Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning

In an era characterized by significant technical advancements in the field of Artificial Intelligence (AI), it is crucial to comprehend AI by considering its origins and future prospects. This chapter examines the historical origins of artificial intelligence (AI) and explores its relationship with philosophy. It also delves into the significant inquiries that philosophy poses regarding AI, encompassing its metaphysical, epistemological, and axiological dimensions. The chapter additionally provides an overview of the historical context of artificial intelligence (AI), its various manifestations, its theoretical underpinnings, and a framework that establishes a correlation between humans and machines, referred to as “Human-machine Teamwork.” The chapter also explores the importance of AI in several fields and illuminates emerging areas where artificial intelligence is also examined, giving rise to significant inquiries. The objective of this chapter is to offer comprehensive knowledge and a fresh viewpoint on the examination of AI by its users, producers, and designers.

This chapter will emphasize the advances in artificial intelligence and bioinformatics to accelerate the drug design and discovery process. The field of artificial intelligence (AI) aims to develop machines and systems that can perform tasks that require human intelligence, such as learning, reasoning, and decision-making. One of the domains where AI has been applied is bioinformatics and computer-aided drug designing (CADD). Bioinformatics is an interdisciplinary field that uses computational methods to analyze biological data, such as genomic, proteomic sequences and structures, and gene expressions. CADD is the process of using computational tools to design and optimize new drugs or drug candidates based on their molecular properties and interactions with biological targets. AI can improve both bioinformatics and CADD by providing novel methods for data analysis, pattern recognition, feature extraction, prediction, optimization, and simulation. In this book chapter, we will review the current state-of-the-art and future challenges of AI in bioinformatics and CADD. We will discuss how AI can help solve some of the key problems in these fields, such as data integration, data quality, data interpretation, data visualization, data mining, data modelling, data validation, and data discovery. We will also highlight some of the ethical, social, and legal implications of using AI in bioinformatics and CADD, such as data privacy, data security, data ownership, data sharing, data governance, data accountability, and data responsibility.

Artificial intelligence (AI) has become the latest tool in facilitating the computer-assisted teaching-learning process. The AI-based teaching system is viewed as a personalized one-on-one student-teacher interaction. AI in education is becoming more common and has received a lot of attention recently. This growing interest is likely to have a big impact on higher education. Many educators and educational authorities are considering integrating AI topics into K-12 curricula, to provide school students with insights into these evolving technologies. Recent studies on AI curricula have mostly concentrated on identifying which AI tools are better for student learning and what subject matter knowledge and abilities need to be taught. Since the goal of these studies was to promote information, they designed their curriculum with appropriate content, effective delivery methods, and strategies to increase students' competency levels. Environmental education seeks to explore natural processes and foster the development of skills and attitudes geared towards sustainability and the protection of the environment. Recently, AI has been widely used in geological, environmental, and related research. It can also assist in the exploration of energy resources and minerals. The use of AI in education teaching-learning and academic achievement has been elucidated in this chapter. This chapter also aims to explore the role of AI-assisted teaching of environmental sciences and how it impacts the overall learning experience. Understanding the role of AI in environmental sciences is very important as it can address crucial problems like climate change, early prediction of natural disasters, and many others. Currently, there is a need to develop accurate models at an affordable time and cost. Other than research, the implementation of AI in environmental education can lead to a change in students’ aptitude and interest and can help in the development of sensitivity towards environmental protection.

Pollution is a major hazard to ecosystems, human health, and the stability of the global climate. Acknowledging the shortcomings of traditional methods, this thorough examination investigates the incorporation of Artificial Intelligence (AI) as a revolutionary instrument for reducing air pollution. A summary of the current situation of air pollution is given in this chapter, with a focus on its significant effects. It provides an overview of AI's ability to address environmental issues and lays the groundwork for a full investigation of its uses. This chapter uses satellite technology, sensor networks, and remote sensing to demonstrate how AI is revolutionising air quality monitoring, predictive modelling, and early warning systems. It also emphasizes AI's ability to identify pollution sources, presenting methods for measuring pollution sources and incorporating AI findings into urban planning. It clarifies AI's critical role in influencing public involvement, awareness, and evidence-based policymaking. It provides examples of AI-driven air pollution solutions from around the world, together with best practices and insights into successful projects. It discusses privacy and equality issues as well as ethical issues related to AI in environmental monitoring. It also points the way for upcoming discoveries and lines of inquiry, enabling ongoing progress.

