The application of artificial intelligence and Big Data analytics in personalized learning

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

Within the past few decades, the increasing use of advanced technology in the manufacturing industry has drawn extensive study attention from all around the world. Smart technology is technology that gives previously thought-to-be-inanimate objects cognitive awareness through the application of artificial intelligence, machine learning and big data analysis. Smart Technology (ST) involves many emerging technologies like the internet of things, Industrial Internet of Things (IoT), cyber-physical production system artificial intelligence, blockchain and systematic data analysis. We provide an overview of different smart technologies to control the intensive features of computer and sensor technologies to diminish the gap between humans and machines. This research paper shows the complete literature review of ST to increase the production and maintenance of machinery equipment and future threats faced by ST. This study also includes the survey report of the rate of adoption today as compared to the past five years, to obtain real-time results, cost-effective techniques and the use of different sensors to reduce human workload in the manufacturing sector.

The operations of many industries, such as banking, insurance, energy, healthcare, and retail, have been improved and transformed by the application of artificial intelligence. The education sector is the particular one which has more potential for artificial intelligence and its applications. Universities and colleges are currently facing a number of challenges, including high rates of dropouts and student disengagement in their classes, as well as the failure of traditional learning techniques in the face of ongoing outbreaks. Major issues in the educational sector can be resolved by the proper application of artificial intelligence and big data, which can facilitate learner-centred approaches as per the needs of today’s era. This can be better described as a personalised learning approach, in which the needs of each and every student can be considered and education can be provided uniquely for them. In this way, the educator can provide a better learning experience according to the needs and ability of the learners/students. Thus, it also assists in enhancing the motivational level of the learners and minimising the dropout rates of the students. The main purpose of the study is to examine the role of artificial intelligence and big data in the adaption of the E-learning system for imparting better education facilities to the students of various colleges and universities situated in Saudi Arabia. The sample dataset for this study was collected through a purposive sampling technique from the 290 students enrolled in numerous higher educational institutions. The collected data were analysed using SPSS and SmartPLS 3 software. The findings of this study reveal that AI and big data assist educators and students as well to run the teaching and learning process smoothly even in the period of an ongoing pandemic at the higher levels of the education system. It also explores the educational inferences of transpiring technologies in the ways of students’ “learning and institutions” teaching and their adaption. In recent eras, technological evolution and an increase in the adoption of latest technologies in the field of higher education have been scouted to forecast the essence of higher education globally in the near future, where AI will be the gist for universities. This study reveals some issues faced by higher educational institutions and students related to the adaptation of these technologies for learning, teaching, administration, and student support. The implications of the present study will be helpful for students and teachers to access educational resources effectively and for educational institutions to improve the efficiency and personalization of learning tasks. Furthermore, it will allow students to get more time with their respective teachers.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

In recent years, the application of artificial intelligence and big data analysis has become increasingly common, including in areas related to security and cybersecurity. The use of artificial intelligence and advanced big data analysis tools has enabled new critical perspectives on many different areas of the world around us. The purpose of this volume is to present the perspectives of researchers, experts and scientists, who are concerned with the problem of the distortion of reality by the authors of false narratives. The book focuses primarily on security matters, issues demanded by the international political and economic situation. Disinformation is a tool of hybrid war, shaping the social and economic environment long before the start of the Russian invasion of Ukraine. Since February 2022, the issues of post-truth, fake news, and disinformation have taken on additional gravity.

The rapid advancements in Artificial Intelligence (AI) and Big Data Analytics have significantly transformed financial markets, enabling more precise, data-driven, and strategic decision-making. This study explores the profound impact of AI-driven analytics in financial decision-making, focusing on how machine learning algorithms, predictive analytics, and automation enhance market efficiency, risk assessment, and investment strategies. The research highlights how AI-powered Big Data Analytics processes vast amounts of structured and unstructured financial data to identify patterns, trends, and anomalies that influence market movements. It further examines how financial institutions leverage AI for fraud detection, sentiment analysis, and algorithmic trading, leading to more informed and efficient investment decisions. Through a survey-based approach, this study collects data from financial professionals, analysts, and investors to evaluate their perspectives on AI’s role in improving data accuracy, reducing uncertainty, and optimizing financial strategies. The findings provide valuable insights into how AI-driven analytics contributes to minimizing risks, enhancing forecasting accuracy, and supporting regulatory compliance in financial markets. The study concludes that the integration of AI in Big Data Analytics is revolutionizing financial decision-making by improving efficiency, reducing human biases, and enabling real-time strategic actions. However, challenges such as data privacy concerns, ethical considerations, and the need for advanced AI governance frameworks remain crucial areas for future exploration. Keywords—Artificial Intelligence, Big Data Analytics, Financial Markets, Strategic Decision-Making, Algorithmic Trading, Risk Assessment, Predictive Analytics.

