Artificial Intelligence Design Research Articles

An initial interpretation of the U.S. Department of Education's AI report: Implications and recommendations for academic libraries

This article provides an analysis of the U.S. Department of Education's report on Artificial Intelligence (AI) and its implications for academic libraries. It delves into the report's key points, including the importance of AI literacy, the need for educator involvement in AI design and implementation, and the necessity of preparing for AIrelated issues. The author discusses how these points impact academic libraries and offers actionable recommendations for library leaders. It emphasizes the need for libraries to promote AI literacy, involve librarians in AI implementation, develop guidelines for AI use, prepare for AI issues, and collaborate with other stakeholders. The article concludes with a call to action for academic libraries to take a proactive approach to AI, ensuring its effective, ethical, and responsible use in library services and operations. This analysis serves as a roadmap for academic libraries navigating the evolving landscape of AI in education.

Artificial intelligence and structural design of inorganic hollow fiber membranes: Materials chemistry

A key challenge is to produce the uniform morphology and regular pore design of inorganic hollow fiber membranes (HFMs) due to involvement of multiple parameters including, fabrication process and materials chemistry. Inorganic HFMs required technical innovations via novel structural design and artificial intelligence (AI) to produce the uniform structure and regular pore design. Therefore, this review aims at critical analysis on the most recent and relevant approaches to tackle the issues related to tune the morphology and pore design of inorganic HFMs. Structural design and evaluation of routes towards the dope suspension, spinning, and sintering of inorganic HFMs are critically analysed. AI, driving forces and challenges involved for harnessing of materials are revealed in this review. AI programs used for the prediction of pore design and performance of HFMs have also been explained in this review. Overall, this review will provide the understanding to build the equilibrium in spinning and sintering processes to control the design of micro-channels, and structural properties of inorganic HFMs. This review has great significance to control the new design of membranes via AI programs. This review also explain the inorganic membrane efficiency as algal-bioreactor.

Redes neuronales de apoyo en la docencia bajo Moodle para predicción de deserción y reprobación escolar

Artificial Intelligence (AI) designs computer tools that simulate human intelligence processes, including learning, reasoning, and selfcorrection. In the educational field, AI facilitates the evaluation of student progress thro ugh neural networks that adjust tests to previously achieved achievements. The purpose of this work was to design an Artificial Neural Network (ANN) that will analyze the performance of students in the subject of Biomedical Informatics I at the Facultad de Medicina of the UNAM in order to estimate the probabilities of passing, failing, and dropping out. Materials and methods: A neural network was designed in Python, consisting of 3 fully connected layers, using performance information from approximately 2000 students in the Moodle platform for Biomedical Informatics I during 2019, 2020, and 2021 at the Facultad de Medicina of UNAM. The neural network was trained to estimate the probable performance of the students, executing 2500 epochs with a mean absolute error level of 0.0158. A kappa accuracy rate of 0.947 was obtained through a confusión matrix when classifying between passing and failing grades. For visualization, a web system with a traffic light system was designed to allow evaluation and teaching coordinators from the Department of Biomedical Informatics to consult the results obtained from the network. Discussion: The network must continue to be fed with controlled data, meaning data that is specifically worked for the network’s training, since the quality of the data in the input directly determines the quality of the data obtained at the end of the process, affecting the network’s objective attainment. Conclusion: There is still much to learn and work to be done, but the interdisciplinary effort will allow us to improve our system and ultimately support the educational process of future doctors in the best way possible. Keywords: Neural networks; education; Moodle; dropout; failure; AI; machine learning.

Implementation of explainable artificial intelligence in commercial communication systems using micro systems.

The developments in the field of artificial intelligence (AI) and decision making systems are identified as virtuous models for banking and finance sector (BFS) applications. Even though AI provides great advantage in application changes it is essential to remodel the system using explainable artificial intelligence (XAI) design system. Also the standard sensing models provides appropriate monitoring values but huge size of sensors is considered as a major drawback. Thus two diverse problems are addressed in this research work where XAI has been integrated with micro electro-mechanical systems (MEMS) for solving the problems related to BFS applications. Further the data security has been enhanced as XAI is implemented with conviction managements and real time experiments are carried out by developing a unique application by integrating new mathematical designs. To validate the effectiveness of BFS application the developed model is tested with five scenarios which includes multiple parametric arrangements with interpretability process. The tested and compared outcomes with existing models indicates that XAI and MEMS provides inordinate improvements in terms of data impairments thus increasing the transparency of the projected system to an average 96%.

