Generative AI in Higher Education

Front matter

Cecilia Ka Yuk Chan and Tom Colloton

Generative Al in Higher Education

The ChatGPT Effect

Prologue

Acknowledgments

Short Summary of Each Chapter

1.1 1.2

1.3

1.4

1.5

1.6 1.7

1.8

1.9

Artificial Intelligence (AI) 1

1.2.1 Three Types of AI 2

Big Data and the Internet of Things (IoT) 3 1.3.1 What Is Big Data? 4

1.3.2 What is the Internet of Things (IoT)? 7 Generative Artificial Intelligence (GenAI) 9 1.4.1 Applications of GenAI 10

ChatGPT — A Game Changer in Education? 11 Generative AI Concerns and Challenges 14 How Does ChatGPT Work? 15

1.7.1 So What Happens When You Type in a Question

(Also Known as Prompt)? 15 How does GenAI Generate Images from Text? 16

1.8.1 So What Happens When You Type in a Question (Also Known as Prompt) to Generate an Image? 18

Questions to Ponder 19 Personal Reflection 20 References 20

21 2.2

Introduction 24 The Importance of AI Literacy 25 x Contents

2.3 Literacy in the Digital World 25

2.4 Al Literacy Frameworks 28

2.5 The Definition of AI Literacy 32 2.5.1 Expanding the Fundamental AI Literacy Framework:

Incorporating Role-Specific and Multilevel Considerations 34

2.5.2 AI Literacy for Teachers 35

2.6 Advantages and Disadvantages of Developing AI Literacy 38

3.1 Introduction 44 3.2 ChatGPT and GenAI Text Tools 45 3.2.1 What Is ChatGPT? 45 3.2.2. Common Functions of ChatGPT 45 3.3 GenAI Tools and Its Strengths and Potential Opportunities in Higher Education 46 3.3.1 User-Centric Design 47 3.3.2 Humanistic Conversational Style 48 3.3.3. Variability 49 3.3.4 Multimodal Capability 50 3.3.5 Scalability 50 3.3.6 Customisability 51 3.3.7 Comprehensive Coverage in Breadth and Depth 52 3.3.8 Contextual Relevance 52 3.3.9 Multilingual Support 53 3.3.10 Appropriateness of Topics 53 3.3.11 Code Generation 54 3.4 GenAI Tools and its Weaknesses and Potential Threats in Higher Education 55 3.5 Al-Partnered Pedagogy through the Lens of Bloom’s Taxonomy 64 3.5.1 Literary Study of ChatGPT’s GPT-4 via Bloom’s Taxonomy 64 3.6 Redesigning Pedagogical Activities with GenAI 68 3.6.1 List of Pedagogies Facilitated by AlI-Human Partnership 69 3.7 Case Scenarios Embracing ChatGPT into the Higher Education Classroom 74 3.8 Introduction to Prompt Engineering 80 3.8.1 The Essence of Prompts 80 3.8.2 The Prompt Engineering Components 81 3.9 Conclusions 83 Questions to Ponder 84 Personal Reflection 84 References 84

Contents Xi

4.1 Introduction 87 4.2 The Evolution of Assessment and Outcomes-Based Learning in Higher Education 87 4.3 Challenges in Assessment in the Traditional Era 91 4.4 Challenges in Assessment in the GenAI Era 92 4.5 Redesigning Assessment with GenAI 94 4.5.1 Integrate Multiple Assessment Methods 95 4.5.2 Promote Authentic Assessments in the AI Era 96 4.5.3 Promoting Academic Integrity and Genuineness in the AI Era 96 4.5.4 Embracing AI as a Learning Partner 97 4.5.5 Prioritising Soft Skills in Assessments in the AI Era 98 4.5.6 Prioritising Feedback Over Grades in the AI Era 99 4.6 AI Assessment Integration Framework 100 4.6.1. Performance-Based Assessment 102 4.6.2 Personalised or Contextualised Assessment 104 4.6.3 Human-Centric Competency Assessment 106 4.6.4 Human- Machine Partnership Assessment 107 4.6.5 Project- or Scenario-Based Assessment 109 4.6.6 Time-Sensitive Al-Generated Adaptive Assessment 112 4.6.7 | Meta-cognitive Assessment 113 4.6.8. Ethical and Societal Impact Assessment 115 4.6.9 Lifelong Learning Portfolio Assessment 118 4.7 GenAI Text Detection 120 4.7.1 GenAI Text Detection Approaches 120 4.7.2 GenAI Text Detection Tools 122 4.8 Conclusions 123 Questions to Ponder 124 Personal Reflection 124 References 124

