Susanta Das

Misinformation becomes viral in the field of the digital world, which lowers the confidence of people towards the schools, universities, and other learning institutions on their authority. Students and educators are getting increasingly susceptible to the inability to differentiate between credible information, and the school system cannot position itself as the credible source of knowledge. The chapter examines misinformation ecosystem and its impact on individuals in terms of their credibility and what ought to be done in a post-truth world in terms of the importance of educational institutions. It, further, explores the pedagogical, institutional, and technological strategies of misinformation counteractions. Finally, it concludes providing useful wisdom of how to grow in critical thinking, resilience and learning to seek the truth in education.

AI is changing development, testing, & delivery process of medicines to patients. Earlier, making a new drug would be more than 10 years & cost billions. Most drugs would fail test due to side effects, low effectiveness or may not be easily targeted among suitable patients. Analysis of a large volume of medical & scientific information at a reasonable speed promises AI to accelerate drug development, reduce costs & increase accuracy. AI has the ability to identify patterns in complex data that would be overlooked by human/conventional systems. AI assists researchers to spot superior drug targets, create more secure & efficacious medicines, & strategize utilization of clinical tests. Even virtual patients can be modeled & different approaches to treatment can be tested before conducting trials in real life. Promising results are already being delivered through using AI by many companies to make drug discovery process faster, more individual, & personal. This is a significant transformation of the classical approaches to use of AI & data-driven studies in pharmaceutical sphere.

The unparalleled growth of Internet of Things (IoT) ecosystems has brought up complicated cybersecurity issues since devices are heterogeneous, interconnected on a vast scale, and attack patterns are undergoing change. This chapter focuses on the aspects of the threat surfaces designed to be utilized by the IoT and the drawbacks of the traditional centralized security controls. It discusses the use of blockchain technology to offer decentralized trust, immutable data and safe logging, and machine learning methods to detect anomalies in real-time and prevent intrusions. A blockchain-machine learning model is suggested to improve the resilience, transparency, and automated access control in IoT settings. Supervised learning, deep learning, federated learning and protocol-specific intrusion detection model have proven to be effective as shown in experimental use cases. The chapter also addresses the main constraints, such as the scalability, adversarial vulnerability, interpretation issues, and the research perspectives in the future in terms of secure and sustainable IoT ecosystems.

Cyberattack on various digital systems has become a global issue. Quantum computing is expected to be a solution to these problems. This chapter explores a comprehensive analysis on the connection between quantum computing, cybersecurity, & sustainable development. It also examines the exploration of the systematic review on the quantum algorithms such as Shor's & Grover's, which can enhance data confidentiality and integrity. This chapter discusses the PQC solutions, incorporating lattice-based schemes and integrating the QKD. It will also discuss how quantum technology addresses emerging threats in security systems and enables innovative business strategies by solving the complex cryptographic challenges. This chapter also analyses the quantum-resistant cryptographic methods in mitigating cyber risks and financially sensitive data, as well as the role of machine learning in fraud detection, blockchain, IoT, risk assessment, and optimization of trading algorithms. It also highlights how quantum-enhanced cybersecurity can support UNSDGs such as SDG 9 and SDG 16 by financial systems.

Agricultural industry is dealing with previously unheard-of difficulties as it works to provide the food needs of the world's fast expanding population-expected to approach 10 billion people by 2050. Food production must rise significantly because of this demographic shift-an estimated sixty percent higher than it is now. AI has become a game-changing tool in answering these issues, especially in the field of seed management. To improve productivity & sustainability, this chapter exemplifies many uses of AI in agriculture, with a particular emphasis on how it may optimize seed selection, cultivation, & management. AI technologies use ML, predictive modeling, & sophisticated data analytics to assess large datasets, such as past crop performance, soil health & meteorological conditions. Farmers may choose the best seed kinds for certain conditions by using AI to evaluate this data and make well-informed judgments. In addition to increasing crop yields, this data-driven strategy reduces resource waste, which promotes more environmentally friendly farming methods.

