Lesson 1.1 | Generative AI in Contemporary Life

1. Lesson Overview

Welcome to COMP 3270 and to the first lesson of the course. In this opening lesson, you will explore what generative AI is, how it differs from other forms of artificial intelligence, and where it appears in contemporary life — from education and professional practice to creative work and everyday planning.

You will begin to recognise the capabilities of generative AI systems, the types of content they can produce, and the opportunities they create. At the same time, you will start to notice the limitations and risks that accompany these capabilities — a theme that will become central to the entire course.

This lesson also invites you to reflect on your own experiences, expectations, and concerns about generative AI. You will complete an initial self‑assessment, map an AI ecosystem, and begin to identify where human responsibility and judgment remain essential.

By the end of this lesson, you will have a working understanding of the foundational concepts needed to engage critically with generative AI. You will also be prepared to move on to Lesson 1.2, where we examine capabilities and risks in greater depth, and to Lesson 1.3, where we introduce the AI Paradox and the ORBIT framework.

In this lesson you will:

Define artificial intelligence and generative AI at an introductory level.
Distinguish generative AI from non‑generative AI applications.
Identify common types of content produced by generative AI systems.
Recognise common uses of generative AI in learning, work, creativity, and daily life.
Understand the AI ecosystem — model, system, application, and human roles.
Reflect on your initial experiences and concerns regarding generative AI.

2. Lesson Learning Outcomes

After completing this lesson, you will be able to:

Define artificial intelligence and generative artificial intelligence at an introductory conceptual level.
Distinguish generative AI from selected non‑generative AI applications.
Identify common types of content produced by generative AI systems.
Identify common uses of generative AI in learning, professional practice, organisational activity, creative work, and everyday life.
Explain the distinction between an AI model, system, application, and the broader ecosystem.
Map a simplified AI ecosystem, identifying human purpose, user, interface, model, output, review, stakeholders, and boundaries.
Reflect on your own experiences, expectations, concerns, and initial boundaries regarding generative AI.

3. Before You Begin

Assigned reading: Chapter 1 — Generative AI and the AI Paradox — read the opening sections (up to the introduction of the AI Paradox).
Prior knowledge: No prior knowledge of AI, programming, or mathematics is required. Bring your own experiences and observations of technology in daily life.
Optional approved AI tool: You may use an institutionally approved generative AI system for some activities. A supplied‑output alternative is always provided — you will not be disadvantaged if you choose not to use a live system.
Privacy reminder: Do not enter personal, confidential, proprietary, or sensitive information into any AI system. Use only synthetic, public, or generalised examples.
Academic integrity: All work submitted for credit must be your own. Using AI to generate content that you submit as your own work, without proper attribution or in violation of course policies, constitutes academic misconduct. Consult your course outline for specific guidance.
Estimated preparation time: 15–20 minutes to review the reading and set up your workspace.

Responsible‑use reminder: Do not enter personal, confidential, proprietary, privileged, or security‑sensitive information into any public or unapproved generative AI system. Use only synthetic, public, or generalised information for all activities in this lesson. If you are unsure whether information is suitable, do not enter it.

4. Opening Scenario

Scenario: A day in the life of an online learner

Maya is an adult online learner enrolled in a 300‑level course. She is also a communications coordinator at a small non‑profit. Over the past few months, she has noticed generative AI appearing in many parts of her life — sometimes helpfully, sometimes confusingly.

Study: Maya uses a free AI assistant to generate study questions from her textbook readings. The questions help her test her understanding, but sometimes they refer to concepts that were not actually in the reading.
Work: Her organisation recently started using an AI tool to draft routine emails and social‑media posts. The drafts save time, but Maya has caught several factual errors and tone mismatches.
Personal: She asked an AI chatbot for meal‑planning ideas and received a detailed weekly menu — complete with nutritional information that looked credible but turned out to be inconsistent with official dietary guidelines.
Online services: Her streaming service, email provider, and search engine all use AI features — some generative, some not.

Questions to consider:

In which of Maya's situations is she using generative AI, and which involve other forms of AI?
What opportunities does Maya experience in each situation?
What risks or limitations does she encounter?
What human judgment or verification is needed in each case?

Show guided analysis of the scenario

Generative vs. non‑generative:

Study questions: Generative — the system produces new text (questions) in response to Maya's request.
Workplace drafts: Generative — the system produces new email and social‑media content.
Meal‑planning: Generative — the system produces a new plan with nutritional information.
Streaming, search, email: These may involve non‑generative AI (recommendation systems, classification, ranking) as well as generative features (e.g., email smart‑reply).

Opportunities:

Time savings, convenience, idea generation, new perspectives, accessibility.

Risks and limitations:

Inaccurate or missing information (study questions, meal‑planning).
Factual errors and tone mismatches (workplace drafts).
Over‑reliance on convenient but potentially flawed outputs.
Hidden verification work — Maya must check everything.

Human judgment needed: In every case, Maya must review, verify, and take responsibility for the final output. The AI provides a draft or suggestion, but Maya remains accountable.

5. Core Lesson Content

The following sections introduce the foundational concepts for this course: what artificial intelligence is, what makes generative AI distinctive, the types of content it produces, where it appears in everyday life, and how to distinguish the model, the system, the application, and the wider ecosystem.

Read the sections in order. Each one builds on the previous, and each includes self‑check questions you can use to test your understanding before moving on.

6. What is Artificial Intelligence?

At its broadest level, artificial intelligence (AI) refers to the capacity of machines — particularly computer systems — to perform tasks that have historically required human intelligence. These tasks include reasoning, learning, perceiving, understanding language, solving problems, and making decisions.

Working definition

Artificial intelligence (AI): A field of computer science and engineering concerned with creating systems that can perform tasks that typically require human intelligence. In practice, AI systems use algorithms, data, and computational power to identify patterns, make predictions, or generate outputs.

