Foundations
Explore artificial intelligence, generative AI, large language models, tokens, probabilistic generation, context, retrieval, tools, and multimodal systems.
Module overview
Module 1 introduces the foundations, capabilities, limitations, and principal applications of generative AI. You will examine how large language models generate content and why polished language does not establish understanding or accuracy. You will then use the AI Paradox and ORBIT frameworks to examine opportunities alongside corresponding risks, beneficiaries, oversight needs, and transparency requirements.
Critical judgment
Distinguish demonstrated capability from suitability, fluency from reliability, and speed from meaningful effectiveness.
Human responsibility
Define the human purpose, identify affected people, preserve agency, and make accountability explicit.
Module principle: Every attractive AI opportunity has corresponding limitations and risks. Responsible use requires examining both before deciding whether and how to proceed.
Module learning outcomes
By the end of Module 1, you should be able to:
- Define artificial intelligence, generative AI, large language models, tokens, prompts, outputs, context windows, retrieval, tools, and multimodal AI.
- Explain conceptually how a generative AI system produces content through probabilistic generation.
- Distinguish generated fluency from human-like understanding, factual accuracy, reliability, and contextual suitability.
- Identify common capabilities and limitations of generative AI without treating model output as authoritative.
- Distinguish efficiency from effectiveness and evaluate whether an AI use provides genuine net benefit.
- Identify the human problem, legitimate purpose, stakeholders, beneficiaries, risk bearers, and accountability requirements of an AI use.
- Apply the AI Paradox to pair an AI opportunity with corresponding risks and limitations.
- Apply the author-approved ORBIT framework to a bounded introductory use case.
- Compare an AI-assisted approach with a human-led, conventional digital, or non-AI alternative.
- Establish preliminary personal boundaries and learning goals for responsible AI use.
Module 1 units
Generative AI and the AI Paradox
Examine the evolution and main applications of generative AI, identify paired opportunities and risks, and begin applying ORBIT.
How Generative AI Works
Develop a conceptual understanding of training data, tokens, probabilistic generation, context windows, retrieval, tools, and multimodal systems.
Effective and Responsible AI Use
Evaluate suitability, effectiveness, human purpose, agency, accountability, boundaries, and when not to use AI.
Implementation note: replace the placeholder anchors with the corresponding Moodle page or section URLs.
Embedded Module 1 study guide
This guide follows the recommended two-week pace. If your individualized course schedule differs, follow the official dates in Moodle and preserve the sequence of learning activities.
Week 1: Unit 1 and the beginning of Unit 2
Recommended: 8–10 hours- Orient and establish a baseline.
Review the module outcomes, complete the initial AI-use self-assessment, and open your Assignment 1 working file. - Read Chapter 1 and complete Unit 1.
Focus on definitions, applications, opportunities, limitations, and the AI Paradox. - Explore a bounded AI task.
Compare several low-risk outputs and note capabilities, limitations, assumptions, and uncertainties. - Apply ORBIT provisionally.
Analyze one opportunity, its risks, affected people, oversight needs, and transparency requirements. - Begin Chapter 2.
Study tokens, probabilistic generation, context, and why output can be fluent without being reliable. - Write your own journal notes.
Record initial assumptions, confidence, concerns, and preliminary responsible-use boundaries.
Evidence to retain
- Initial AI-use self-assessment
- One opportunity-risk map
- One short ORBIT analysis
- Comparison notes from the exploration exercise
- Your own Assignment 1 reflection notes
Week 2: Complete Units 2 and 3
Recommended: 8–10 hours- Complete Chapter 2 and Unit 2.
Explain generation conceptually and distinguish models, interfaces, retrieval, tools, and multimodal inputs and outputs. - Test your understanding.
Reconstruct the generation process without notes, then correct gaps using the reading and tutorial feedback. - Read Chapter 3 and complete Unit 3.
Examine suitability, human purpose, effective versus merely efficient use, agency, and accountability. - Complete the fluency-reliability exercise.
Identify features that make an answer look credible and explain why those features do not prove accuracy. - Consolidate Module 1.
Complete the module quiz, cumulative exercise, and selected homework or mini-project work. - Prepare for Module 2.