Understanding the underlying physics of a physical system at both the cosmological and molecular scales has been a focus of attention for decades. Modeling the system using ordinary and partial differential equations along with the Markov Chain Monte Carlo technique are the conventional methods being used. These methods have been proven to reconcile accurate results, however, they fail miserably when the physics is not completely known, which leads to the presence of a large number of free parameters in the model describing the system. Recently, conventional methods have been aided by the use of machine learning techniques to solve real-world problems, which include the use of artificial neural networks such as convolutional neural networks, generative adversarial networks, and random forests. The ability of these techniques to understand the complexity of a physical system and predict new physics solely from data has given a new edge to conventional methods. Their prevalent applications lie in parameter prediction, where available data is used to train a neural network model, and then physical quantities are predicted using the trained model. Classification is another fundamental aspect of machine learning that involves predicting the specific family or category to which the provided data pertains. These techniques find an essential place in physics, providing important insights into complex systems.

Artificial Intelligence (AI) is revolutionizing our everyday tasks, and education has certainly not been left behind. AI harnesses technologies such as machine learning, natural language processing, and deep learning, to execute tasks and elevate our problem-solving capabilities. The infinite possibilities that arise due to interactions between atoms and molecules further leading to bond formation are nearly impossible for a human to comprehend. Thus, AI is playing a vital role in understanding chemistry by accelerating research, designing novel molecules, and optimizing processes. AI plays a diverse role, from assisting in drug discovery research to identifying new drug targets to supporting personalized learning experiences that aid students in their learning journeys. AI-powered adaptive learning system identifies a student’s performance and tailor the learning requirements accordingly. Students receive real-time feedback and personalised content helping them to understand the concepts more easily. AI is being used to develop interactive simulations and customized learning programs to help students learn chemistry more efficiently. Virtual laboratories driven by AI provide a safe and reachable environment for hands-on experience. This allows students to be inquisitive about chemical reactions, molecular structures, and their spectroscopic analysis in a risk-free environment. Some examples include Chat GPT, which helps create a customized learning experience for students while helping them answer their queries, an AI-powered tutoring system known as Socratic, which helps the students learn chemistry concepts, and Molecules in Motion (an AI-powered simulation) to inspect the behaviour of molecules. This chapter discusses how the union of AI and chemical sciences has accelerated innovation in the field of chemistry and can further improve learning outcomes.

Artificial Intelligence (AI) is quickly becoming ubiquitous, with applications in all spheres of life. The education sector is also not untouched, in fact students are now relying on AI tools for studying, doing homework, making assignments and reports, and preparing for exams. Teachers are also using AI tools to enhance classwork and assessments. The use of AI in chemistry education is rapidly growing and many AI tools are proving to be quite useful in this regard. However, chemistry being a vast subject with lots of concepts, laws, formulae, reactions, and applications, requires deep understanding and comprehension, which is a challenge for these tools as they are not always accurate and consistent in providing answers. The present chapter gives a brief account of the uses of AI in chemistry, with teaching-learning chemistry, in particular. It explores the advantages and disadvantages of using AI in chemistry education and how AI can be incorporated in classrooms.

This chapter constitutes a comprehensive exploration of the far-reaching impact of Artificial Intelligence (AI) on the domains of commerce and economics. It conducts a thorough analysis, meticulously concentrating on the intricate aspects of analytics and decision-making. Within this dynamic landscape, the research not only elucidates the transformative role of Artificial Intelligence in predictive and descriptive analytics but also delves into its nuanced contributions to tracking market trends, risk mitigation, and the enhancement of operational decision-making processes. Examining the economic implications of Artificial Intelligence, the chapter goes beyond a cursory overview, providing a detailed understanding of documented instances where Artificial Intelligence has led to heightened productivity and subtle yet impactful shifts in labour dynamics. By grounding its analysis in real-world examples, the study aims to uncover the intricacies of how Artificial Intelligence is actively reshaping traditional economic paradigms, potentially ushering in a redefined fabric of commerce. Recognizing the pivotal role of Artificial Intelligence in driving efficiency and innovation, the research places a heightened emphasis on addressing inherent biases embedded within AI models. It advocates not only for the identification of these biases but also proposes meticulous rectification processes to ensure the fostering of fairness and equity in AIdriven decision-making. The study acknowledges ethical dimensions associated with the integration of Artificial Intelligence, highlighting the imperative for responsible Artificial Intelligence development and deployment, especially in economic settings. Furthermore, the paper identifies the essence of the AI-driven era in commerce and economics, foreseeing a landscape where Artificial Intelligence becomes indispensable for informed decision-making. It not only outlines the current landscape but also proposes future lines of inquiry to guide ongoing research. This paper, therefore, stands as a substantive and forward-thinking resource for scholars, practitioners, and policymakers alike, seeking to navigate and understand the evolving intersection of Artificial Intelligence, commerce, and economics.