5.1 Introduction 127 5.2 Are There Similar Laws for ChatGPT and GenAI? 127 5.2.1 Transparency, Explainability, and Interpretability 128 5.2.2 Fairness and Bias 128 5.2.3 Accountability 129 5.2.4 Safety and Robustness 130 5.2.5 Privacy and Data Protection 130 5.2.6 Autonomy and Human Oversight 132 5.2.7 AI Alignment for Humanity 132 5.3 AI Policy Around the World 134 5.3.1 China 135 xii Contents

5.3.2 The United States (US) 135 5.3.3. The European Union (EU) 135 5.3.4 United Kingdom (UK) 136 5.3.5 Australia 137 5.3.6 India 137 5.3.7 Japan 138 5.3.8 UNESCO 138 5.3.9 Differentiation and Progressiveness in Global AI Regulation 138 5.3.10 Business Reactions Towards the Regulations 139 5.4 AI Policy in Education 139 5.4.1 UNESCO's Guidance for GenAI in Education and Research 140 5.5 Research Findings from the Perception of Students, Teachers and Staff on GenAI in Education Policy in Hong Kong 146 5.5.1 Methods 146 5.5.2 Quantitative Findings 147 5.5.3 Qualitative Findings 147 5.5.4 The AI Ecological Education Policy Framework 148 5.6 Devising an AI Policy in Higher Education 151 5.6.1 An Example of an AI Policy in Higher Education 157 5.6.2 University of ABC 158 5.7 Conclusions 160 Questions to Ponder 161 Personal Reflection 161 References 161

6.1 Introduction 166 6.2 The History 167 6.2.1 The Genesis Phase (1940s—1980s) 170 6.2.2 The Maturing Phase (1980s to 2010s) 175 6.2.3 The Acceleration Phase (2010s to 2030s) 177 6.3 Creating a Model 179 6.3.1 The Training Data 181 6.3.2 Model Design and Structuring 185 6.3.3 Model Layers and Connections 188 6.3.4 Key Model Capabilities 189 6.3.5 Foundational Model Training 193 6.3.6 Foundational Model Testing 196 6.3.7 The Fine-Tuning 216 6.3.8 The Deployment 217 6.3.9 Model Use (aka Inference) 219 6.4 Models and Ecosystems 221 6.4.1 Custom Interfaces and Chat 221

6.5

6.6

Contents xiii

6.4.2 APIs and Functions 223

6.4.3 Plugins and Agents 223

6.4.4 Custom Fine-Tuning 224

6.4.5 Custom Models 224

6.4.6 Vector Databases and Retrieval Augmented Generation (RAG) 224 State-of-the-Art Models Overview 225

6.5.1 LLM — OpenAI ChatGPT-4.0 225

6.5.2 LLM — Meta LLaMA-2 226

6.5.3 LLM -— Google Bard and PaLM-2 227

6.5.4 LLM -— Anthropic Claude 228

6.5.5 LLM -— Mistral AI Mistral 229

6.5.6 Diffusion Model — stability.ai Stable Diffusion 230 6.5.7 Diffusion Model — OpenAI DALL-E 3 231

6.5.8 Diffusion Model — Midjourney Inc. Midjourney 231 6.5.9 Speech Recognition — OpenAI Whisper 232 Conclusions 233

Questions to Ponder 234 Personal Reflection 234 References 234

7.1 7.2 7.3

7.4

7.5

7.6

Previous Technology Adoption 240

Predictions on General GenAI Implications 241

7.3.1 GenAI Adoption by General Population 241

7.3.2 Job Impact 241

7.3.3. Al Safety Impact — Adoption 243

7.3.4 AI Safety Impact — Post-Adoption 244

7.3.5 Impact from Governments 245

7.3.6 AI Development Companies 246

Predictions on GenAI’s Implications in Higher Education 247 7.4.1 Pedagogy and Assessment in Higher Education 247 7.4.2 Research in Higher Education 249