AI is not a single technology but a broad field that includes many subfields and approaches:

Machine learning: Systems that improve their performance on a task through exposure to data, without being explicitly programmed for every step.
Natural language processing: Systems that understand, interpret, or generate human language.
Computer vision: Systems that analyse and interpret visual information.
Robotics: Systems that interact with the physical world.
Generative AI: A subset of AI that produces new content — text, images, audio, video, or other media — in response to prompts or inputs.

Key insight: AI is a broad umbrella. Generative AI is one part of that umbrella — but not all AI is generative, and not all AI systems produce new content.

Self‑check: What is AI?

Question: Which of the following is not a typical area of AI research or practice?

A. Systems that learn from data
B. Systems that understand human language
C. Systems that generate new text or images
D. Systems that perform arithmetic calculations exactly

Answer: D. Arithmetic calculations are a standard function of all computers; they are not distinctive of AI. While AI systems may perform arithmetic, the defining characteristic of AI is the ability to perform tasks that require some form of intelligence, learning, or adaptation.

7. What is Generative AI?

Generative artificial intelligence (generative AI) refers to a class of AI systems that can produce new content — such as text, images, audio, video, code, or structured data — in response to a user's input, often called a prompt.

Working definition

Generative AI: A type of artificial intelligence that creates new content (text, images, audio, video, code, plans, or other media) based on patterns learned from training data. The content is generated in response to a prompt or other input, rather than simply retrieved or classified.

[INSERT DEFINITION FROM CHAPTER 1]

What makes generative AI distinctive is its ability to produce content that did not previously exist in exactly that form. It does not merely search for and return an existing document, image, or answer; it generates a new response based on patterns it has learned.

Example

If you ask a traditional search engine "What is the capital of France?" it retrieves the answer from an existing index. If you ask a generative AI system the same question, it may generate a new sentence explaining that Paris is the capital — a response that is constructed on the fly, not simply retrieved.

But generative AI goes much further: it can also produce a poem about Paris, a travel itinerary, a summary of French history, or an image of Paris in the style of a painting.

Key characteristics of generative AI systems:

They respond to prompts — natural language instructions, questions, or descriptions.
They produce new content rather than simply retrieving existing content.
They are trained on large datasets of existing content (text, images, etc.) to learn patterns.
They generate outputs that are probabilistic — the same prompt may produce different outputs on different occasions.

Important: Generative AI systems do not understand content in the human sense. They are pattern‑matching and prediction systems that generate outputs based on statistical relationships in their training data. This is why they can produce fluent, confident‑sounding text that is nevertheless factually incorrect — a phenomenon we will examine in Lesson 1.2.

Self‑check: Generative AI definition

Question: Which of the following is a defining characteristic of generative AI?

A. It always produces factually accurate information.
B. It produces new content in response to a prompt.
C. It requires a paid subscription.
D. It can only generate text.

Answer: B. Generative AI produces new content in response to a prompt. It is not always accurate (A), does not always require payment (C), and can generate many types of content beyond text (D).

8. Generative vs. Non‑Generative AI

Not all AI systems generate new content. Many AI applications perform other tasks: they classify, recommend, translate (in a rule‑based way), predict, or summarise without necessarily generating novel outputs.

Table 1. Generative AI vs. non‑generative AI: a comparison
Characteristic	Generative AI	Non‑generative AI
Primary function	Create new content (text, images, audio, etc.)	Classify, predict, recommend, or retrieve existing information
Output	Novel content that did not previously exist in that form	A label, score, ranking, or existing piece of information
Examples	Text generation, image generation, code generation, music composition	Spam filtering, image recognition, recommendation systems, fraud detection
Training data	Large corpora of text, images, audio, etc.	Labelled data for classification, historical data for prediction

Examples to compare

Generative: An AI that writes a new essay on climate change based on a prompt.
Non‑generative: An AI that classifies an email as "spam" or "not spam."
Generative: An AI that creates a new image of a "cat wearing a hat" from a text description.
Non‑generative: An AI that identifies objects in a photograph (e.g., "cat," "hat").
Mixed: An AI search assistant that retrieves information and then summarises it — the retrieval is non‑generative, but the summarisation may be generative.

It is important to recognise that the boundary between generative and non‑generative AI is not always sharp. Some systems combine both approaches. For example, a customer‑service chatbot might retrieve answers from a knowledge base (non‑generative) and then rephrase them (generative). In this course, we focus primarily on systems with a significant generative component.

Quick practice

Consider your own experience: can you identify an AI system you have used that is generative? Can you identify one that is non‑generative? Write down one example of each before moving on.

Show possible examples

Generative examples: ChatGPT, Claude, Gemini, image generators (Midjourney, DALL·E), AI code assistants, AI writing tools.

Non‑generative examples: Spam filters, search‑engine ranking, recommendation systems (Netflix, Amazon), voice assistants (Siri, Alexa — when they retrieve information), facial recognition, language translation (rule‑based or statistical, without generative rephrasing).

Note: Some tools that were once non‑generative may now include generative features. The distinction depends on whether the system produces new content in response to a prompt.

9. Types of Content Produced by Generative AI

Generative AI systems can produce a wide variety of content types. Not every system produces every type — but the range is broad and growing.

Table 2. Common types of content generated by AI systems
Content type	Description	Example
Text	Essays, articles, summaries, stories, poems, emails	"Write a summary of this research article."
Summaries	Condensed versions of longer documents	"Summarise this 20‑page report in 2 paragraphs."
Questions	Study questions, interview questions, quiz items	"Generate 5 multiple‑choice questions about photosynthesis."
Explanations	Concept explanations, lessons, step‑by‑step guides	"Explain quantum computing to a beginner."
Translations	Text translated from one language to another	"Translate this email from English to French."
Software code	Programming code in various languages	"Write a Python function to sort a list."
Images	Photographs, illustrations, diagrams, art	"Generate an image of a futuristic city."
Audio	Speech, music, sound effects	"Compose a short jazz piece."
Video	Short video clips, animations	"Create a 5‑second animation of a bouncing ball."
Simulations	Virtual scenarios, models, or environments	"Simulate a conversation between two historical figures."
Recommendations	Personalised suggestions, plans, or options	"Suggest a 3‑day itinerary for Vancouver."
Plans	Project plans, study schedules, meal plans	"Create a 4‑week study schedule for my exam."
Structured information	Tables, charts, data summaries, formatted documents	"Create a table comparing three job offers."