Identify one prompt or task that you will improve through structured prompt design.
Evidence to retain
- Concept map or reconstruction of how generative AI works
- Fluency-versus-reliability analysis
- Effectiveness and net-benefit comparison
- Revised personal boundaries
- Questions to carry into prompt engineering
Show a recommended session routine
- Review the learning outcome and attempt retrieval.
- Complete the assigned reading.
- Work through the tutorial and guided activity.
- Use feedback to correct misunderstandings.
- Retain useful evidence for Assignment 1.
- Update your schedule and next action.
Assignment 1 starts in Module 1
Assignment 1: AI Learning and Reflection Journal is worth 15%. It begins during Module 1 and continues cumulatively through Module 6. The recommended submission point is the end of Module 6, in Week 11.
Official deadline: Record the exact Moodle date and time here: ______________________________
Begin now
- Create the journal file or approved workspace.
- Record your initial experience, confidence, expectations, and concerns.
- Preserve evidence from the exploration, AI Paradox, and ORBIT activities.
- Record how your boundaries begin to change.
Keep it authentically yours
- Write personal reflections yourself.
- Do not ask AI to invent experiences or reflective conclusions.
- Follow the assignment’s AI-use and disclosure instructions.
- Connect claims about AI to verified examples or course evidence.
Do not postpone the journal: Assignment 1 is designed to show development over time. A retrospective account written at the end cannot fully replace contemporaneous evidence from each module.
Show Module 1 journal-planning prompts
- What did you initially believe generative AI could and could not do?
- Which feature of fluent output most influenced your confidence?
- What opportunity-risk pair was most important in your selected case?
- What should remain under human control?
- Which personal boundary will you test or revise during Module 2?
Use these prompts to plan your own response. Do not treat them as a required model or ask AI to generate the reflection.
Required and recommended learning activities
| Activity | Purpose | Suggested evidence | Assignment connection |
|---|---|---|---|
| Initial AI-use self-assessment | Establish a baseline of experience, confidence, expectations, and concerns. | Dated personal baseline | Assignment 1 |
| Generative AI exploration | Observe capabilities and limitations using bounded low-risk tasks. | Prompt-output comparison notes | Assignments 1 and 3 |
| Opportunity-risk map | Pair each selected opportunity with corresponding risks and affected people. | Completed map | Assignments 1 and 3 |
| ORBIT mini-analysis | Examine opportunity, risk, beneficiaries, oversight, and transparency. | Structured analysis | Assignments 1, 3, and 6 |
| Fluency versus reliability | Explain why credibility cues do not establish accuracy. | Annotated response | Assignments 1 and 5 |
| Module reflection | Document learning, uncertainty, and changed boundaries. | Student-authored journal entry | Assignment 1 |
Live AI and non-platform options
Option A: Approved AI
Use an institutionally approved system with synthetic, public, or otherwise authorized information.
Option B: Supplied outputs
Use the prompts and outputs supplied in the tutorial. You will assess the same learning outcomes.
Option C: Non-AI comparison
Complete a comparable analysis using conventional search, writing, or organizational tools.
You must not be disadvantaged for selecting a supplied-output or non-platform option.
Key concepts to carry forward
Generative AILarge language modelTokenPromptProbabilistic generationContext windowRetrievalMultimodal AIFluencyReliabilitySuitabilityAI ParadoxORBITHuman accountability
Show a retrieval check
Before opening your notes, explain the difference between fluency, accuracy, reliability, and suitability. Then describe one generative AI opportunity and its corresponding risk. Finally, identify who remains accountable for deciding whether the use proceeds.
Responsible use, accessibility, and support
Responsible-use boundaries
- Do not enter confidential, personal, proprietary, privileged, security-sensitive, or culturally governed information into an unapproved system.
- Treat outputs as provisional.
- Verify important claims independently.
- Keep human purpose, decisions, and accountability explicit.
Accessibility and support
- Use supplied alternatives if a live system is inaccessible, unavailable, or unwanted.
- Contact accessibility services for approved accommodations.
- Contact the course coordinator for content, assignment, or permitted-use questions.
- Use technical support for Moodle or submission problems.