7.4.3 Drafting Funding Proposals 249

7.4.4 Submitting and Reviewing Journal Articles 250 Can AI or GenAI Predict the Future? 250

7.5.1 What did OpenAI ChatGPT Say? 251

7.5.2 What did Google Bard Say? 252

7.5.3 What did Microsoft Bing Chat Say? 253 Conclusion 253

Figures

Three Types of Artificial Intelligence

ChatGPT Dall.E3 Generated Images

How Does ChatGPT Work?

An image showing RGB pixel

The Text to Image Process

Showing the Diffusion Process

The Image Response Generated from the Prompt “A Serene Lake Surrounded by Autumn Trees”

The Dynamic AI Literacy Model (DAILM) for Specific Roles

A Screenshot of ChatGPT by OpenAI, Note the User-Friendly Dialogue Box, Identical to Many Popular Messenger Applications A Screenshot of Bard by Google, This Version of Bard, also Allows Voice-Input

A Screenshot of ChatGPT by OpenAI, Note the Tips for Getting Started Regarding Sensitive Info and Check Your Facts

The Prompt Engineering Components

Outcomes-Based approach to Student Learning with Reflection and Feedback

AI Assessment Integration Framework

Microsoft Bing’s Chat Feature to Protect Data Privacy

European AI Act Risk Level Diagram

AI Ecological Education Policy Framework

An Example of Attribution of GenAI Generated Content

An Example of Using a Padlet for Online Forum

AI Categorisation

AI and Gaming Timeline

AI Periods and Hardware Advances

The Genesis Phase (1940s to1980s) — Publications and Models The Maturing Phase (1980s to 2010s) — Publications and Models The Acceleration Phase (2010s to 2030s) — Publications and Models Creating a Model — The Big Picture

Data Gathering and Customisation Pipeline

Feed-Forward Cell

Recurrent Cell

Deep Neural Network

Deep Neural Network Variations Tokenisation Example 1 Tokenisation Example 2 Encoding Matrix

The Model Ecosystem

Figures xv

Tables

Examples of Various Sources of Big Data

Characteristics of Big Data

Characteristics of IoT

ChatGPT Generated Codes

Examples of Types of Generative AI Applications and Software Steps Showing How ChatGPT Works

Common Literacy in the Digital World

AI Literacy Framework Foundational for the Typical Individual Using ChatGPT GPT-4, Demonstrating the Humanistic Conversational Capability and also Its Ability to Be in a Role (i.e. a Tutor) Using ChatGPT GPT-4, demonstrating the customisability and style Using ChatGPT GPT-4, demonstrating the multilingual support

and the access to Bing for real-time information

Student/Teacher Category: Weaknesses and Potential Threats using GenAI in Higher Education

Educational Institutions Category: Weaknesses and Potential Threats using GenAI in Higher Education

Society Category: Weaknesses and Potential Threats using GenAI

in Higher Education

Summary on the Biases in Training Data for GenAI

A Complete List of the Strengths and Weaknesses of GenAI in Higher Education

Bloom’s Taxonomy Cognitive Domain Model

Rubrics for Assessing ChatGPT GPT-4 Literary Capability at the “Remembering” Level using Bloom’s Taxonomy

Prompt used for the study of ChatGPT GPT-4’s Literary Capability against Bloom’s Taxonomy

The Evaluation of ChatGPT’s Literary Capability for Oliver Twist via Bloom’s Taxonomy