Note: The ability to generate a particular content type depends on the system's training data and architecture. A text‑based model cannot generate images; an image‑generation model cannot generate music (unless it is multimodal).

Self‑check: Content types

Question: Which of the following content types can a text‑only generative AI system produce?

A. A photograph of a cat
B. A musical composition
C. A summary of a news article
D. A 3D model

Answer: C. A text‑only system can generate a summary (which is text). It cannot generate images, music, or 3D models unless it has multimodal capabilities.

10. Applications in Everyday Life

Generative AI is becoming embedded in many aspects of contemporary life. Below are common application contexts — but remember that not every use is equally suitable or equally low‑risk.

Table 3. Common application contexts for generative AI
Context	Description	Example uses
Learning and study	Supporting comprehension, practice, and review	Generating study questions, explaining concepts, creating flashcards
Research assistance	Helping locate, summarise, or synthesise information	Summarising articles, generating literature‑review outlines
Writing and communication	Drafting, editing, rephrasing, or translating text	Drafting emails, revising essays, translating documents
Creative work	Ideation, prototyping, and artistic expression	Generating story ideas, composing music, creating concept art
Software development	Assisting with coding, debugging, and documentation	Generating code snippets, explaining code, writing tests
Professional practice	Supporting work in law, finance, health, education, etc.	Drafting legal memos, generating financial reports, creating patient summaries
Workplace administration	Streamlining routine tasks and documentation	Generating meeting minutes, drafting policy documents, creating job descriptions
Organisational decision support	Providing analysis, scenarios, or recommendations	Generating market analysis, risk assessments, strategic options
Public information	Making information accessible and understandable	Translating government documents, creating summaries of public reports
Personal planning	Helping organise daily life, travel, or projects	Generating travel itineraries, meal plans, study schedules
Consumer comparison	Comparing products, services, or options	Generating product comparisons, summarising reviews
AI assistants	General‑purpose conversational agents	Chatbots, virtual assistants, copilots
AI agents (introductory)	Systems that take actions on behalf of users	Automated scheduling, data retrieval, task execution

Important: These applications vary widely in their risk profile. Generating a study question is very different from generating a legal memo or a health diagnosis. Context matters — we will examine this in Lesson 1.2 and in depth in Unit 3.

Self‑check: Applications

Question: Which of the following uses of generative AI would typically be considered higher risk?

A. Generating a recipe for dinner
B. Generating a study guide for an exam
C. Generating a draft of a legal contract
D. Generating a poem for a friend

Answer: C. A draft of a legal contract has significant potential consequences if it contains errors or omissions. It is a higher‑risk use than generating a recipe, a study guide, or a poem.

11. AI Model, System, Application, and Ecosystem

To understand generative AI clearly, it is helpful to distinguish several interrelated concepts. These terms are often used loosely — but in this course, we use them with specific meanings.

Key distinctions

AI model: The computational component that has been trained on data to perform a specific task. The model is the "engine" — a set of parameters and weights that encode patterns learned from training data.
AI system: A complete operational arrangement that includes the model, plus the infrastructure, interface, and processes that make it usable. A system may include multiple models, retrieval mechanisms, safety filters, and user interfaces.
AI application: A specific tool or service that users interact with directly. An application is built on top of one or more AI systems. (The word "app" is often used interchangeably with "application.")
AI ecosystem: The broader environment that includes not only models, systems, and applications, but also the people who use them, the data they consume, the organisations that develop and deploy them, the policies and regulations that govern them, and the social and cultural contexts in which they operate.

Example

Model: GPT‑4 (the underlying language model).
System: The full ChatGPT service, including the model, the chat interface, safety filters, and the infrastructure that powers it.
Application: The ChatGPT web or mobile app that users interact with.
Ecosystem: The broader context — the developers, the users, the training data, the policies governing use, the societal impacts, the competitive landscape, and the regulatory environment.

This distinction matters because responsibility is distributed across the ecosystem. A model may generate a problematic output, but the system may include filters to catch it. The application may present it in a way that shapes how users interpret it. And the ecosystem — including developers, users, and regulators — ultimately determines what is acceptable and what is not.

Self‑check: Model vs. system vs. application

Question: A student uses a web‑based AI tool to generate a summary of a research article. The tool uses a large language model and includes a built‑in plagiarism checker. Which of the following is the system?

A. The large language model that generates the summary
B. The full web service, including the model, the interface, and the plagiarism checker
C. The web browser the student uses to access the tool
D. The student themselves

Answer: B. The system includes the model, the interface, the plagiarism checker, and the infrastructure that makes it work. The model (A) is part of the system. The browser (C) is a separate tool. The student (D) is a user, not part of the system.

Quick practice: Ecosystem mapping

Think about a generative AI tool you have used (or seen used). Try to identify the model, system, application, and ecosystem elements involved. Write a brief description of each.

Show an example

Tool: A university‑provided AI assistant that generates study questions from lecture slides.

Model: A language model trained on academic text.
System: The full service, including the model, a retrieval component that extracts text from slides, and a content filter.
Application: The web interface or tool within the learning management system.
Ecosystem: The university, the IT department, the instructors, the students, the data‑privacy policies, the academic‑integrity guidelines, and the broader context of educational technology.

12. Widespread but Uneven Presence

Generative AI is becoming increasingly widespread, but its presence is uneven. Access to generative AI systems varies across regions, languages, cultures, socioeconomic groups, and professional fields.