In today’s era, the healthcare domain is highly influenced by the widespread applications of big data analytics and artificial intelligence (AI). These technologies are being used for high-level molecular research, drug development and predictive analysis of various diseases. Currently, big data and AI are being used in several aspects against corona virus disease 2019 (COVID-19) pandemic. Literature as well as the genomic studies available for various strains of severe acute respiratory syndrome coronavirus 2, suggest how the concepts of big data and AI are being implemented to understand COVID-19 better which can assist with its antiviral drug development as well as vaccine production. These concepts of big data and AI have effectively helped with the contact tracing, epidemiology, molecular studies, medical diagnosis and treatment of COVID-19 that can help against future pandemics. Similarly, with this computational approach, various disease patterns are being recognized in different types of cancer. AI plays a major role in biomarker identification for disease progression to recognize novel hallmarks of cancer as well as in the predictive analysis of the disease. This allows an early-stage diagnosis of cancer which leads to a better prognosis. With an interdisciplinary collaboration of big data and predictive analytics, patient risk can be analyzed and estimated concerning the new treatment options available for cancer. Such high-throughput technologies have improvised the diagnosis and treatment options available for various diseases. With the advancements in science and technology and the introduction of computational models, public health and medicine have been completely revolutionized for the betterment of society.

The paper is devoted to the changes in fighting delinquency connected to the application of artificial intelligence and Big Data analytics. The focus of the paper has been made on the Social Credit System and related advanced mechanisms of control and surveillance, which are currently being built and implemented in China. The issue of how the latest technologies of social control impact the fight against crimes and administrative offences has been examined. The transforming effect of introduction of the Social Credit System and algorithmic mechanisms of social control upon the legal system and some of its institutions (notably, the legal liability institution, the punishment, the concept of an offender) has been assessed in the paper. The authors come to the conclusion that the introduction of the Social Credit System in China and the development of algorithmic mechanisms of social control and crime prevention may lead to the separation of punishment from the construct of legal liability and the concept of an offence as a guilty deed.

Adverse drug reactions (ADRs) remain a major challenge in clinical pharmacology, contributing significantly to patient morbidity and mortality. Traditional methods of ADR detection, such as spontaneous reporting systems (SRS) and clinical trials, often fail to identify ADRs in real-time, limiting their effectiveness. However, the advent of Artificial Intelligence (AI) and Big Data analytics has opened new avenues for improving ADR prediction and prevention. By leveraging vast amounts of clinical, genomic, and pharmacological data, AI models, particularly machine learning (ML) and deep learning, offer powerful tools for predicting ADRs with high accuracy. This review explores the application of AI and Big Data technologies in ADR prediction, detailing the methodologies, data sources, and models used, as well as challenges and opportunities for their future implementation. Furthermore, the integration of real-time data and AI-based algorithms into clinical practice could enhance pharmacovigilance systems, leading to safer drug prescriptions and better patient outcomes.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

In today’s digital era, businesses are increasingly adopting innovative approaches to gather valuable data for informed decision-making and maintaining competitiveness. This study examines the application of big data analytics and predictive artificial intelligence (AI) in sales forecasting, a task that remains challenging but essential for effective demand planning and resource allocation. Traditional forecasting methods often fall short in dynamic market environments, whereas advanced techniques offer greater accuracy. Using real-world data, this research employs machine learning algorithms to uncover hidden patterns and generate reliable sales predictions. A predictive model based on the XGBoost algorithm was developed and achieved a high R² score of 0.94, with cross-validation yielding a consistent mean score of 0.94 (SD = 0.02), indicating strong predictive power and stability. The findings demonstrate the effectiveness of big data and predictive AI in improving forecast accuracy and supporting data-driven business decisions. This study highlights the practical value of integrating advanced analytics into sales forecasting processes for enhanced strategic planning.

Smart maintenanceis a model created on the concept of Industry 4.0, and uses its advanced technologies, the Internet of Things (IoT), artificial intelligence (AI) and predictive big data analytics (BDA), in order to reduce equipment failures and extend its life, thus reducing maintenance costs, and technological resources are used optimally. By monitoring data in real time, from sensors and IoT devices, the condition of the equipment is predicted, decisions are made automatically, and the operational performance of the equipment (OEE) is improved. Equipment failure prediction is done by analyzing the behavior patterns of equipment entities, which are obtained by machine learning (ML), generated by digital twins (DT) and supported by cloud computing. Today, we are witnessing the full maturity of Industry 4.0, and the emergence of its advanced or future version, Industry 5.0. However, we must state that the creators of the Industry 4.0 model (German Academy of Engineering Sciences) believe that the Industry 5.0 model today, as promoted by its creators, is not a NEW model, but only a somewhat improved version of Industry 4.0 in the areas of: greater human integration into this automation model (operator 4.0/5.0), greater environmental aspects of production (green production) and the application of artificial intelligence (AI), which we are witnessing today in the Industry model as well 4.0. Starting from the above facts, this paper deals with the current level of development of the maintenance model in the context of Industry 4.0, which is the smart maintenance model (SM). System analysis of predictive maintenance model, smart maintenance framework model was performed. Some results of our research in this area are also given at the end of the paper

This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.

The application of artificial intelligence (AI) in education has received widespread attention, especially in personalized learning and teaching management, where it has shown significant potential. However, current research still has limitations, especially in addressing the ethical and privacy challenges of AI technology and the decline in students' self-discipline. This paper analyzes the application of AI in the classroom and explores its positive impacts on enhancing teaching efficiency, personalized learning, enhancing teaching tools, and supporting special education. The study found that although AI has significant advantages, its widespread use has also led to problems such as technological dependence, academic dishonesty, privacy breaches, and moral bias. Based on this, the paper suggests that AI literacy and critical thinking should be cultivated when introducing AI technology in the classroom to ensure the rational use of technology. Additionally, clear ethical norms and privacy protection strategies should be established to balance the relationship between technological progress and educational ethics, thereby driving the comprehensive improvement of educational quality.