A List of AI-Human Partnered Pedagogies Separated into Teaching, Learning, Research and Administrative Categories

A Simple Outline of an AI Education Policy

OpenAI GPT-n Series Release History

Meta LLaMA Release History

Google PaLM/Bard/LaMDA Release History

Anthropic Claude Release History

Mistral AI Release History

Stable Diffusion Release History OpenAI DALL-E Release History Midjourney Release History OpenAI Whisper Release History

Tables xvii

229 230 231 232 232

Author Biographies

Cecilia Ka Yuk Chan is a Professor in the Faculty of Education and the Founding Director for the Teaching and Learning Innovation Centre at the University of Hong Kong (HKU). With a dual-discipline background, she possesses expertise in both engineering and education, particularly higher education. Her combined expertise in these fields has enabled her to lead and conduct research on topics such as assessment and feedback, experiential learning, engineering education and technology-enhanced learning, with a particular focus on Artificial Intelligence in education and the development and assessment of 21st-century skills (holistic competencies). Prof. Chan serves as the President of the Asian Society for Engineering Education and is an associate editor for both the Journal of Engineering Education and Studies in Educational Evaluation. Her work, adopted by software companies and organisation and utilised globally including UNESCO, has garnered industry donations and funding. She presents and works closely with organisations worldwide including UNESCO, QS Summit, Harvard, Oxford, UCL, Sydney University to promote the development of holistic competencies and AI Literacy. More information can be found in the Teaching and Learning Enhancement and Research Group (TLERG) website: http://tlerg.cetl_hku.hk/

Tom Colloton is the founder of Eireann Technology, an Educational Technology (EdTech) research and development company. The company is dedicated to addressing major challenges in education by exploring the effective application of technology. With over twenty-five years in the technology sector, Tom has held diverse roles, including Director, Enterprise Architect, and Senior Software Engineer, spanning domains such as Finance, Healthcare, Telecommunications, and Education.

Tom is deeply committed to harnessing technology to impact the educational landscape, specifically in areas like student engagement, retention, programme design, and authentic assessment. He is designing cutting-edge visualisation technology and Artificial Intelligence systems to enhance the experience for teachers, students, and administrators.

Prologue

Thinking back, it seems almost mad that we even attempted to write a book in just eight months — especially during the last eight months I have been very busy with new responsibilities, having taken up the directorship of the newly structured teaching and learning centre at my university. On top of that, the ever-evolving landscape of Generative AI (GenAI)—with its constant introduction of new apps, regulations, functions, and guidelines — kept setting us backwards from our finishing line, forcing us to keep up with the latest developments. I remember my co-author Tom telling me, “We need to revise the chapter again! Did you hear that ChatGPT can now recognise images?” As much as I wanted to exclaim, “Wow, cool!” my actual response was, “Oh, no!” Despite that, our commitment remained: we wanted to bring the latest ideas, news, and practices to our readers, and we felt compelled to stay ahead of the curve. Thus, you will find very up-to-date news and references in the book. The content in some chapters is so cutting-edge that our primary sources were online forums, substack, LinkedIn, Facebook teachers’ groups, Twitter (X), newspapers, and YouTube; we must have joined so many Facebook, LinkedIn forums and discussion groups, and, naturally, engaged in extensive discussions with colleagues from universities and industries worldwide.

Being at the top-ranking university in Asia and the world, we often hold the centre stage. Given that the University of Hong Kong is the first university to have an AI policy and assessment guidelines in place (as far as we know), it was expected that news reporters would be interested in our work. Apart from the book, I also conducted a number of research projects in this area, and put together an AI literacy online course an assessment guidebook and infographics and leaflets on GenAI assessment (please visit the AI in education website at https://aied.talic.hku.hk/). Looking around and speaking to the publisher, we believe this book will be the first on GenAI in higher education.

While this book is primarily for educators, I felt it would’ be incomplete without including the technical side of GenAI. Therefore, I invited Tom as my co-author. I am grateful to him, joining me in writing this book to check up on my AI know-how, making sure the tech parts are sound, given his current expertise in this domain. I must admit my technical background is a bit out-of-date.

