and meaningfully augmenting or automating 6 core tasks. The goal is to provide actionable recommendations for policy makers and health care practitioners regarding AI design and implementation. In this multimethod study, we combine a systematic task analysis across 6 ICUs with an international Delphi survey involving 19 health data scientists from the industry and academia and 61 ICU clinicians (25 physicians and 36 nurses) to define and assess optimal levels of human-AI teaming (level 1=no performance benefits; level 2=AI augments human performance; level 3=humans augment AI performance; level 4=AI performs without human input). Stakeholder groups also considered ethical and social implications. Both stakeholder groups chose level 2 and 3 human-AI teaming for 4 out of 6 core tasks in the ICU. For one task (monitoring), level 4 was the preferred design choice. For the task of patient interactions, both data scientists and clinicians agreed that AI should not be used regardless of technological feasibility due to the importance of the physician-patient and nurse-patient relationship and ethical concerns. Human-AI design choices rely on interpretability, predictability, and control over AI systems. If these conditions are not met and AI performs below human-level reliability, a reduction to level 1 or shifting accountability away from human end users is advised. If AI performs at or beyond human-level reliability and these conditions are not met, shifting to level 4 automation should be considered to ensure safe and efficient human-AI teaming. By considering the sociotechnical system and determining appropriate levels of human-AI teaming, our study showcases the potential for improving the safety and effectiveness of AI usage in ICUs and broader health care settings. Regulatory measures should prioritize interpretability, predictability, and control if clinicians hold full accountability. Ethical and social implications must be carefully evaluated to ensure effective collaboration between humans and AI, particularly considering the most recent advancements in generative AI.

Artificial Intelligence in Design and Impact on Electronic Marketing in Companies

E-marketing refers to the use of technology and artificial intelligence to analyze and interpret marketing data. In addition to providing recommendations and guidance to improve marketing campaigns, improve user experience, and increase sales. These tools include data analytics, machine learning, data classification and aggregation, voice and image recognition, and natural language. Therefore, this study aimed to identify the effect of artificial intelligence (AI) in Design on E-Marketing in Companies. This study used a survey design with a quantitative approach, with the target group being workers of E-Marketing companies. A 198 surveys were also gathered from Jordanian E-Marketing companies, however, 187 questionnaires were judged viable for study. Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing.

Artificial intelligence in optical lens design

Traditional optical design entails arduous, iterative stages that significantly rely on the intuition and experience of lens designers. Starting-point design selection has always been the major hurdle for most optical design problem, and different designers might produce different final lens designs even if using the same initial specification. Lens designers typically choose designs from existing lens databases, analyse relevant lens structures, or explore patent literature and technical publications. With increased processing capability, producing automated lens designs using Artificial Intelligence (AI) approaches is becoming a viable alternative. Therefore, it is noteworthy that a comprehensive review addressing the latest advancements in using AI for starting-point design is still lacking. Herein, we highlight the gap at the confluence of applied AI and optical lens design, by presenting a comprehensive review of the current literature with an emphasis on using various AI approaches to generate starting-point designs for refractive optical systems, discuss the limitations, and suggest a potential alternate approach for further research.

Design of artificial intelligence based technique for classification chronic pulmonary disease from chest X-RAY

Design of artificial intelligence based technique for classification chronic pulmonary disease from chest X-RAY

Universal design for learning and artificial intelligence in the digital era: Fostering inclusion and autonomous learning

In the digital era, the convergence of universal design for learning (UDL) principles and artificial intelligence (AI) stands as a transformative force shaping education. This article explores their intersection, emphasizing their combined impact on fostering inclusion and autonomous learning through the lens of UDL’s multiple means of representation, engagement, and expression. UDL, committed to inclusivity by providing various ways for students to access, engage with, and demonstrate understanding of content, synergizes with AI’s transformative capabilities. The article presents three practical applications illustrating how the integration of UDL and AI, employing multiple means, enhances autonomous learning, eliminates barriers, and cultivates inclusive educational spaces. This collaboration not only addresses immediate challenges but also serves as a catalyst for systemic change, paving the way for a more just and equitable educational landscape in the digital era. Continuous reflection, ethical considerations, and purposeful integration of UDL principles and AI are essential for refining these approaches and ensuring a responsive educational system that promotes inclusion and autonomy.

