Using Copilot to summarize academic information.

IA

Summary of Generative AI, LLMs, and Copilot

Summary

Artificial Intelligence is presented in the document as the science of making machines think and act in ways that resemble human behavior. It is described as an “eternal learner”: a system that observes millions of examples until it can imitate patterns, make predictions, or generate new responses. Although AI has existed for decades in tools such as GPS navigation and email spam filters, it has become much more visible with the rise of generative systems that create text, images, music, and other forms of content.

The document explains several types of AI through everyday examples. Generative AI creates new content and is represented by tools such as ChatGPT, Gemini, and Midjourney. Recognition AI identifies objects, faces, and places, as in facial unlocking systems or augmented reality games. Recommendation AI filters information according to a user’s preferences, while predictive AI anticipates results based on past data, such as weather forecasts or bank fraud detection. Together, these examples show that AI is not a single tool, but a broad set of technologies used to observe, classify, predict, and create.

Current uses of AI are illustrated through cases that show how people often help train these systems without noticing it. In Pokémon GO, for example, players’ camera movements and location-based interactions help artificial intelligence understand real-world spaces, objects, depth, and surfaces. CAPTCHA systems are also described as a kind of classroom where humans teach AI to recognize traffic lights, crosswalks, and road signs. Social media algorithms are another example: they learn from small user signals, such as watching a video until the end, skipping it quickly, or giving it a like. These algorithms build mathematical profiles of user interests and prioritize content that keeps people engaged, even if that content is not necessarily the most truthful or educational.

Large Language Models, or LLMs, are introduced as the technology behind tools such as ChatGPT and similar assistants. Their main task is to predict the next word in a sequence. Although this sounds simple, it allows them to produce coherent sentences, paragraphs, explanations, and complete texts. The document explains that LLMs are first trained with huge amounts of text through pretraining and then improved through feedback from humans or other AI systems, a process called refinement. It also identifies the transformer as the internal structure that allows these models to process language efficiently. The term GPT is explained as Generative Pretrained Transformer.

A central idea in the document is that an LLM should not be confused with a search engine or a source of truth. It does not simply consult a database of verified facts. Instead, it generates probable language by predicting what words are likely to fit together. Because its priority is fluency and coherence, not truth, it may produce answers that sound convincing but are false. These errors are called hallucinations. They can include invented dates, books, laws, authors, or citations that appear real. For that reason, the document emphasizes that AI-generated content must always be checked carefully.

The role of the teacher or professional user is presented as essential. The AI is compared to a copilot that can generate drafts, ideas, and initial text, while the human remains the captain who validates, corrects, and decides what is useful. The document highlights the difference between a novice, who may not detect mistakes, and a trained professional, who can identify historical, pedagogical, or conceptual problems because of their knowledge and experience. This reinforces the idea that information is not the same as knowledge: AI can provide data and text, but human judgment supplies understanding, ethics, and context.

The document also explains basic terms related to how AI works. A data center is the physical infrastructure where AI systems run, described as a building full of powerful computers that give the AI the capacity to respond. A model is the trained digital brain, such as GPT-4 or Gemini. The interface is the screen through which the user communicates with the system, and the text box is where instructions are entered. A prompt is the question or instruction given to the AI, and the quality of the prompt strongly affects the quality of the answer. Tokens are the small pieces of text that AI systems process, similar to building blocks rather than full words or letters.

Another important concept is the context window, which works like the AI’s short-term memory during a conversation. It determines how much previous text the system can remember and use when answering. The document also describes iteration and refinement as the process of improving results by asking the AI to adjust its response. For example, if a generated exam is too difficult, the user can ask the AI to make it simpler. This back-and-forth process helps transform a first draft into a more useful final product.

Finally, the document provides guidance for writing effective prompts. Before asking the AI for help, users should define their objective, explain the context, specify the sources or references needed, and make their expectations clear. They should indicate the desired tone, audience, format, and purpose of the response. Courtesy and clarity are also encouraged, because respectful and specific instructions make it easier for the AI to understand the request. The document closes with guiding questions that help users improve their prompts: what the text should achieve, who the audience is, what tone is appropriate, which data or references should be included, how the response should be organized, and what exact result is expected.

In conclusion, the document presents generative AI and LLMs as powerful tools for learning, writing, organizing information, and supporting teaching, but it also warns that they require critical thinking. AI can accelerate work and provide useful first drafts, but it can also make mistakes, invent information, or reinforce habits shaped by engagement-driven algorithms. The most responsible approach is to use AI as an assistant, not as an unquestionable authority. In this model, Copilot or any similar AI tool supports the work, while the human user provides verification, professional judgment, ethical responsibility, and final decisions.

Explanation of how we use Copilot: For the summary about AI, LLMs, and Copilot, Copilot was used. A prompt was given asking it to create a summary of the document provided and discussed in class. The summary had to be written in English, delivered in a Word document, and presented in prose form.

Reflection: 

In my opinion, Artificial Intelligence can be a very useful support tool for teachers when it is used responsibly. AI can help create teaching materials, organize ideas, prepare summaries, design activities, and save time during lesson planning. If teachers know how to use it well, it can make classes more dynamic, creative, and easier to prepare.

However, AI also has important limitations. It can sometimes provide incorrect or incomplete information, and it does not fully understand the specific needs, context, or learning process of each group of students. For this reason, teachers should not depend on AI for everything. It is important to review and adapt the information, because the teacher is the one who has the professional knowledge, critical thinking, and responsibility for the class.