As educators, we are always talking about an interdisciplinary focus, many bachelor programmes are no longer just a degree majoring in one discipline area but many. This book is interdisciplinary by nature; together, my co-author and I encompass both the educational and technical aspects. By merging education and engineering, we reflect the multi-faceted world we inhabit. Moreover, GenAI fosters collaboration across disciplines. Hence, xx Prologue this book contains examples from all disciplines, assisting readers in grasping the expansive world of GenAI and interdisciplinarity.

In the book, we tried to use as many examples as we can to illustrate the different aspects of the power of GenAI. Knowing the readers of this book may be teachers, educational developers, policy writers from various disciplinary backgrounds and different types of post-secondary education institutions (such as community, vocational and technical colleges, research-intensive, and teaching-intensive universities), we made it a point to use straightforward language and terms that are not specific to educational research, and we hope that we have managed to make it readable and approachable for all readers.

GenAT’s role in education is so new for the majority, we want to help readers to try out this new tool, not to be afraid of it, but to embrace it. Thus, wherever possible, we include examples, and even provide ‘try-it-out’ prompts so readers can test them out. Any changes are not easy for our teachers, staff and policymakers; this book also raises the challenges not to underestimate the workload and hurdles through which teachers and supporting staff must jump. GenAI is driving us to revolutionise assessment in education. Recognising this transformative potential, in Chapter 4, we have developed an AI Assessment Integration Framework, together with an array of strategies and scenarios to effectively partner with GenAI in this domain. Since May, when a research article I authored on AI policy was published, I have been contacted by over 200 universities to discuss about how to devise an AI policy. Thus, in Chapter 5, we provide a step-by-step guide on how to devise an AI policy; in fact, we gave an example of what an AI policy should look like. Of course, if you wish to know more, please do contact us.

The examples, case studies, and focus of the book are written for post-secondary education; however, most of the content is suitable for different levels of education. School teachers and principals can adapt some of the strategies to suit the culture of their students and environments.

Each chapter begins with a quote that relates to the theme of the chapter, and the structure of the main body may differ depending on the theme of each chapter. The chapter ends with a section called questions to ponder and we included our own personal reflection after completing each chapter which diverges from the conventions of a typical academic book. This book is written for everyone — academic developers, researchers, teachers, policymakers, students, IT and administrative staff, and parents. There are lots of useful “freshly out of the oven” research, guidelines, examples, frameworks and practices from this book and it may help to contemplate the kind of findings that are needed to support these practices and to generate theories and frameworks. The questions and reflections at the end of each chapter may also inspire research questions. At the end of the preliminary pages is a short summary of each chapter.

Acknowledgments

I would like to express my deepest gratitude to my husband. He was the first to get me into the world of GenAJI, and he jumped on it even before ChatGPT was on everyone’s radar. He excitedly got the whole family involved, his enthusiasm was infectious; his tireless efforts in setting up accounts for our family including our kids. My eleven-year-old, Aisling, even christened our ChatGPT with the name “James”. She felt he was more than just code; almost like another member of the family. She taught us to always say “please” and “thank you” to James — seeing her do this, I think, ‘“Well, at least she’s picking up good manners!’” Every little interaction becomes its own story.

My eldest daughter, Grace, has harnessed the potential of ChatGPT commendably, utilising it for feedback and crafting French comprehension exercises with Q&As to selfassess her grasp on various topics. My son Sean’s passion for engineering serves as yet another driving force, inspiring me to advance AI in education. Observing my family’s interactions with AI reinforced my urge to stay ahead, understanding its implications in the education sector.

Big thanks to Dr. Katherine Lee and Dr. Hannah Wong, my dedicated postdocs. You two are stars! Katherine and Hannah deserve special mention for their meticulous proofreading of this book in an impressively short time span. I have also drawn immense inspiration from my students, Carol Hu, Louisa Tsi, and Wenxin Zhou. Your keen interest in AI has been a continuous source of inspiration, motivating me to push my boundaries. A heartfelt thank you to Dr. SiawWee Chen, who has been involved in assisting me with my literature research. I am genuinely grateful for all your hard work and support.