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Design and Development of Artificial Intelligence Based Applications Using Classification Algorithms in Datamining to Predict Early Diabetes

Diabetes is a chronic disease characterized by high blood sugar levels. High blood sugar levels can cause various serious complications, such as heart disease, stroke and kidney failure. Early detection of diabetes is essential to prevent this complication. This research is using experimental method. The experimental method is a research method carried out by deliberately manipulating the independent variable to see its effect on the dependent variable. In this case, the independent variable being manipulated is the machine learning algorithm used to predict diabetes. The dependent variable observed was the accuracy of the diabetes prediction model. From the research results, it was found that the Naive Bayes model had the highest accuracy, namely 81.5%. This model is better than the Decision Tree C.45 (72.7%), Random Forest (73.8%), and K-Nearest Neighbor (71.5%) models. Based on the research results, it can be concluded that the Naive Bayes model is the best model for predicting diabetes. The appropriate system development method for this research is the Rapid Application Development (RAD) method. Further research can be carried out to improve the accuracy of diabetes prediction models by using optimization models, such as Particle Swarm Optimization or Colony Optimization. In addition, further research can be carried out to develop diabetes prediction applications that can predict diseases that may be caused by diabetes, such as heart disease, stroke, kidney disease and blindness.

Generative AI in Higher Education: Graduate Teaching Assistants’ Practice and Reflection on ChatGPT for Module Assessment

The rapid evolvement of Artificial Intelligence (AI) and the launch of ChatGPT and other Generative AI tools have concerned Higher Education Institutions (HEIs), which now need to develop comprehensive pedagogical guidelines and frameworks in this emerging AI era. These advancements have sparked discussions and research on their implications on assessment design and student assessment, with multiple opposing perspectives emerging. Whilst ChatGPT is perceived as an important opportunity for enhancing student learning, it is considered as a significant threat to academic integrity and student skills development. These differing perspectives create the need for teaching staff to reflect on their pedagogical practices on ChatGPT and Generative AI and propose potential paths forward for HEIs. Although research on Generative AI and assessment design is rapidly growing, the perspective of Graduate Teaching Assistants (GTAs) as teaching staff uniquely positioned to bridge the gap between faculty and students, is under-represented. To this end, in this practice paper, GTAs reflect on their dual identities as student and tutor to evaluate differing positions to the role of ChatGPT to support or hinder assessment. By being involved in a ChatGPT project for student assessment for MSc students in Engineering degrees, the authors present their reflections on the impact that ChatGPT and Generative AI technologies may have on HEIs, with a focus on assessment design, as well as on potential paths forward for the sector. This practice paper contributes to the ongoing discussions and research on the development of pedagogical guidelines and frameworks in the Generative AI era.

English speaking with artificial intelligence (AI): The roles of enjoyment, willingness to communicate with AI, and innovativeness

The pervasive use of artificial intelligence (AI) among educational stakeholders has inevitably led to discussion around its influence on learning as well as users' continuance adoption. There is a dearth of studies that contextualize the use of AI in the English-speaking learning setting, and even fewer studies examined the roles of learners' emotions generated from AI usage in influencing their speaking practice. The current study was designed based on the Expectation Confirmation Model to investigate the impact of perceived enjoyment, willingness to communicate with AI, and innovativeness on influencing EFL learners' continuance learning intention with AI-enhanced tools, such as EAP Talk. Results from the structural equation modelling suggested EFL EAP Talk users perceived positively in AI affordances and wiliness to communicate with AI significantly influenced perceived usefulness and satisfaction, which further impacted their intentions to continue the usage. Moreover, the enjoyment they perceived is significantly associated with continuance intention, while the role of innovation was not suggested. The research model explained 88% of the variance for continuance behavioral intention. This study provides suggestions for policymakers, AI designers, and course instructors to innovate learning methods and strengthen supervision to enhance students’ language learning.

