Artificial Intelligence Learning Research Articles

Artificial Intelligence Usage in Higher Education: Influence on Online Learning

The conceptualization and implementation of artificial intelligence (AI) in online learning have shown the most effective ways to use AI pedagogically. Viewing AI as a tool for online learning is crucial. By integrating AI technologies into teaching practices, educators can create more engaging, effective, and personalized learning experiences for their students. However, it is essential to use AI responsibly, ensuring it complements and enhances the role of educators rather than replacing human interaction and support. This article begins by outlining its purpose and providing an overview of AI history. It will presents a comparative literature analysis, discussing the advantages and disadvantages of using AI in online learning. Finally, the article explores how to incorporate AI into online teaching practices and offers strategies to encourage students to utilize AI effectively.

AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration

Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.

Artificial Intelligence in Cybersecurity : Advancing Threat Modeling and Vulnerability Assessment

This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.

The trajectory of artificial intelligence for competency-based personalised learning: past, present and future

PurposeThe study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning.Design/methodology/approachThe study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app.FindingsThe analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche).Originality/valueFollowing an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.

Stances toward generative AI in teaching and learning: An introduction to the special issue

ABSTRACT This article introduces the special issue of Communication Teacher on generative artificial intelligence (AI) in communication education. An important and unique contribution of this issue is that it demonstrates a range of stances that communication educators have toward generative AI. Each article is introduced as illustrating one or more of four stances: Observe, Replicate, Enhance, and Transform.

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study

ObjectiveThe purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist. Materials and MethodsThe diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation. ResultsThe differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p<0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst. ConclusionAI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.

The Impact of Artificial Intelligence on Marketing Strategies

The research project examines the impact of Artificial Intelligence (AI) on marketing strategies across various industries, focusing on how AI-driven technologies enhance customer engagement and optimize marketing campaigns. The objective of this study is to explore the diverse effects of AI on marketing practices and to identify the challenges and ethical considerations that arise from its implementation. The research was conducted through a qualitative methodology that conducted interviews with 18 marketing professionals to gather information about their experiences and perceptions regarding AI in marketing. The findings reveal a significant trend toward the adoption of AI and machine learning technologies. Participants noted their effectiveness in personalizing customer interactions and improving data-driven decision making. Key themes include the success of AI-driven tools such as recommendation systems and predictive analytics in enhancing marketing effectiveness. The researchers highlighted some ethical concerns which included data privacy, algorithmic bias, and the need for transparency. The research study validates the importance of data literacy and AI competence among marketing professionals. It suggests that organizations prioritize skill development to adapt to the evolving marketing landscape. The research contributes to the existing body of knowledge by emphasizing the dual role of AI as a transformative force in marketing while also raising critical ethical issues. The research offers practical recommendations for businesses to effectively utilize Artificial Intelligence while ensuring they remain competitive in the global marketing landscape.

Transform Financial Data into Strategic Insights Using the Sap Business Technology Platform

This article examines the groundbreaking fusion of SAP Business Technology Platform (BTP) and Artificial Intelligence (AI) in the realm of financial reporting, emphasizing their transformative role in translating financial data into strategic insights for executive leadership. By introducing automated commentary generation, this integration enables BTP to convert intricate financial datasets into coherent narratives that furnish decision-makers with actionable intelligence. Positioned at the nexus of AI progress and financial analysis, BTP facilitates the seamless conversion of complex data into narrative or commentary reports, fostering coherence, insightfulness, and accessibility for decision-makers. The discourse delves into the underlying mechanisms of BTP functionality in financial reporting, spanning data interpretation, language processing, commentary generation, and customization, thereby enhancing report efficiency, precision, and accessibility. Through meticulous exploration, this article elucidates BTP's contributions to financial reporting commentary, including enhanced efficiency, accuracy, scalability, and personalized delivery of financial insights. Furthermore, it confronts the challenges inherent in BTP application, encompassing technical intricacies, ethical considerations, and the constraints of current technologies in capturing financial intricacies. Looking ahead, the article envisions a future where ongoing advancements in AI and machine learning refine BTP capabilities, furnishing even more sophisticated and nuanced financial insights to underpin strategic decision-making at the highest echelons of business leadership.

Usage of Information technology for the success of managing various stages of the COVID-19 pandemic

This study explores the crucial role of Information and Communication Technology (ICT) in managing the COVID-19 pandemic, focusing on its impact on surveillance, contact tracing, patient monitoring, telemedicine, artificial intelligence (AI), vaccination logistics, communication, and public education. ICT has facilitated the collection and analysis of real-time data, enabling authorities to make informed decisions about containment strategies. Digital contact tracing systems have been instrumental in limiting the virus's spread, while remote patient monitoring and telemedicine have allowed healthcare services to continue with minimal transmission risk. AI and machine learning have enhanced the speed and accuracy of COVID-19 diagnosis, and ICT has streamlined vaccine distribution and monitoring to ensure equitable access. Additionally, online platforms have played a key role in public education, fostering collaboration among researchers and promoting interdisciplinary research efforts. The study emphasizes that the continued integration of ICT into healthcare systems is critical for addressing future global health crises. However, policymakers must invest in ICT infrastructure, bridge the digital divide, and ensure that marginalized populations have equitable access to these technologies. The findings highlight the importance of ongoing ICT development to improve healthcare outcomes in future pandemics and suggest that collaboration across sectors is essential for maximizing the potential of these digital tools. Ultimately, ICT has been pivotal in the effective management of the COVID-19 pandemic and will be vital in future crisis preparedness.

Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV). This retrospective study included 217 patients (199 in the training/validation of the AI model and 18 in the testing set) with a diagnosis of unilateral type 3 MNV. The purpose of the AI model was to predict fellow eye neovascularization within 24 months after the initial diagnosis. The data used to train the AI model included a baseline fundus image and horizontal/vertical cross-hair scan optical coherence tomography images in the fellow eye. The neural network of this study for AI-learning was based on the visual geometry group with modification. The precision, recall, accuracy, and the area under the curve values of receiver operating characteristics (AUCROC) were calculated for the AI model. The accuracy of an experienced (examiner 1) and less experienced (examiner 2) human examiner was also evaluated. The incidence of fellow eye neovascularization over 24 months was 28.6% in the training/validation set and 38.9% in the testing set (P = 0.361). In the AI model, precision was 0.562, recall was 0.714, accuracy was 0.667, and the AUCROC was 0.675. The sensitivity, specificity, and accuracy were 0.429, 0.727, and 0.611, respectively, for examiner 1, and 0.143, 0.636, and 0.444, respectively, for examiner 2. This is the first AI study focusing on the clinical course of type 3 MNV. While our AI model exhibited accuracy comparable to that of human examiners, overall accuracy was not high. This may partly be a result of the relatively small number of patients used for AI training, suggesting the need for future multi-center studies to improve the accuracy of the model.

Application and Innovation of Artificial Intelligence in Forensic Medicine

As the fourth industrial revolution, artificial intelligence is reshaping many industries. It has become a new cornerstone of digital conversion, and it is no exception in the field of forensic medicine.This paper mainly discusses how artificial intelligence can solve the problems related to forensic medicine.How to use artificial intelligence technology to develop into an auxiliary tool in the field of forensic medicine.This paper summarizes the exploratory data analysis, statistical modeling and machine learning in artificial intelligence, extracts insights and knowledge from the data, and applies them to various fields of forensic medicine.Artificial intelligence will improve the accuracy and efficiency of forensic work: it can automate some tasks and improve the quality of evidence. The comprehensive analysis results show that the artificial intelligence proposed in this paper will be an important auxiliary in the three directions of forensic medicine.

AI and Thinkable-Assisted learning media for physical education: a descriptive study on collaborative lecturer education

This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills. This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills.

The Investigation of Artificial Intelligence in Cultural Relics Protection

It is well-known that the importance of cultural heritage in history, culture, art and other aspects is self-evident. However, due to various force majeure damage, the situation of cultural heritage protection is becoming more and more severe. This article will review and look forward to the application of Artificial Intelligence (AI) in cultural relics protection. The method section of this paper demonstrates various AI technologies such as Generative Adversarial Networks (GAN), deep learning, and reinforcement learning, and applies them to cultural relic digitization, damage detection, virtual restoration, and cultural heritage management and display. The next section outlines the specific tasks for which these AI techniques are applicable, demonstrating the potential of these innovations to improve accuracy and efficiency in work. In the subsequent discussion section, the current limitations and challenges of AI in this field are analyzed, such as data dependency and “black box” characteristics. At the same time, the future direction of AI is discussed, such as interpretable AI (XAI) and adaptive learning. Finally, this paper concludes that although AI has challenges, it will play an increasingly important role in the field of cultural heritage protection in the future.

Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems.

There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.

The Role of Artificial Intelligence (AI) in Personalized Learning: A Case Study in K-12 Education

This study investigated the mediating role of artificial intelligence (AI) in intensifying personalized learning in K-12 education by employing qualitative case research. The study examines how an AI deployed within a K-12 school changes the engagement, academic performance, and role of teachers through its implementation. Results show that AI tools lead to large improvements in student achievement through personalized learning paths, increased engagement, and the immediacy of feedback. Nevertheless, challenges including data privacy ponderings and the digital gap are significant insights for educators to consider as well. The report suggests guidelines for policymakers and practitioners to ensure the productive application of AI in personalized learning, including equal access to technology and continuous teacher upskilling.

Role of AI and Machine Learning in Enhancing Mental Health Care

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into mental health care has gained significant attention in recent years, transforming how mental health disorders are diagnosed, treated, and managed. This research paper examines the multifaceted roles of AI and ML in enhancing mental health care, including their applications in early diagnosis, personalized treatment plans, and patient monitoring. The paper further explores ethical considerations, challenges, and future directions for AI and ML in mental health, emphasizing the need for collaboration between technology developers and mental health professionals. The findings indicate that AI and ML can significantly improve mental health outcomes, increase accessibility to care, and optimize resource allocation in mental health services.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Pathways to Enhance New Quality Productivity in New Liberal Arts Education Through Artificial Intelligence

This paper explores the pathways through which artificial intelligence (AI) enhances new quality productivity in new liberal arts education. By analyzing the role of AI in personalized learning, interdisciplinary integration, and the application of virtual reality/augmented reality technologies, it reveals how AI technology promotes the development of students’ innovative capabilities and productivity in the context of new liberal arts education. The study shows that AI is not only a technical tool but also a driving force for transforming educational models and fostering knowledge innovation. Further exploration of the deep integration of AI and new liberal arts education is necessary to promote comprehensive social progress.

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.