Key observations:

Geographic variation: Access to AI systems, particularly paid or high‑performance versions, varies by country and region.
Language variation: Most generative AI systems are trained predominantly on English‑language data and perform much less effectively in many other languages.
Cultural variation: AI systems may reflect the cultural assumptions and biases of their training data, which may not align with the user's cultural context.
Professional variation: Generative AI is being adopted at different rates across different professions and industries.
Digital access: Access to the internet, devices, and digital literacy all affect who can use generative AI and how effectively.

For this course, it is important to recognise that your own experience with generative AI may be very different from that of other students. Some students may have extensive experience with AI tools; others may have limited or no experience. Some may have access to premium systems; others may use only free or public tools. Some may have used AI in a workplace; others may have only encountered it in personal contexts.

All of these experiences are valid starting points. This course is designed to meet you where you are, regardless of your prior exposure.

Reflection

Before moving on, take a moment to reflect:

How much exposure have you had to generative AI?
What contexts (study, work, personal, etc.) have you encountered it in?
What factors (language, access, tools, etc.) have shaped your experience?
Are there aspects of generative AI that you have not yet encountered but are curious about?

You will have an opportunity to document these reflections more formally in the self‑assessment activity later in this lesson.

Core content summary:

Artificial intelligence is a broad field concerned with creating systems that perform tasks requiring human intelligence.
Generative AI is a subset of AI that creates new content — text, images, audio, video, code, and more — in response to prompts.
Generative AI differs from non‑generative AI, which classifies, predicts, recommends, or retrieves information.
Generative AI can produce many types of content, but not every system produces every type.
Generative AI appears in many contexts — learning, work, creativity, personal life — but not all uses are equally suitable or low‑risk.
It is useful to distinguish between model, system, application, and ecosystem.
The presence of generative AI is widespread but uneven — access and experience vary widely.

13. Worked Example: Using Generative AI for Language Translation

This worked example demonstrates how to analyse a generative AI use by identifying the human purpose, the AI capability, the type of content, the opportunity, the limitations, the stakeholders, and the human‑review requirements.

Scenario: A university student named Priya is preparing a bilingual presentation. She has written her presentation notes in English, but she needs a version in French for a portion of the audience. She uses a generative AI translation tool to translate her notes from English to French.

Table 4. Analysis of Priya's translation use case
Element	Analysis
Human purpose	To produce a French‑language version of presentation notes for a bilingual audience.
AI capability	Generative translation — producing French text from English input.
Type of generated content	Text (translated presentation notes).
Opportunity	Priya can create a French version quickly and efficiently, without needing to be fluent in French herself.
Limitations or risks	The translation may contain errors in terminology, tone, or cultural nuance. It might misinterpret technical terms or idiomatic expressions.
Affected stakeholders	Priya (the user), the audience (who receive the translation), and anyone who relies on the accuracy of the presentation.
Human‑review need	Priya must review the translation carefully, especially for technical terms, names, and key concepts. If possible, a fluent French speaker should verify the translation.
Information boundary	Priya should not input any confidential or proprietary information into the translation tool unless it is approved and secure.
Preliminary conclusion	Using AI for a first draft of a translation can be efficient and helpful, but human review is essential. The AI‑generated translation should be treated as a draft, not a final product.

Key takeaway: A generative AI system can be technically capable of producing a translation, but that does not mean the translation is contextually suitable for use without verification. Priya's responsibility is to ensure the translation meets the needs of her audience.

Show reflection questions for this worked example

What other factors might affect the quality of the translation? (e.g., language pair, domain‑specific terminology, cultural references).
What would change if Priya were translating a legal contract instead of presentation notes?
What if Priya were using a free, public translation tool versus an enterprise‑grade tool?

Discussion: These questions highlight how context — including the domain, the stakes, and the tool — shapes what is appropriate and how much review is required.

14. Guided Learning Activities

Activity 14.1: Initial AI‑Use Self‑Assessment

Purpose: To document your current experiences, expectations, and concerns regarding generative AI. This will serve as a baseline for your learning throughout the course and can inform your Assignment 1 journal entries.
Learning outcome: Reflect on your own experiences, expectations, concerns, and initial boundaries regarding generative AI.
Estimated time: 15–20 minutes.
Materials: A notebook, document, or journal (digital or physical).

Instructions: Using the prompts below, write a brief self‑assessment. Do not disclose sensitive personal experiences. Focus on general observations and reflections.

AI systems or features you have encountered: List any AI systems, tools, or features you have used or noticed. These may include chatbots, search engines, writing assistants, image generators, recommendation systems, etc.
Purposes for which you have used AI: For what purposes have you used AI? (e.g., study help, writing, personal planning, entertainment, work).
Confidence in evaluating outputs: How confident do you feel in evaluating whether an AI‑generated output is accurate, appropriate, or reliable? (Scale: very low / low / moderate / high / very high).
Perceived benefits: What benefits do you see from generative AI? What does it make easier or more accessible?
Concerns: What concerns do you have about generative AI? What makes you uncertain, cautious, or worried?
Information you would not provide: What types of information would you not enter into an AI system? Why?
Expectations for COMP 327: What do you hope to learn in this course? What questions do you want answered?

Show self‑review checklist

Have you listed at least 3 AI systems or features you have encountered?
Have you identified at least 2 purposes for which you have used AI?
Have you rated your confidence and explained why you chose that rating?
Have you identified at least 2 benefits and 2 concerns?
Have you specified at least 2 types of information you would not enter into an AI system?
Have you written at least 2 expectations for this course?

Activity 14.2: Generative or Non‑Generative?

Purpose: To practise distinguishing generative AI from non‑generative AI applications.
Learning outcome: Distinguish generative AI from selected non‑generative AI applications.
Estimated time: 15–20 minutes.

Instructions: For each of the following examples, classify it as:

Generative AI — the system produces new content in response to a prompt.
Non‑generative AI — the system classifies, recommends, predicts, or retrieves information.
Mixed or uncertain — the system may combine generative and non‑generative elements.
Insufficient information — you need more details to make a classification.