Artificial intelligence psychological anthropomorphism: scale development and validation

ABSTRACT This study explores the conceptualization, dimensional structure, and measurement of artificial intelligence (AI) psychological anthropomorphism in service scenarios. Data were collected using semi-structured in-depth interviews. A grounded theory research approach was employed to construct a structural model of AI psychological anthropomorphism that included the dimensions of personality, empathy, and mind. Exploratory and confirmatory factor analyses were subsequently conducted on questionnaire data collected through online surveys and from which a scale for AI psychological anthropomorphism was developed. It consisted of 16 items and demonstrated good reliability and validity. Moreover, using structural equation modeling, strong nomological validity was demonstrated. The results indicate that AI psychological anthropomorphism and its dimensions significantly and positively predict trust and identity threat. These findings enhance understanding of the conceptual meaning and dimensional structure of AI psychological anthropomorphism in service scenarios, as well as provide a psychometrically reliable and valid measurement tool for use in subsequent empirical research. Additionally, the findings offer important insights for AI developers, service providers, and regulatory agencies to ameliorate AI design, formulate AI marketing strategies, and refine AI governance policies.

Artificial intelligence in design of smart city (the Innopolis case study)

Artificial intelligence in design of smart city (the Innopolis case study)

TRENDS OF ARTIFICIAL INTELLIGENCE AND IMMERSIVE DESIGN USE IN THE FORMATION OF ARCHITECTURAL ENVIRONMENT

There is increasing attention to a new design methodology incorporating the latest achievements of a non-hierarchical way of forming based on the learning process of computers or machines, namely the concepts of artificial intelligence (AI) and immersive design, using virtual and other types of reality. The study aims to reveal the necessity to identify the main trends of artificial intelligence and immersive design use in architectural environment formation. The results can contribute to rethinking architectural design algorithms at the present stage and identifying potential opportunities for improvement. The paper describes the possibilities of AI and immersive design in architectural environment formation. Analysis of current research in this field has made it possible to identify trends that likely will continue to develop further and impact the architectural profession. One of these trends is the improvement of the architect’s toolkit, currently provided by additional plug-ins to existing programmes or separate services based on AI algorithms, but in the future, it may be a change in the programme code itself with a completely updated structure. In this case, the role of software from the architect-managed toolkit may shift to another plane, such as a digital assistant that partially or fully manages its responsibilities in working on an architectural project. The symbiosis of multidirectional but powerful immersive design technologies (VR, AR, MR) and AI algorithms is another trend in architectural environment formation. It can also be characterised as the most experimental and, at the same time, dynamically progressing, given the active development of its components. Another modern trend is to bring architectural design to a new level of a balanced combination of virtual and real. The immersion of the user in virtual reality for design work and demonstration of the relevant results is gradually changing as the 3D model of an architectural structure is increasingly manifested in the physical world, including at the implementation site, and interactive equipment with flexible and constantly evolving functionality provides architects with more opportunities for creativity. Keywords: artificial intelligence, immersive design, architectural environment.

AI-Based Metamaterial Design.

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

The influence of subjective knowledge, technophobia and perceived enjoyment on design students’ intention to use artificial intelligence design tools

The influence of subjective knowledge, technophobia and perceived enjoyment on design students’ intention to use artificial intelligence design tools

Embracing artificial intelligence design for better radiopharmaceuticals

Embracing artificial intelligence design for better radiopharmaceuticals

Revolutionary 3D Design with AI

The 3D design delves into the creation and deployment of an innovative web experience that seamlessly merges Artificial Intelligence (AI) technology and captivating 3D visuals within a React-based framework. It demonstrates the utilization of AI algorithms for enhancing user interactivity, personalization, and the creation of dynamic 3D elements, fostering a captivating user experience. Embark on a transformative journey into the heart of fashion's evolution, this unveils a paradigm-shifting collaboration between Artificial Intelligence (AI) and 3D cloth design, heralding a new era of creativity and innovation within the fashion industry. This explores the fusion of Artificial Intelligence (AI) with 3D cloth design, revolutionizing the fashion industry's creative landscape. Integrating AI into cloth design processes allows for swift ideation, realistic simulations, and diverse fabric explorations, empowering designers to redefine fashion concepts efficiently. Key Words: AI,3D, Design.