Microsoft Copilot summarises the chapters

Here follows summaries of the various chapters of this blog book, as generated by Microsoft Copilot. They are presented in the form in which they appeared through the «Create a summary» option in the Copilot window titled «Explore this page.»

Prologue

This page, part of the blog «Didaktiske betraktninger,» serves as a prologue to a larger discussion on artificial intelligence (AI) and chatbots. Here are the key takeaways:

AI Criticism and Skepticism: The page includes quotes from various academics and thinkers who challenge the hype surrounding AI, especially large language models (LLMs) like ChatGPT. Some argue that AI lacks true intelligence since it does not engage in logical reasoning or understand truth and falsehood.
Origins of the Blogbook: The author started with an initial blog post in January 2023, testing ChatGPT, and subsequently expanded the discussion based on further testing through 2024 and early 2025.
Chatbot Testing: Extensive evaluations were conducted on a variety of chatbots, including ChatGPT, Bing Chat (now called Microsoft Copilot), Claude, Gemini Pro, and others. The tests focused on whether these tools could produce academically rigorous responses and fact-based essays.
Concerns About AI Perception: The author expresses skepticism toward the term «artificial intelligence,» arguing that it creates unrealistic expectations among the general public. Instead, the term «artificial actors» is proposed as a more accurate descriptor.
Impact on Education and Research: The page raises concerns about how AI is perceived in academia, questioning whether chatbots can meet higher-order learning requirements in subjects like social informatics.
Ethical and Practical Challenges: Issues surrounding AI ethics, security, and misinformation are touched upon, with warnings about how inflated expectations could lead to an eventual «AI winter»—a period of disillusionment and reduced investment in AI.

This prologue sets the stage for deeper discussions on AI’s actual capabilities versus public perception.

Chapter 1

This page discusses the nature and limitations of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Microsoft Copilot. The key points include:

AI, especially LLMs, is often misunderstood and overhyped. While they can mimic human language, they do not possess true intelligence or reasoning abilities.
The term «artificial intelligence» can be misleading, as current AI systems rely on statistical calculations rather than actual cognitive processes.
These models operate based on predefined algorithms and patterns, rather than independent thought or understanding.
AI chatbots cannot analyze the validity of their own outputs, leading to potential inaccuracies and hallucinations.
AI tools can generate responses based on language models, but they lack true comprehension or intent.
The perception of AI as a powerful, autonomous system is exaggerated, and it should instead be understood as a highly sophisticated predictive text system.
Chatbots is often framed as an «experts system,» but it does not possess the structured knowledge representation traditionally associated with such systems.
The text argues that LLM-based chatbots should simply be referred to as «conversation robots» rather than true AI.

Chapter 2

This blog post discusses the misconceptions surrounding artificial intelligence (AI), particularly large language models like ChatGPT. The key points include:

AI is not a singular technology: Different tools like facial recognition and translation software use machine learning but should not be grouped under one term.
ChatGPT does not understand input: It generates responses based on statistical patterns rather than true comprehension.
Lack of real intelligence: Language models do not exhibit independent reasoning or problem-solving beyond their training data.
General AI is far off: Some experts argue that we are centuries away from achieving true artificial intelligence.
ChatGPT’s limitations: The system can mimic human-like responses but has significant weaknesses that make it unsuitable for serious AI applications.
Comparison with Microsoft Copilot: The blog notes that even Microsoft Copilot acknowledges its limitations in adaptability and autonomous decision-making.

Overall, the page presents a skeptical view of AI’s current capabilities, particularly in educational and professional settings.

Chapter 3

This page discusses the reliability and usefulness of large language models (LLMs), particularly ChatGPT, in academic and practical settings. Here are the main points:

Criticism of LLMs: Various experts argue that LLMs like ChatGPT are sophisticated pattern recognizers but lack true intelligence or understanding. They cannot provide reliable factual information consistently.
Testing ChatGPT: The author conducted several tests across different subjects, including information security and digital preparedness. The results showed that ChatGPT often produced incorrect or unreliable responses.
Limitations in Academic Use: ChatGPT struggles with complex university assignments based on Bloom’s Taxonomy’s highest level, as it cannot generate truly creative or insightful responses.
LLMs as «Bullshit Generators»: While ChatGPT can be useful, it often provides misleading or incorrect information. When questioned, it automatically apologizes but does not truly «learn» or verify its own mistakes.
Future Potential: The text questions whether AI tools like ChatGPT will ever become genuinely intelligent assistants capable of improving everyday life.