A revolutionary paradigm for education is presented by the fusion of AI & mathematics, which promises enhanced teaching strategies, adaptive evaluations, and individualized learning. AI tools can improve student comprehension and engagement by producing various explanations for mathematical topics, customizing learning experiences, and providing instant feedback. AI can also be used by educators to automate administrative duties, which will free up time for student interaction and better instruction. Creation of AI-powered adaptive learning platforms, intelligent tutoring programs, & automated grading tools—all of which can customize math instruction to meet requirements/skills of specific students—is made possible by this confluence. The potential of convergence of AI & mathematics is examined in this chapter, along with the opportunities & problems that result from this integration & its effects on teaching, learning, & evaluation. The way we teach/study maths could be completely transformed by the new paradigm for education brought about by the confluence of AI & maths.

An Interactive VR Platform with GenAI Support for Learning Data Structures is a new educational tool that uses virtual reality to improve the understanding of abstract computer science concepts. The need for such a tool arises from the challenge of understanding abstract concepts of Data Structures and Algorithms (DSA) faced by students. This new educational tool combines virtual reality with AI and aims to make the process of understanding data structures more intuitive and interactive. The AI technology will enable the creation of educational content on the fly as students engage with the educational tool. This educational tool will also enable a 3D experience that will allow students to perform various actions on different data structures. The system will provide a multi-sensory experience to the students where they can view as well as perform various actions on the data structures in a 3D reality. The aim of the new educational tool will be to increase the students' understanding of data structures and enable them to implement data structure concepts on a real-world scenario.

Artificial Intelligence (AI) has become the indispensable element of the modern educational process that offers groundbreaking instruments to make the process more efficient, creative, and productive in terms of learning, creation, and academic achievements. Thoughtfully tutoring systems and customized learning guides are just but few examples of AI applications that are changing how students learn, collaborate and develop the requisite critical skills. Adaptive learning, automated testing and content development have become more effective with the use of AI in education, and has provided learners with an opportunity to engage with the learning content more interactively and meaningfully. Also, AI-based applications can be used to enhance time management, research assistance, and language translation to make the educational process more universal and globalized. The chapter analyses various AI tools that have been student specific and explain their functionality, advantages and its prospects on the learning environments.

ML is an important branch of AI, which allows a computer to get patterns based on the data & make decisions/predictions without any explicit programming. ML methods are used in many fields due to fast development of digital technologies & access to massive data. In this chapter, we give a review of two important ML paradigms - supervised & unsupervised learning. Labelled datasets are utilized to supervised learning to train predictive models based on classification, regression tasks with algorithms like decision trees, random forests, naive bayes, support vector machines, & neural networks. Unsupervised learning analyses unlabelled data to extract concealed trends & associations in the data. Pattern discovery & exploratory data analysis techniques like dimensionality reduction & clustering, are frequently employed to analyse data patterns. The chapter also addresses the performance evaluation metrics, ML issues, & their use in practice, with a focus on the ways in which these methods contribute to process of making decisions & informing about the capability to analyse complex data.

Intelligent tutoring systems, adaptive learning platforms, automated grading systems, & Gen AI applications are some of the tools used in universities to enhance educational practices. This chapter examines the connection between AI and higher education and puts the idea in its professional growth of academicians in the era of AI. It talks about the increasing applications of AI technologies in learning and teaching practices and how such tools can be used to facilitate data-driven decision making and customized learning. Another point that the chapter discusses is the changing of academic roles and the need to impart new digital competencies in educators. Some of the most important aspects of professional growth are AI literacy, digital pedagogical skills, data literacy, ethical awareness, institutional training programs, and lifelong learning. The chapter concludes that effective implementation of AI in higher education is hinged on the constant learning and development of the institution and the capacity of the teachers to learn in fast changing technological environments.