I would like to thank Prof. Rui Yang, the Dean of Education, for his genuine support and encouragement in my research. I am grateful to have a visionary leader to learn from.

I would like to express my profound gratitude to Prof. Ian Holliday for his trust in me to spearhead the support on GenAI at the university.

Special thanks go to Mr. Pashur Au Yeung, Dr. Carson Hung, Dr. Leon Lei, Ms. Flora Ng, Dr. Wilson Kwok, Mr. Cyrus Chan, Mr. Alan Poon, Mr. Alex Lee and Mr. Noel Liu for engaging in stimulating discussions and exchanges of ideas. A heartfelt thanks to William, who adeptly managed much of my administrative workload, allowing me to focus and put some time aside on my writing.

Iam deeply appreciative of the entire team at TALIC, the Teaching and Learning Innovation Centre, at the University of Hong Kong, for their support throughout this endeavour.

To everyone at the University of Hong Kong who has been to my AI seminars, workshops, and clinics: your energy and interest made all the difference. You gave me the push I needed to get this done. A big virtual hug to my colleagues at the Faculty of Education for your support. Your constant backing means the world.

xxii Acknowledgments

And, of course, a massive thanks to the crew from Routledge. Katie Peace and Khin Thazin — you guys navigated me through the maze of publishing. Katie recognised the unique position of this work, as this book is likely the first book on GenAI in higher education. Her enthusiasm and belief in the project’s significance have been instrumental in bringing this vision to fruition. I can’t tell you how much I appreciate it.

Lastly, I want to express my gratitude to the wider community on LinkedIn, Facebook (Meta), YouTube, news, journal articles, Twitter (X), and other platforms. I have learned immensely from your contributions and insights, enriching my understanding of this dynamic field. Thank you all for your invaluable contributions to this book and my ongoing exploration of GenAI in higher education.

Cecilia Ka Yuk Chan (28 October 2023)

Firstly, I would like to thank my inspiration, my truly remarkable wife. It is unbelievable how someone can be such a wonderful wife, mother, and daughter. She will always be an inspiration to me and those around her. I would not have embarked on or completed this endeavour without her.

I would also like to extend my gratitude to my children. They infuse life with joy and intrigue, constantly surprising and occasionally shocking me, though thankfully not too often. A special mention to their grandparents, Teresa — 22 and Wing — 282, who are always on hand to assist and drop over some delicious Char Siu (X $8).

Lastly, heartfelt thanks to my parents, Mayler and Loretto. I owe my very existence to them, and I am grateful for the education they provided and for nurturing my inquisitive and curious nature.

Tom Colloton (28 October 2023)

Short Summary of Each Chapter

Chapter 1: Introduction to Artificial Intelligence in Higher Education

Opening with an introductory backdrop on Artificial Intelligence (AI), Big Data, the Internet of Things (IoT) and Generative AI (GenAT), this chapter offers readers foundational knowledge of these groundbreaking technologies and their specific applications within the educational settings. The spotlight is cast on ChatGPT by OpenAJ, highlighting its potential as a revolutionary tool in higher education. A detailed breakdown of AI, from its inception in the 1950s to its current categorisations — Artificial Narrow Intelligence (AND), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI) — is provided. The chapter then delves into the significance of Big Data, IoT, and GenAI and their intrinsic characteristics, with a focused example on their implications for higher education. The chapter connects technological advancements with their practical implications in education, urging readers to consider the potential and pitfalls of integrating AI in the classroom.