Overall, the page emphasizes skepticism about LLMs’ reliability, arguing that they should not be trusted for factual accuracy or deep understanding.

Chapter 4

This page discusses the nature of artificial intelligence, particularly focusing on large language models (LLMs) like ChatGPT. Here are the key points:

AI as a pattern finder: AI models don’t possess true intelligence but merely identify and generate patterns in data.
Criticism of «artificial intelligence»: Some experts prefer not to call LLMs “artificial intelligence,” as their function is statistical rather than genuinely intelligent.
Generative AI mechanisms: These models predict the most likely next word based on previous words, using probability and randomness.
Dependency on human input: LLMs do not evolve autonomously; they rely on human labor for improvement.
Comparison to ELIZA: AI chatbots function similarly to older psychological programs, mirroring user inputs to simulate understanding.
Challenges with accuracy: LLM-generated responses depend on the reliability of their training data, making them vulnerable to misinformation.
Limitations in memory: Conversations are not consistently remembered across sessions, reducing continuity in AI interactions.
Potential for misinformation: AI can be used to spread false information if it stores and recalls user-provided data unchecked.
Lack of genuine reflection: AI does not “understand” concepts but instead constructs responses based on structured data patterns.

The page presents a critical perspective on AI’s capabilities, highlighting its limitations and potential risks.

Chapter 5

This page discusses the limitations of AI-generated text responses, particularly in higher-order thinking tasks. It critiques ChatGPT and other similar systems, arguing that they produce answers based on statistical probabilities rather than genuine understanding. Some key points include:

AI chatbots often fail in complex reasoning tasks: While they can generate well-structured responses, they struggle with deeper analysis and logical consistency.
Hallucinations and inaccuracies: AI chatbots sometimes fabricate information, including false references, books, and research papers.
ChatGPT’s performance in exams: Reports claiming ChatGPT passed graduate-level exams are misleading; its answers are often superficial.
Concerns about AI accuracy: Developers and researchers frequently downplay the issue of AI hallucinations, despite their implications for fields like law and medicine.
Students must critically assess AI-generated content: AI can be useful for brainstorming but is unreliable as a factual source.

The page also touches on AI models like Bing Chat and Jenni, evaluating their effectiveness in academic and professional tasks. It argues that AI-driven responses are often entertaining but not necessarily reliable for serious work.

Chapter 6

The blog post discusses the limitations of generative AI in handling complex academic tasks. It highlights key expert opinions on AI’s capabilities, including thoughts from Jason M. Lodge, Melanie Mitchell, and Noam Chomsky, among others. The main points include:

AI’s limitations in reasoning: Experts argue that AI models rely on pattern recognition rather than true understanding or reasoning.
Evaluation of chatbots in academic tasks: The study tested five AI tools—including different versions of ChatGPT and Bing Chat—on a complex academic assignment about digital preparedness.
Results and conclusions: All AI performed poorly, failing to demonstrate meaningful progress in solving higher-order thinking tasks.
Criticism of AI reliance in education: The post questions whether AI tools genuinely support learning or simply mimic information retrieval.

Chapter 7

This blog post discusses the role of AI-powered chatbots like ChatGPT in education and explores whether they should be banned or integrated into learning environments. The key points include:

Banning is ineffective: The author argues that prohibiting tools like ChatGPT is unrealistic and counterproductive, likening it to banning Wikipedia or spell-checkers.
Integrity and trust matter: Instead of enforcement, the focus should be on fostering integrity, ethical use, and reducing incentives to cheat.
Limitations of detection tools: AI-detection systems, such as GPT-2 Output Detector Demo and GPTZero, are unreliable, especially when texts are translated.
Understanding AI is crucial: Education should emphasize understanding AI’s capabilities and limitations rather than dismissing it outright.
Integration in teaching: Rather than banning AI chatbots, educators should explore how to include them in coursework to help students develop informed and responsible usage.