An AI system that suggests movies you might like based on your viewing history.
An AI system that writes a new blog post based on a topic you provide.
An AI system that flags spam emails in your inbox.
An AI system that generates a new image of a "panda eating a watermelon."
An AI system that translates a document from English to Spanish.
An AI system that corrects spelling errors in a text document.
An AI system that answers a question by retrieving information from a knowledge base and then rephrasing it in natural language.
A calculator that performs arithmetic operations.

Show answers and explanations

Non‑generative AI — recommendation systems are typically non‑generative; they rank or classify existing options.
Generative AI — producing a new blog post is a generative task.
Non‑generative AI — spam filtering is classification.
Generative AI — generating a new image is a generative task.
Mixed or uncertain — translation can be generative (if the system rephrases or produces new text) or non‑generative (if it uses rule‑based or retrieval‑based methods). Many modern translation tools are generative.
Non‑generative AI — spelling correction is typically a rule‑based or classification task.
Mixed or uncertain — retrieval is non‑generative, but rephrasing is generative. This is a mixed system.
Insufficient information — a calculator is a standard computational tool, not AI. If it uses AI for some reason, we would need more information.

Explanation: The key distinction is whether the system produces new content in response to a prompt, rather than simply classifying, recommending, or retrieving existing information.

Activity 14.3: AI Ecosystem Snapshot

Purpose: To map a simplified AI ecosystem by identifying the key components and human roles.
Learning outcome: Map a simplified AI ecosystem, identifying human purpose, user, interface, model, output, review, stakeholders, and boundaries.
Estimated time: 20–25 minutes.

Instructions: Choose a generative AI application you are familiar with (or use the worked example on translation). Create an introductory ecosystem map that includes the following elements:

Human purpose: What goal does the human user have?
User: Who is using the system?
Interface or application: What tool or service is being used?
Model: What underlying model is involved (if known)?
Optional source or tool: Are there any additional data sources or tools involved?
Generated output: What content does the system produce?
Human review: Who reviews or verifies the output, and how?
Affected person or group: Who is affected by the output or its use?
Applicable boundary or rule: What rules, policies, or constraints apply?

You may present your map as a diagram, a table, a list, or a short narrative.

Show a solution design (example framework)

Example: Using a generative AI study assistant

Human purpose: To generate practice questions for an upcoming exam.
User: A university student.
Interface/application: An AI study‑tool web app.
Model: A large language model (e.g., version X).
Optional source: The student uploads their lecture notes as a source document.
Generated output: A set of 10 multiple‑choice questions with answers.
Human review: The student checks each question for accuracy and relevance to the exam content.
Affected person/group: The student (learning outcomes), the instructor (assessment integrity).
Boundary/rule: The student does not input any copyrighted or confidential materials; the student verifies all answers before use.

15. Exercises

These exercises are designed to help you apply the concepts from this lesson. Attempt each exercise before revealing the solutions or hints.

Exercise 1: Define and Distinguish

Learning outcome: Define artificial intelligence and generative artificial intelligence at an introductory conceptual level.
Estimated time: 10–12 minutes.
Task: Write a brief definition of each of the following terms, and then explain the key difference between them: (1) artificial intelligence (AI); (2) generative artificial intelligence (generative AI). Your definitions should be accessible to a university student without a technical background. Use your own words.

Show sample solution

Artificial intelligence (AI): A field of computer science and engineering that creates systems capable of performing tasks that typically require human intelligence, such as understanding language, recognising patterns, solving problems, and making decisions.

Generative artificial intelligence (generative AI): A subset of AI that creates new content — such as text, images, audio, video, or code — in response to prompts or inputs, based on patterns learned from training data.

Key difference: While all generative AI is AI, not all AI is generative. Non‑generative AI systems may classify, recommend, predict, or retrieve information, but they do not produce new content in the same way. Generative AI is distinguished by its ability to create novel outputs that did not previously exist in that exact form.

Exercise 2: Classify Generated Content and AI Activity

Learning outcome: Identify common types of content produced by generative AI systems.
Estimated time: 10–12 minutes.
Task: For each of the following activities, identify (a) whether it involves generative AI, and (b) what type of content is being generated (if any).

An AI system produces a 200‑word summary of a 50‑page government report.
An AI system identifies faces in a photograph.
An AI system composes a short piano piece in the style of a known composer.
An AI system classifies customer reviews as "positive," "neutral," or "negative."
An AI system generates a personalised 7‑day meal plan with recipes.

Show answers and explanations

Generative — the system produces a new summary (text).
Non‑generative — face identification is classification (no new content is produced).
Generative — the system composes a new piece of music (audio).
Non‑generative — sentiment classification is a labelling task.
Generative — the system produces a new meal plan with recipes (text, plans).

Explanation: Generative tasks produce new content; non‑generative tasks classify, label, or retrieve existing information.

Exercise 3: Identify Human Responsibility in the AI Ecosystem

Learning outcome: Identify where human responsibility appears in an introductory AI ecosystem.
Estimated time: 12–15 minutes.
Task: Consider the following scenario and identify at least four points where human responsibility, judgment, or oversight is required. For each point, briefly explain what the human must do and why it matters.

Scenario: A small business owner uses a generative AI tool to draft a customer‑facing newsletter. The tool generates a draft that includes product descriptions, a special offer, and a call to action. The owner plans to send the newsletter to 10,000 subscribers.

Show sample solution

1. Input quality: The owner must ensure that the information provided to the AI (e.g., product details, offer terms) is accurate and complete. If the AI receives incorrect information, the output will be incorrect.
2. Output review: The owner must review the draft for factual errors, misleading claims, tone, and cultural appropriateness. The AI may generate fluent text that contains subtle errors.
3. Brand and audience alignment: The owner must ensure the newsletter aligns with the brand's voice and is appropriate for the audience. The AI may not understand the brand's values or the audience's expectations.
4. Legal and regulatory compliance: The owner must ensure the newsletter complies with relevant laws (e.g., privacy, advertising standards, accessibility). The AI is not aware of these requirements.
5. Accountability: If the newsletter contains an error that causes harm or loss, the owner — not the AI — is responsible. The owner must be prepared to take accountability for the final product.