Artificial Intelligence and Graphic Design

The connection between artificial intelligence (AI) and graphic design is close and increasingly significant in the world of technology and creativity. Graphic design involves the use of various elements, such as images, text, and color, to create visual communication. This process is often slow and laborious. Artificial intelligence can be useful to automate these processes and improve the efficiency of design work. Artificial intelligence in the modern era is a powerful weapon for creating new graphic elements, improving the quality of designs and analyzing user behavior. In the future, it is expected that these capabilities will develop and new ways to use artificial intelligence in graphic design will appear, but one should not forget the dangers that this spanless new tool hides.

Design and Implementation of Adaptive and Artificial Intelligence Controller for Brushless Motor Drive Electric Vehicle

<div>Brushless direct current (BLDC) motor aims to obtain high efficiency when compared to conventional DC motors due to several reasons. But when it comes to the control then its control is much more complicated due to the requirement of a phase supply switching circuit. Usually, the conventional and classical proportional integral derivative (PID) controller is used but it is quite cumbersome to tune its fixed gains. APID controller is used where PID fails to fulfill the objectives in varying situations. So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor. ANFIS includes all the advantages of controllers and provides the most accurate results. The mathematical model of all the controllers is discussed and its performance is simulated in MATLAB/Simulink. ANFIS includes all the advantages of controllers and provides the most accurate results.</div>

Epistemically violent biases in artificial intelligence design: the case of DALLE-E 2 and Starry AI

PurposeThe paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin by expanding their critique to the African continent. The research aims to assess if algorithmic biases are prevalent in DALL-E 2 and Starry AI. The aim is to help inform better artificial intelligence (AI) systems for future use.Design/methodology/approachThe paper utilised a desktop study for literature and gathered data from Open AI’s DALL-E 2 text-to-image generator and StarryAI text-to-image generator.FindingsThe DALL-E 2 significantly underperformed when it was tasked with generating images of “An African Family” as opposed to images of a “Family”. The pictures lacked any conceivable detail as compared to the latter of this comparison. The StarryAI significantly outperformed the DALL-E 2 and rendered visible faces. However, the accuracy of the culture portrayed was poor.Research limitations/implicationsBecause of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. The implications, however, are that more inclusion is warranted to help address the issue of cultural inaccuracies noted in a few of the paper’s experiments.Practical implicationsThe paper is useful for advocates who advocate for algorithmic equality and fairness by highlighting evidence of the implications of systemic-induced algorithmic bias.Social implicationsThe reduction in offensive racism and more socially appropriate AI can be a better product for commercialisation and general use. If AI is trained on diversity, it can lead to better applications in contemporary society.Originality/valueThe paper’s use of DALL-E 2 and Starry AI is an under-researched area, and future studies on this matter are welcome.

Retracted: Artificial Intelligence and VR Environment Design of Digital Museum Based on Embedded Image Processing

Retracted: Artificial Intelligence and VR Environment Design of Digital Museum Based on Embedded Image Processing

Optimization and application of artificial intelligence in robotic automated distribution network overhead line engineering