Chapter 2: AI Literacy

Chapter 2 of the book emphasises the crucial nature of AI literacy in today’s technologically-driven society and provides a perceptive definition based on literature and research findings. It underscores the necessity of possessing foundational AI literacy, which grants individuals the essential understanding to navigate the modern digital landscape, regardless of their profession. However, the chapter posits that AI literacy cannot remain broad-brushed; it needs further granularity based on professional contexts. For instance, while healthcare professionals may grapple with life-threatening AI mistakes, business professionals might harness AI to shape brand perceptions. Consequently, the Dynamic AI Literacy Model (DAILM) for Specific Roles is introduced, proposing a layered approach to AI literacy tailored to specific professions. Additionally, the chapter sheds light on AI literacy’s importance in the broader digital literacy spectrum, with a special focus on AI literacy for teachers, and also evaluates various AI literacy frameworks from previous research.

Chapter 3: Strengths and Weaknesses in Embracing ChatGPT in Curriculum Design

This chapter provides an in-depth exploration of ChatGPT and GenAI’s implications in higher education curriculum design. The chapter underscores the strengths, potential opportunities, weaknesses, and threats posed by GenAI in higher education settings. Drawing from a wealth of resources, including literature reviews, online discussions, and xxiv Short Summary of Each Chapter firsthand testimonials from students and educators, the chapter offers a list of practical examples of how ChatGPT can be woven into pedagogical design. Serving as a roadmap, the list encourages stakeholders to merge AI capabilities with human ingenuity to foster innovative teaching methods. In addition, the chapter demonstrates a research study examining the synergy between ChatGPT and Bloom’s Taxonomy to present a holistic view of Al-partnered pedagogy. The chapter concludes with insights into prompt engineering and its components, offering guidance on optimising interactions with GenAI.

Chapter 4: Redesigning Assessment in the AI Era

Chapter 4 delves into the transformation of assessment methodologies in higher education, driven by advancements in GenAI. Tracing the evolution of assessment from its traditional roots, the chapter underscores the challenges and innovations brought forth in both pre- GenAI and GenAI contexts. As GenAI offers dynamic, adaptive assessment and feedback mechanisms, the chapter emphasises the paramount importance of maintaining educational principles such as authenticity, integrity, and genuine learning. Introducing the Six Assessment Redesign Pivotal Strategies (SARPS) and the AI Assessment Integration Framework, the chapter highlights nine distinct assessment types tailored for the GenAI era. However, a critical message conveyed is that GenAI should be “co-”, not replace, the human touch in education. The chapter concludes by advocating for a holistic, learner-centric approach to assessment, prioritising feedback and genuine learning experiences over mere grades.

Chapter 5: Developing an AI in Education Policy

Chapter 5 delves into formulating policies for Artificial Intelligence (AI) in education, emphasising the importance of understanding regulatory principles like transparency, fairness, accountability, safety, and privacy across platforms such as ChatGPT and GenAI. The chapter explores the AI Ecological Education Policy Framework by Chan (2023), structured into three main dimensions — Governance, Operational, and Pedagogical. The Governance Dimension focuses on ethical governance, while the Operational Dimension examines practical implementation in university settings. The Pedagogical Dimension optimises teaching and learning with AI integration. Qualitative data enriches the chapter, offering a comprehensive perspective for shaping a holistic AI policy in higher education. The chapter also provides a review of AI policies from various global regions, and concludes by offering a step-by-step blueprint for establishing an AI policy within higher educational institutions.

Chapter 6: Technology Behind GenAI

This chapter offers an in-depth exploration of Generative AI (GenAJ) technology within the context of higher education. While the primary focus of this book is on higher education and its implications, we recognise that some readers may have a curiosity about the technical intricacies of GenAI. Therefore, the aim of this chapter is to delve into the technical aspects of GenAI. Beginning with a historical overview of artificial neural networks, it traces their evolution from early developments to recent advances.

Short Summary of Each Chapter xxv

The chapter unveils the process of creating a GenAI model, covering data gathering, model design, training, and rigorous testing. It also introduces the expansive GenAI ecosystem, highlighting its various applications and interactions within different environments.

Furthermore, the chapter spotlights state-of-the-art GenAI models, discussing their applications, design principles, strengths, and limitations. This chapter serves as a foundational knowledge base, equipping readers with essential terminology and understanding of GenAI’s technical aspects.