Explanation: Human responsibility is required at every stage of the AI‑supported workflow. The AI is a tool, not a replacement for human judgment, accountability, or oversight.

16. Homework Problems

Formative · Ungraded · Recommended preparation for Assignment 1

Homework 1: Concept Distinction

Estimated time: 8–10 minutes.
Task: Distinguish between generative AI and non‑generative AI by providing two examples of each and explaining why each example fits its category.

Show hint

Think about whether the system produces new content (generative) or whether it classifies, recommends, retrieves, or predicts (non‑generative).

Show sample solution

Generative AI examples:

AI writing assistant: Generates a new blog post based on a topic. This is generative because it produces new text.
AI image generator: Creates a new image from a text description. This is generative because it produces a new visual output.

Non‑generative AI examples:

Spam filter: Classifies emails as "spam" or "not spam." This is non‑generative because it labels existing content rather than creating new content.
Recommendation system: Suggests movies based on viewing history. This is non‑generative because it ranks or selects existing content.

Homework 2: Content Type Classification

Estimated time: 10–12 minutes.
Task: Classify each of the following as a type of content that can or cannot be produced by a typical text‑only generative AI system. Explain your reasoning.

A 5‑paragraph essay
A high‑resolution photograph
A computer program in Python
A musical composition (sheet music)
A summary of a research article

Show hint

A text‑only system can only produce text‑based outputs. Consider whether the content type is textual or requires other modalities.

Show sample solution

Can produce — an essay is text.
Cannot produce — a photograph is an image; a text‑only system cannot generate images.
Can produce — code is text (with syntax).
Can produce (with limitations) — sheet music can be represented as text (e.g., in ABC notation) or as a symbolic format. Many text‑based models can generate musical notation in plain text.
Can produce — a summary is text.

Homework 3: Ecosystem Mapping

Estimated time: 15–18 minutes.
Task: You are a customer‑service manager at a small online retailer. You are considering using a generative AI tool to handle routine customer inquiries. Map the ecosystem for this use, identifying at least the following: human purpose, user, application, output, human review, stakeholders, and boundaries/rules.

Show hint 1: Identify the components

Start by defining the human purpose (e.g., "To provide efficient, accurate, and consistent responses to routine customer inquiries"). Then consider who the users are, what application is used, what output is produced, and who reviews it.

Show hint 2: Consider stakeholders and boundaries

Who is affected by this use? Consider customers, employees, the company, and regulators. What rules or policies apply? Think about privacy, data security, consumer protection, and brand consistency.

Show solution design

Human purpose: To provide efficient, consistent, and accurate responses to routine customer inquiries, reducing wait times and freeing up human agents for complex issues.
User: Customers contacting the retailer through the website, email, or chat.
Application: A generative AI chatbot integrated into the retailer's website and customer‑service platform.
Model: A large language model fine‑tuned for customer service (specific version TBD).
Optional source/tool: The chatbot may have access to the retailer's product catalogue, order history, and FAQ knowledge base.
Generated output: Text responses to customer questions (e.g., order status, return policy, product information).
Human review: A customer‑service manager periodically reviews a sample of interactions for accuracy, tone, and compliance. If the chatbot cannot answer a question, it escalates to a human agent.
Affected person/group: Customers (receive responses), customer‑service agents (workload changes), the company (brand reputation, cost savings).
Boundary/rule: The chatbot should not collect or store sensitive personal data beyond what is necessary. It must comply with privacy regulations and the company's data‑security policies. It should not provide legal, medical, or financial advice.

Homework 4: Application Identification

Estimated time: 8–10 minutes.
Task: For each of the following contexts, identify one plausible generative AI application and one reason why that application might be useful.

Learning and study
Professional practice (e.g., law, finance, health)
Creative work
Everyday life

Show hint

Think about the tasks that people in each context perform. What could be accelerated, augmented, or improved with AI‑generated content?

Show sample solution

Learning: Generating practice questions from lecture notes. Useful for self‑testing and exam preparation.
Professional practice: Summarising legal documents for a lawyer. Useful for quickly understanding lengthy case files.
Creative work: Generating story prompts or character ideas for a writer. Useful for overcoming creative block.
Everyday life: Generating a weekly meal plan with a shopping list. Useful for saving time and reducing decision fatigue.

Homework 5: Stakeholder Identification

Estimated time: 10–12 minutes.
Task: Consider a generative AI application that creates personalised résumés for job applicants. Identify three stakeholders (people or groups) who might be affected by this use, and describe how they might be affected (positively or negatively).

Show hint

Think about the applicant, the employer, the AI developer, the recruiter, and anyone else who interacts with or is affected by the résumé.

Show sample solution

Job applicant: May benefit from a well‑structured, professional résumé that highlights their strengths. May be harmed if the AI generates inaccurate information or misrepresents their experience.
Employer or recruiter: May benefit from receiving well‑organised résumés that are easy to review. May be harmed if AI‑generated résumés are misleading or if the employer cannot differentiate between human‑written and AI‑written content, leading to poor hiring decisions.
AI developer: May benefit from usage data and revenue. May be harmed by reputational damage if the tool produces biased or inaccurate résumés.

Homework 6: Responsible‑Use Boundary

Estimated time: 10–12 minutes.
Task: For the résumé‑generation application described in Homework 5, identify two information boundaries or rules that users should observe. Explain why each boundary is important.

Show hint

Consider what information would be inappropriate to include in a résumé (e.g., sensitive personal data, false claims) and what policies might govern the use of such a tool.

Show sample solution

Boundary 1: Do not enter sensitive personal information (e.g., social insurance number, bank details, health information) into the AI tool. The tool may not be secure, and this information is not relevant to a résumé.
Boundary 2: Do not use the AI to fabricate or exaggerate qualifications. The applicant is responsible for the accuracy of the résumé and must verify all information before submission. Misrepresentation could lead to rejection, termination, or legal consequences.

