Indexes

#11.1Figures

• Figuur 3.1 SBE’s depiction of the negative cycle of generative AI, also known as the “AI dependency trap.”
• Figure 5.1 Depiction of the field of artificial intelligence and generative AI according to AI for Education (AI for Education, 2023)
• Figure 5.2 Visualization of how a neural network works to detect characteristic parts in a figure (source: Wikimedia/Cyberbotics Ltd.).
• Figure 5.3 Visualizations of neural networks and training based on the video “Generative AI in a nutshell” by Henrik Kniberg (2024).
• Figure 5.4 Images used to show the diffusion process for generating and recognizing images from Yang et al. (2024).
• Figure 6.1 Graphic representation of how users interact with an LLM through an intermediate product. The user sees an interface (UI) in a product, and that product communicates with the model and back again via Application Programming Interface standards (API).
• Figure 6.2 Example of conversational memory versus no conversational memory (Pinecone, 2025)
• Figure 6.3 Illustration of a simple Agentic AI system. Prompts go back and forth between language models; each time the model responds to the input from the other model and sends it back again (until the decision is made to show the final result to the user. Image Tiwari, R.K. (2025).
• Figure 7.1 Image of a typical data center: a very large enclosed building with many computers inside it (image source https://www.northcdatacenters.com/ ).
• Figure 7.2 Interior of the xAI data center Colossus in Memphis, Tennessee. Colossus contains about 100,000 Nvidia Graphics Processing Units (GPUs) to train language models. Expansion to 200,000 GPUs is already being planned (image source: https://www.datacenterfrontier.com/machine-learning/article/55244139/the-colossus-ai-supercomputer-elon-musks-drive-toward-data-center-ai-technology-domination).
• Figure 7.3 Comparison of the amount of energy chatbots consume compared with all other AI services (bron grafiek: Masley, 2025a).
• Figure 7.4 Comparison of energy use with other forms of everyday energy consumption (bron grafiek: You, 2025).
• Figure 7.5 Graph showing which lifestyle change results in a certain reduction in CO2 emissions (in tons) (bron grafiek: You, 2025).
• Figure 7.6 Comparison of water use by various online activities (bron grafiek: Masley, 2025a).
• Figure 7.7 Comparison 2 of water use by various functions (bron grafiek: Masley, 2025a).
• Figure 7.8 Podcast from Trouw, June 25, 2025.
• Figure 9.1 The top 20 sources in the mC4 dataset as shown on the website of De Groene Amsterdammer (Hofman & Veerbeek, 2023).

#11.2Original figure URLs

Figure-4.2: https://images.squarespace-cdn.com/content/v1/64398599b0c21f1705fb8fb3/4e25a8bd-e1c5-4f01-8ffd-13665eef7423/Defining+Generative+AI+Explainer+%281%29.png?format=1000w

Figure-4.3: https://upload.wikimedia.org/wikipedia/commons/2/28/Artificial_neural_network_image_recognition.png

Figure-4.4: https://canvas.vu.nl/courses/83333/files/9170751/

https://canvas.vu.nl/courses/83333/files/9170751/download?download_frd=1

Figure-4.5: https://edudatabase.ctl-vu.nl/wp-content/uploads/2025/09/Figuur-4.5.png

Figure-5.3: https://www.pinecone.io/_next/image/?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fvr8gru94%2Fproduction%2F927ca8cc5d92ee75f36d7eb4bef4685c4e3118e5-2880x1370.png&w=3840&q=75

Figure-6.3: https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a6b6ff7-950c-40f7-8ce3-389938dd38e3_1284x378.png

Figure-6.5:
https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff586df9b-e5a1-4ddb-81bb-ca0eb5d99efa_2518x1544.png

Figure-6.6:
https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F21550978-efd9-4aca-bfd8-55be56807d85_2506x1260.png

Figure 3.1 URL for Canvas display: https://edudatabase.ctl-vu.nl/wp-content/uploads/2025/09/Figuur-3.1.png

Figure 4.5 URL for Canvas display: https://edudatabase.ctl-vu.nl/wp-content/uploads/2025/09/Figuur-4.5.png

#11.3Boxes

• Box 1.1 What do we mean by ‘GPT’?
• Box 1.2 If AI sounds convincing, that does not mean it is correct
• Box 1.3 Techno-optimists, techno-realists, techno-activists, and techno-critics
• Box 1.4 Ethics: academic integrity
• Box 1.5 Political influence through generative AI
• Box 1.6 Accountability for AI use in this companion
• Box 2.1 What is a prompt?
• Box 2.2 Large Language Models and multimodal models
• Box 2.3 Prompt engineering: a new academic skill
• Box 2.4 Know your tools
• Box 3.1 The illusion of understanding
• Box 3.2 When does AI support help with learning?
• Box 3.3 Ethics: ownership of your own learning process
• Box 3.4 Using Gemini Notebook to study
• Box 3.5 Practical tips for responsible AI use
• Box 4.1 A prompt for learning how to prompt
• Box 4.2 Practical prompt examples
• Box 4.3 Too much sycophancy
• Box 5.1 Animation about the technology behind generative AI
• Box 5.2 Algorithms
• Box 5.3 AI, statistics, and Big Data: differences and similarities
• Box 5.4 An audiovisual explanation of neural networks and training
• Box 5.5 What does the work of a ‘data labeler’ look like?
• Box 5.6 Data is never neutral
• Box 5.7 Where does machine learning go wrong?
• Box 5.8 Techniques to combat hallucinations
• Box 5.9 Why AI believes it is right
• Box 6.1 LLM Models
• Box 6.2 Context window: when does the chat history break down
• Box 6.3 Uploading files and data security
• Box 8.1 The digital divide in society
• Box 8.2 Ethics in the future of AI
• Box 9.1 ‘Enshittification of the internet’
• Box 9.2 When AI conversations become (too) real: from chatbot to life partner
• Box 9.3 The burned-down Vondel Church
• Box 9.4 Cambridge Analytica scandal
• Box 9.5 Why social media companies do not use AI to check users’ information for accuracy or logical reasoning errors
• Box 9.6 Personal, special-category, and sensitive data
• Box 9.7 The scale of AI investments
• Box 9.8 The AI bubble
• Box 10.1 How do you develop an AI-literate profile?
• Box 10.2 Ethical reflection in the AI era

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#11.5Viewing tips

#11.6Tables

• Table 1.1 The DigiComp conceptual reference model (European Commission, 2025)
• Table 4.1 Overview of AI tools
• Table 5.1 Overview of machine learning methods.
• Table 6.1 Developers of LLMs, their service, models, and a broad indication of compliance with Privacy and Security requirements based on the knowledge of the AI Competence Network of VU Amsterdam.
• Table 7.1 A comparison between the energy required for 100 generative AI prompts per day and other everyday energy use.