Abstract
This chapter introduces the book’s central theme: the complex and evolving relationship between academic integrity and artificial intelligence (AI). AI is concerned with developing computers and machines to carry out functions generally associated with human intelligence, including learning from data, making decisions, solving problems, predicting results, understanding language, and identifying patterns. A large language model (LLM), such as ChatGPT, is a typical example of an AI that has significantly influenced how teaching, learning, and research are conducted. Types of AI technologies, such as natural language processing tools, big data, and analytics platforms, have also offered significant educational benefits, such as personalised support for learners and providing educators with tools for generating learning resources, identifying students who are at risk of failing, and automated grading and data-driven insights. As these tools become more available to students and educators, concerns about their impact on academic integrity and the associated ethical implications are growing. Academic integrity embodies a dedication to upholding honesty, trust, fairness, respect, and responsibility across teaching, research, and service. This edited volume brings together diverse contributions from different researchers on the complex relationship between AI and academic integrity. Chapters covered case studies, theoretical and conceptual frameworks, and proof-of-concept applications that address emerging challenges in assessment and AI-driven grading. Through these contributions, the volume aims to provide insights into how educators and institutions can adopt ethically grounded, inclusive strategies that uphold the core values of academic integrity in teaching and learning.