Homework 7: Transfer to an Unfamiliar Context

Estimated time: 12–15 minutes.
Task: You are a community health worker in a rural area. You need to create a multilingual information sheet about a new health initiative. How might you use generative AI to help with this task? What opportunities and limitations do you anticipate?

Show hint 1: Identify the opportunities

Generative AI can translate text, simplify complex information, and adapt tone for different audiences. Consider how these capabilities might help in a community health setting.

Show hint 2: Identify the limitations

Consider the risks of inaccurate translation, cultural misunderstanding, missing context, and the need for expert review of health information.

Show sample solution

Opportunity: Generative AI could be used to draft the information sheet in English and then translate it into the languages spoken in the community. It could also simplify technical health language into plain language accessible to a general audience.

Limitations and risks:

The AI may not accurately translate health terminology, leading to misunderstandings or harm.
Cultural differences may not be adequately addressed; what is appropriate in one cultural context may not be in another.
The AI may omit crucial information or caveats.
Final approval must come from a qualified health professional who understands both the health content and the community context.

Conclusion: Generative AI could be a useful drafting and translation tool, but it cannot replace the judgment of a health professional or community liaison. All content must be verified and approved before distribution.

17. Mini‑Project: Generative AI Application Profile

Project: Generative AI Application Profile

Purpose: To integrate the concepts from this lesson by analysing a bounded, low‑risk generative AI application.
Learning outcomes: Identify common uses of generative AI; distinguish generative from non‑generative AI; identify types of generated content; map an AI ecosystem; identify human responsibility.
Estimated effort: 45–60 minutes.

Instructions: Select one bounded and low‑risk generative AI application from your own experience or from the list below. Then complete the application profile template that follows.

Suggested application contexts (choose one):

Using generative AI to generate study questions from course notes
Using generative AI to draft a routine email at work
Using generative AI to generate a travel itinerary for a weekend trip
Using generative AI to create a social‑media caption for a personal post
Using generative AI to brainstorm ideas for a creative project
Using generative AI to summarise a news article
Using generative AI to generate a recipe or meal plan

Table 5. Generative AI application profile template
#	Element	Your response
1	Application — What is the tool or service?
2	Human purpose — What goal does the user have?
3	Intended user — Who uses the application?
4	Type of generated output — What content is produced?
5	Apparent capability — What can the system do?
6	Context — Where and when is this used?
7	Potential benefit — What is the intended benefit?
8	Apparent limitation — What might go wrong?
9	Human‑review need — Who checks the output?
10	Preliminary information boundary — What information should not be entered?
11	Question for Unit 2 — What technical question do you have?
12	Question for Unit 3 — What responsible‑use question do you have?

Privacy and academic integrity:

Use only synthetic, public, or generalised information. Do not enter personal, confidential, or proprietary data.
Your profile is for your own learning and may be used as evidence for Assignment 1. It is not a graded submission.
Do not copy this profile from another student or from an AI system. Use your own analysis.

Show self‑review checklist

Have you selected a bounded, low‑risk application?
Have you completed all 12 elements of the profile?
Have you used only synthetic, public, or generalised information?
Have you identified at least one limitation or risk?
Have you identified a human‑review need?
Have you posed at least one question for Unit 2 and one for Unit 3?

Show solution architecture (staged guidance)

Application: Identify the specific tool (e.g., "ChatGPT," "Claude," "a university‑provided AI study assistant") or describe a general type (e.g., "a generative AI recipe generator").
Human purpose: State the user's goal in one or two sentences.
Intended user: Describe the typical user (e.g., "university student," "busy parent," "small business owner").
Type of generated output: Specify the content type (text, image, audio, etc.).
Apparent capability: Describe what the system can do (e.g., "generate text," "create images," "translate language").
Context: Describe the setting and circumstances of use.
Potential benefit: Explain the intended benefit in concrete terms.
Apparent limitation: Identify at least one limitation or risk (e.g., "may produce inaccurate information," "may not understand cultural context").
Human‑review need: Describe who reviews the output and what they look for.
Information boundary: State what information should not be entered (e.g., "personal identifiers," "confidential data").
Question for Unit 2: Pose a question about how the technology works (e.g., "How does the model learn to generate text?").
Question for Unit 3: Pose a question about responsible use (e.g., "How can I verify the accuracy of AI‑generated information?").

Show illustrative partial example (different application)

Example: Using generative AI to generate a travel itinerary

Application: A free generative AI chatbot (e.g., a public web‑based assistant).
Human purpose: To generate a 3‑day itinerary for a weekend trip to a city the user has never visited.
Intended user: A leisure traveller with limited time to plan.
Type of generated output: Text (a structured itinerary with daily activities, restaurant suggestions, and travel tips).
Apparent capability: The system can generate a detailed, day‑by‑day plan based on the user's input (city, dates, interests).
Context: Personal travel planning, low stakes (no safety‑critical decisions).
Potential benefit: Saves planning time, provides a starting point, helps the user discover options.
Apparent limitation: The AI may suggest activities that are closed, overpriced, or poorly reviewed; it may not account for local events, weather, or transportation.
Human‑review need: The user must verify each activity (check hours, reviews, costs) and adjust based on personal preferences and current conditions.
Information boundary: Do not enter financial information, passport details, or exact dates of travel if they are sensitive.
Question for Unit 2: How does the AI know which attractions are popular in a given city?
Question for Unit 3: How can I determine whether an AI‑generated recommendation is trustworthy?

19. Assignment 1 Journal Connection

Unit 1 contributes to Assignment 1: AI Learning and Reflection Journal (15% of your final grade). The journal entries you write throughout Modules 1–6 will document your learning journey and your evolving understanding of generative AI.

Reflection prompts for Unit 1

After completing this lesson, consider writing a journal entry that addresses some of the following prompts. Your journal entries will be compiled and submitted as part of Assignment 1.