Chapter 8

This chapter discusses the ethical, security, legal, and educational challenges posed by conversational AI like ChatGPT. The key concerns include:

Security Risks: AI can be exploited by cybercriminals for phishing attacks, malware creation, and misinformation campaigns. It may also be used by extremist groups and state actors to destabilize democracies.
Ethical Concerns: Bias in training data, lack of transparency in AI decision-making, and unethical labor practices are highlighted. AI is also criticized for environmental impact, including high water usage for server cooling.
Legal Issues: AI-generated content raises copyright concerns, as companies like OpenAI have used protected material for training without explicit permission. Privacy risks exist as AI tools may store and analyze user data.
Educational Challenges: Over-reliance on AI can weaken students’ critical thinking, creativity, and problem-solving skills. It may also widen the gap between strong and weak learners, as effective AI use requires prior knowledge.

The author suggests that AI tools should be carefully scrutinized and integrated into teaching with awareness of these risks, particularly in information security and digital literacy courses.

Chapter 9

This page discusses teaching and assignment design in higher education, emphasizing the importance of creating rich assignments that foster critical thinking, reflection, and creativity. Key points include:

Effective Assignment Design: Assignments should encourage deeper understanding and prevent students from relying solely on AI-generated responses.
Higher Order Thinking: University assessments should focus on critical thinking and genuine individual reflection, as AI struggles with complex reasoning.
Challenges with AI: While AI models can generate convincing text, they lack analytical depth and personal reflection.
Academic Integrity: Encouraging formative assessment and draft submissions helps detect plagiarism and enhances student learning.
Research on AI in Exams: Studies show that AI-generated responses can go undetected, raising concerns about assessment methods.
Student Engagement: Metacognitive learning strategies and active student participation lead to meaningful learning experiences.
Alternative Assessment Methods: Portfolio assessments and guided feedback can mitigate AI’s impact on academic integrity.
Learning vs. Cheating Debate: Instead of focusing on catching students cheating, educators should design assessments that reduce incentives for dishonesty.

The conclusion reinforces the idea that fostering intellectual engagement through well-designed assignments is crucial.

Chapter 10

This page presents a critical discussion on the capabilities and limitations of AI-based language models like ChatGPT and Microsoft Copilot. Here are the key takeaways:

Nature of AI models: The text emphasizes that ChatGPT and similar models are not true knowledge resources but rather statistical models performing language recognition.
Limitations in reasoning: These models do not analyze or critically assess the content they generate, often presenting incorrect or misleading information as factual.
Challenges in learning and reflection: AI chatbots cannot engage in meaningful reflection over their own learning, nor do they adapt their reasoning autonomously.
Concerns about reliability: Tools designed to detect AI-generated text are not always reliable, leading to questions about their usefulness in academic and educational settings.
Debate about AI classification: The discussion argues that ChatGPT and similar tools do not meet the criteria for artificial intelligence and instead function as sophisticated pattern-recognition systems.
Media hype and skepticism: It critiques exaggerated claims about AI’s capabilities, pointing out that its real-world applications have not lived up to expectations.
Educational implications: The text discusses AI’s role in education, arguing that while AI tools have certain uses, they are not fundamentally transformative learning resources.

Epilogue

The page is discussing the limitations of AI chatbots, particularly in academic contexts. Here are the key points:

Historical Perspectives on AI & Thought: The author includes quotes from various thinkers about the nature of thinking, reading, and AI’s impact on human cognition.
Testing AI Chatbots: The author conducted extensive testing (2022–2025) to assess whether AI chatbots like ChatGPT could produce high-quality academic responses.
Findings on AI’s Academic Performance: The results indicate that chatbots struggle with tasks requiring deep reasoning, critical analysis, and synthesis—essential for higher-level academic work.
General Limitations of AI Models: AI chatbots primarily generate text probabilistically rather than through genuine understanding or reasoning, making them unreliable for complex academic tasks.
Impact on Education & Society: AI chatbots can assist users in structuring responses and summarizing information, but they do not pose a significant threat to traditional academic evaluation methods.
Reflections on AI’s Future: The author argues that AI is far from achieving true intelligence or general artificial intelligence (AGI), instead acting as sophisticated pattern-matching tools.
Concerns About AI Adoption: There is concern that people anthropomorphize AI, leading to misplaced trust and unrealistic expectations of its capabilities.

The page ultimately emphasizes skepticism toward AI’s ability to replace human intellectual processes, particularly in academia.