In an increasingly interconnected and digital era, the role of artificial intelligence i.e. AI in education has gained significant attention. This chapter explores the transformative potential of AI in the realm of English pedagogy, by offering a comprehensive route to enhance language learning through the integration of cuttingedge technology of today’s era. Taking about AI without discussing Generative AI, the content will not be completed and the present chapter will also be incomplete since Generative AI (GAI) finds wide-ranging practical applications in fields as diverse as natural language processing and drug development, as well as the creative sectors. Pedagogically, Generative AI (GAI) enables individualized learning by generating learning materials that are specially designed to meet the needs of each learner. The traditional approaches to teaching English have often been constrained because of the limited, time, resources, and also individualized attention paid to it. AI, with its advanced abilities to adapt, personalize and provide immediate feedback, has the ultimate potential to bring a revolution in the way English is taught and learned. This particular route of language learning begins by examining the foundational elements of AI-driven language education, such as Natural Language Processing (NLP) algorithms and machine learning models. These technologies may help us to create reliable and brilliant virtual tutors and automated assessment tools that can cater to the unique needs and abilities of each learner through analysis. Also, the route dives deeper into the key significance of content personalization. An AI-developed system can conduct a deeper analysis of students’ strengths and weaknesses, allowing personalized and detailed lesson plans and exercises moulded especially for the betterment. A personalized and adaptive content delivery ensures that students remain engaged and motivated, as they receive materials scripted by the AI, which are both challenging and fitted as per their individual needs. This paper also emphasizes the importance of AI-powered assessment tools, which would enable the objective of an accurate evaluation of students’ language skills, which may also reduce the subjectivity in grading and also level up the assessment process. The paper also points out the concerns, which are related to artificial intelligence. Implementing AI in education highlights the need for robust data protection and also the well-being of the learners. It also emphasizes the importance of professional development of educators so that language learning becomes effective from both sides. In conclusion, this chapter, provides a circumstantial route for the transformation of English pedagogy through AI, offering solutions to existing challenges in language education. By including AI-driven platforms, and assessment tools, virtual tutors help in providing an effective learning environment. The integration of AI into English pedagogy represents a promising future in the language learning field making the language learning process interesting.

Artificial Intelligence (AI) has become a game-changer in education, transforming how we learn, and improving the way we conduct research. By using smart algorithms, machine learning, and data analysis, AI is applied in diverse areas, such as intelligent tutoring systems, personalized learning platforms, and automated grading systems. This article explores why it is crucial to use Artificial Intelligence (AI) in academic research nowadays. With research questions getting more complex and lots of data to handle, traditional methods struggle to keep up. AI comes in as a game-changer, helping researchers by doing routine tasks, sorting through big datasets, and finding important patterns. In academic research, AI has proven invaluable. It helps researchers explore complex topics, gain insights, and streamline the overall research process. With its ability to analyze large sets of data quickly, recognize patterns, and offer insights, AI speeds up research significantly. The point is that AI is becoming a must-have tool for universities and researchers. We need it to make our research better, spark new ideas, and keep up with the ever-evolving world of academic exploration. This research article illustrates the development of AI in research and the comparison between AI data analysis and conventional data analysis. This article tries to explore various AI tools and techniques used in academic research, discusses the diverse applications of AI in academic research, outlines the benefits, addresses challenges, and considers ethical considerations in using AI for academic research.

As we navigate the dynamic landscape of education in the 21st century, the role of Artificial Intelligence (AI) in teaching and learning continues to evolve, presenting exciting possibilities and challenges. The future of Artificial Intelligence will transform the educational landscape, offering unprecedented opportunities for personalized, efficient, and engaging learning experiences. The young individual's education levels impact a country's progress. AI will transform tradition al schooling in the future. As robots and technology have mostly replaced skilled labourers, manufacturing industries no longer need them. The educational system has the potential to be extremely effective and customised to a person's personality and abilities. This chapter explores the future trends and innovations at the intersection of AI and education, aiming to provide a forward-looking perspective for educators, policymakers, and researchers. The future of AI in education is examined by emerging technologies, from advancements in natural language processing to the integration of augmented and virtual reality; these innovations promise to enhance the educational experience by fostering immersive and interactive learning environments. In conclusion, a critical aspect explored is the collaborative nature of AI and human educators. Big data analytics plays a role in shaping educational strategies and decision-making, leveraging vast amounts of data generated by students, educators, and institutions.