Current understanding: How do you currently define generative AI? What do you see as its key characteristics?
Previous experience: What experience have you had with generative AI systems? In what contexts have you used or encountered them?
Confidence: How confident do you feel in evaluating AI‑generated content? What factors contribute to your confidence (or lack thereof)?
Expectations: What do you hope to learn in COMP 327? What specific questions do you want answered?
Concerns: What concerns do you have about generative AI — for yourself, for your community, or for society more broadly?
Initial boundaries: What types of information would you not enter into an AI system? Why?

Your journal entries are personal and reflective. They do not need to be perfect; they are a record of your learning process.

Show journal planning checklist

Have you selected at least 3 prompts to address?
Have you written about your own experiences and perspectives (not what you think you "should" say)?
Have you maintained privacy boundaries (no sensitive personal information)?
Have you kept a record of any AI interactions you have had (e.g., prompts, outputs, your observations)?
Have you noted any questions you want to explore in later units?

Note: You may use this self‑assessment and other activities from this lesson as evidence for your journal.

20. Key Takeaways

Artificial intelligence is a broad field; generative AI is a subset that produces new content in response to prompts.
Generative AI differs from non‑generative AI (classification, recommendation, prediction, retrieval) in its ability to create novel outputs.
Generative AI can produce many types of content: text, images, audio, video, code, plans, summaries, and more. Not every system produces every type.
Generative AI appears in many contexts — learning, work, creativity, personal life — but context determines suitability and risk.
It is useful to distinguish between model, system, application, and ecosystem to understand where responsibility lies.
The presence of generative AI is widespread but uneven; access and experience vary across regions, languages, cultures, and demographics.
Human responsibility and judgment are essential at every stage of AI use — from input to output review to final deployment.
You should never enter personal, confidential, or sensitive information into public or unapproved AI systems.

21. Key Terms

Artificial intelligence (AI): A field of computer science and engineering concerned with creating systems that perform tasks requiring human intelligence.
Generative AI: A type of AI that creates new content — text, images, audio, video, code, etc. — based on patterns learned from training data, in response to prompts.
Non‑generative AI: AI systems that classify, recommend, predict, or retrieve information without producing new content.
Prompt: The input provided to a generative AI system, typically in natural language, that instructs or requests the system to generate a response.
Generated output: The content produced by a generative AI system in response to a prompt.
AI model: The computational component that has been trained on data to perform a specific task, encoding patterns in parameters and weights.
AI system: A complete operational arrangement that includes the model, infrastructure, interface, and supporting processes.
AI application: A specific tool or service that users interact with, built on one or more AI systems.
AI ecosystem: The broader environment including people, data, organisations, policies, regulations, and social contexts that shape AI use.
Human reviewer: A person who examines, verifies, and takes responsibility for AI‑generated output before it is used or deployed.
Information boundary: A limit on what information should be entered into an AI system, typically based on privacy, security, or confidentiality considerations.
Stakeholder: A person or group affected by or involved in the use of an AI system, including users, beneficiaries, and those who bear burdens or risks.

[See Appendix A for the complete Generative AI Glossary.]

22. Looking Ahead

In Lesson 1.1, you established a foundation: you learned what generative AI is, how it differs from non‑generative AI, what types of content it can produce, and where it appears in contemporary life.

In Lesson 1.2 — Capabilities, Opportunities, and Corresponding Risks, you will build on this foundation by examining specific capabilities of generative AI systems, the opportunities they create, and the corresponding risks and limitations that accompany them. You will explore why a system may be technically capable of producing a certain output without that output being contextually suitable, and why fluency, confidence, and professional formatting do not establish reliability.

In Lesson 1.3, you will then integrate these insights into the AI Paradox and the ORBIT framework, which will serve as the organising concepts for the entire course.

Distinguishing Unit 1, Unit 2, and Unit 3:

Unit 1 (this unit): Introduces generative AI, its applications, and the AI Paradox at a conceptual level.
Unit 2: Explains how generative AI works technically — training data, models, tokenisation, next‑token prediction, and more. (Detailed technical mechanisms are reserved for Unit 2.)
Unit 3: Addresses how to determine whether an AI use is effective and responsible — suitability assessment, risk classification, verification, and responsible‑use decision‑making. (Detailed procedures are reserved for Unit 3.)

Before moving on, ensure you have completed the self‑check quiz, the exercises, and the mini‑project. Reflect on what you have learned and what questions remain.

Continue to Lesson 1.2: Capabilities, Opportunities, and Risks Return to Unit 1 Home

23. Sources & Further Reading

Primary source:

COMP 3270 Course Textbook — Chapter 1: Generative AI and the AI Paradox (opening sections).

Supporting materials:

COMP 3270 Course Outline
Appendix A: Generative AI Glossary
Assignment 1: AI Learning and Reflection Journal (instructions and rubric)

Additional references (for further exploration):

[INSERT VERIFIED REFERENCE FROM CHAPTER 1]
[INSERT VERIFIED REFERENCE FROM APPENDIX A]
[AUTHOR OR SUBJECT‑MATTER EXPERT REVIEW REQUIRED]

Production note: All references and citations will be verified and updated in the final course materials. Do not treat unverified references as authoritative sources.

Generative AI in Contemporary Life

Table of Contents

1. Lesson Overview

2. Lesson Learning Outcomes

3. Before You Begin

4. Opening Scenario

5. Core Lesson Content

6. What is Artificial Intelligence?

7. What is Generative AI?

8. Generative vs. Non‑Generative AI

9. Types of Content Produced by Generative AI

10. Applications in Everyday Life

11. AI Model, System, Application, and Ecosystem

12. Widespread but Uneven Presence

13. Worked Example: Using Generative AI for Language Translation

14. Guided Learning Activities

15. Exercises

16. Homework Problems

17. Mini‑Project: Generative AI Application Profile

18. Self‑Check Quiz

19. Assignment 1 Journal Connection

20. Key Takeaways

21. Key Terms

22. Looking Ahead

23. Sources & Further Reading