The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.

This research aims to determine the role of developing and implementing discovery learning models to develop students' critical thinking skills in mathematical story problems. The search was carried out in the even semester of 2022/2023 with research subjects totaling 30 fourth semester elementary school education students. The research was conducted using qualitative analysis methods with data collection through observation, interviews, and documentation. The discovery learning model is implemented through learning stages, namely training students to use various concepts, principles, and skills they have learned to solve the mathematical story problems they face. The research results show that the discovery learning model has an impact on the development of critical thinking skills because the research's observations of critical thinking abilities result in the capacity to recognize, evaluate, and resolve narrative difficulties as well as to reason rationally and ethically to conclude. The discovery learning model provides students with the opportunity to apply learning based on solving mathematical story problems, allowing students to develop critical thinking skills.

Quantum-resistant cryptography develops cryptographic protocols and algorithms that can withstand attacks from quantum computers. Quantum computers can solve certain mathematical problems much faster than classical computers, making current encryption techniques like RSA and ECC vulnerable to quantum algorithms such as Shor's algorithm. To address this, quantum-resistant cryptography focuses on creating new algorithms based on mathematical problems that are difficult for quantum computers to solve efficiently. Examples include lattice-based, code-based, hash-based, and multivariate polynomial cryptography. The National Institute of Standards and Technology (NIST) is leading efforts to standardize these algorithms to ensure they are compatible with various systems and applications. While these quantum-resistant algorithms offer protection, they may involve higher computational complexity and larger key sizes compared to traditional methods, posing implementation challenges that ongoing research aims to address.

The rapid growth and application of AI has ushered in ground-breaking technologies like LLMs. However, these innovations also bring significant challenges related to transparency and accountability, especially considering the complex neural network architectures and vast training datasets. This chapter thus explores the journey of AI from rule-based systems to the current ML and deep neural network, identifying the black box problem that plagues the decision-making process in LLMs. The chapter introduces strategies for enhancing transparency using explainable AI (XAI) frameworks to address these issues, offering practical solutions to quantify and improve transparency. Accountability is also emphasized through a detailed protocol for assigning responsibility across AI development phases, reinforced by ethical auditing and reporting methodologies. Mathematical equations and frameworks are also presented to compute transparency scores and accountability measures, providing organizations with structured, actionable guidelines for building transparent, fair, and ethical AI systems.

The development of artificial intelligence has significantly affected all facets of human life, leading to many ethical and legal questions that have positive and negative consequences for individuals, organisations, and society. Amidst this lies the interaction between intellectual property rights and ethical behaviour in the development and use of artificial intelligence. While intellectual property acts as a catalyst for innovation, ethical behaviour ensures responsible and accountable artificial intelligence consistent with social standards. Accordingly, the beneficial effects of artificial intelligence can only be guaranteed by balancing intellectual property rights and ethical behaviour. This chapter thus discusses the notion of intellectual property and ethical behaviour in the context of artificial intelligence by providing a comprehensive historical review and reflecting on the creation and implementation of ethical artificial intelligence while preserving intellectual property rights.