INTRODUCTION: Artificial intelligence is a product of high-end technological development since the 21st century, which has subverted people's traditional cognition in many aspects and greatly enriched and improved people's lives. Artificial intelligence has covered every aspect of life, and the distribution network overhead line project is also one of them. The combination of the two symbolizes the combination of modern technology and infrastructure construction, which is of great significance for modern economic and social development and transformation and upgrading. OBJECTIVES: In order to solve the practical problems in the design of artificial intelligence and distribution network overhead line engineering, this paper focuses on the practical use of such artificial intelligence as robots in distribution network overhead line engineering. METHODS: The models of spatial perception, target recognition and automatic calculation are established, and some key technical problems of robots put into actual engineering are simulated and calculated. RESULTS: In the spatial perception model, the combination of robotic arm and laser device is utilized to solve the problem of direct sunlight, which affects the localization. In the target recognition model, combining the algorithms of minimum spanning tree and maximum critical path, the computational accuracy is improved to 1 mm. in the automatic computation model, the introduction of auxiliary lines and the secondary confirmation of manpower make the error of the work further reduced. CONCLUSION: This paper's simulation algorithm for the reality of the distribution network overhead line project provides a more detailed solution to improve the technical content of the distribution network overhead line project and the quality of construction management is not a simple task, the need for the relevant distribution network overhead line project enterprises as well as the corresponding distribution network overhead line project personnel to take targeted measures.

Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

The Art and Science of Prompt Engineering: A New Literacy in the Information Age

The novel discipline of prompt-engineering is a combination of artificial intelligence, linguistics, and user experience design. Crafting effective prompts for AI models like OpenAI’s GPT can optimize the quality of generated output, especially in reference services. The article explains the CLEAR Framework—a guiding tool that incorporates principles of conciseness, logic, explicitness, adaptability, and reflectiveness in prompt crafting. Despite challenges including biases, ethical concerns, and keeping up with rapidly evolving AI capabilities, the field also presents opportunities for growth. The article concludes with a call to action for library professionals to embrace and master prompt-engineering as an essential skill.

Continuous Error Timing in Automation: The Peak-End Effect on Human-Automation Trust

This study developed an experimental paradigm (CAPTCHA recognition task) with high ecological validity to investigate how continuous errors in an automatic system and the timing of their occurrence affect human-automation trust. The continuous system errors were manipulated to appear at either of the four timing conditions: the early stage, middle stage, late stage of the task, or not showing. Our research found that continuous errors undermines trust in automated systems. More importantly, even with the same average system reliability, overall trust decreases significantly with continuous errors. Human-automation trust is significantly lower in the late continuous error condition compared to the no continuous error condition, indicating that trust in automated systems accords with the peak-end rule. Thus, user trust is mainly affected by the peak and end values of the system reliability. This study provides new suggestions for a trustworthy artificial intelligence design. Although system errors cannot be eliminated thoroughly, developers can minimize their impact on human-automation trust by avoiding continuous errors and preventing them from occurring during the late stage of interaction.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C2 molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

AI VS. HUMAN: THE PUBLIC'S PERCEPTIONS OF THE DESIGN ABILITIES OF ARTIFICIAL INTELLIGENCE

AbstractWith the increasing implementation of artificial intelligence (AI) in the design process, it is crucial to understand how users will accept AI-designed products. This work studies how the public currently perceives an AI's design capability as compared to a human designer's capability by conducting an online survey of 205 people via Amazon Mechanical Turk. The survey collects the respondents' perception on 16 specific bicycle design goals, demographic information, and self-reported level of design and AI/ML knowledge. Findings reveal that people think an AI would perform worse than a human designer on most design goals, particularly the goals that are user-dependent. This work also shows that the higher people's self-reported level of knowledge in design and the older they are, the more likely they are to think an AI's design capability would exceed a human designer's capability. The insights from this work add to the understanding of user acceptance of AI-designed products, as well as human designers' acceptance of AI input in human-AI teams.

Player's Affective States as Meta AI Design on Augmented Reality Games

Games are considered one of the most popular entertainment forms worldwide. The interaction in the game environment makes the players addicted to playing the game. One technique to build an addicting game is utilizing the player's emotions using Meta Artificial Intelligence (AI). The player's emotions can be utilized by adjusting the game difficulty. Most of the game offers static and steady difficulty development throughout the game. This research proposes a Meta AI game design using the player's affective states. We argue that a dynamic difficulty development throughout the game will increase the player's game experiences. The player's facial expressions are utilized to extract the player's affective state information. To recognize the player's facial expressions, a Facial Expressions Recognition (FER) model was trained using VGG-16 architecture and The Indonesian Mixed Emotion Dataset (IMED) dataset in addition to a self-collected dataset. The emotions recognition model (from player's facial expressions) achieved the best validation accuracy of 99.98%. The model was implemented in the proposed Meta AI game design. The Meta AI game design proposed in this game was implemented in several game scenarios to be compared and evaluated. The proposed Meta AI game design was evaluated by 31 respondents using Game Experiences Questionnaire (GEQ). Overall, the results show that the game with Meta AI and Augmented Reality implemented significantly improved the Game Experiences Questionnaire (GEQ) score and the player's overall satisfaction compared to the other game scenarios.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Towards trustworthy multi‐modal motion prediction: Holistic evaluation and interpretability of outputs

AbstractPredicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi‐modal). Most prior approaches proposed to address multi‐modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. The authors aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs. First, the evaluation metrics are comprehensively analysed, the main gaps of current benchmarks are identified, and a new holistic evaluation framework is proposed. Then, a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, an intent prediction layer that can be attached to multi‐modal motion prediction models is proposed. The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi‐modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability.

Creation and application of artificial intelligence for public purposes: a comparative legal analysis.

The article focuses on a comparative analysis of existing approaches regarding the compliance of the artificial intelligence (AI) designed for public purposes in foreign countries and Russia with the current national strategies and regulatory approaches. As part of the study, the research identified the basic problems in the field of transparency in the decision-making of artificial intelligence, specified challenges to implicit regulatory nature for AI in the public sphere arising from the technical design of AI systems designed by developers; suggested theoretical and practical situations of using artificial intelligence that does not comply with the principles of designing AI on the basis of fundamental legal norms; and outlined possible compensatory legal measures that ensure the safe integration of artificial intelligence into the Russian public sphere. The subject of the study covers the issues of the influence of the design of artificial intelligence on its subsequent application in the public sphere. The object of the study focuses on the normative documents, recommendations and other documents regulating the issues of artificial intelligence preoccupation for public legal relations in Russia and foreign countries, judicial practice, academic publications and analytical reports on the issues under study. The research methodology integrates a complex of modern philosophical, general scientific, special scientific methods of cognition, including dialectical, systemic, structural-functional, hermeneutical, comparative legal, formal legal (dogmatic), etc. Within the framework of this study, special emphasis is placed on the implementation of a comparative legal study of the problems of designing artificial intelligence and its subsequent application within the public law field in the context of the problems inherent in the basic stage of creation. Subsequently, potential scenarios for regulating responsibility for AI actions are identified. The research has resulted in asset of measures that can be applied in the legislative and law enforcement practice of relevant authorities implementing the integration of artificial intelligence into the sphere of public relations in Russia, as well as in the scientific field in order to determine subsequent vectors for analyzing the minimization of AI bias as a result of incorrect technology design in violation of the basic legal structures.

AI-Powered Personalized Mobile Education for New Zealand Students

This research endeavors to develop and assess a customized mobile education system for students in New Zealand, employing the principles of artificial intelligence (AI) and user-centered design (UCD). The objective is to overcome the limited personalization observed in current mobile education solutions by offering tailored learning content and recommendations based on individual preferences, thereby accommodating the diverse requirements of students. A mixed-methods approach will be utilized, encompassing user research, persona development, user journey mapping, design, development, and evaluation. Participants, including New Zealand students, parents, and teachers, will actively engage in multiple research phases to ensure the effective implementation of user-centered design principles. By showcasing the potential of AI-driven personalization in enhancing the learning experience for students, this study contributes to the growing utilization of AI algorithms and systems within the educational context.

Artificial Intelligence and Morality: A Social Responsibility

Both the globe and technology are growing more quickly than ever. Artificial intelligence's design and algorithm are being called into question as its deployment becomes more widespread, raising moral and ethical issues. We use artificial intelligence in a variety of industries to improve skill, service, and performance. Hence, it has both proponents and opponents. AI uses a given collection of data to derive action or knowledge. There is therefore always a chance that it will contain some inaccurate information. Since artificial intelligence is created by scientists and engineers, it will always present issues with accountability, responsibility, and system reliability. There is great potential for economic development, societal advancement, and improved human security and safety thanks to artificial intelligence.