Welcome to developing AI literacy for academic practice
The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.
Alvin Toffler
You’re about to begin an 11-lesson course that develops comprehensive AI literacy—the multidimensional capability to engage with generative AI critically, effectively, and responsibly across all aspects of your academic work.
This isn’t a course about learning software or memorising prompts. It’s about developing integrated capability that combines understanding, critical thinking, practical competence, ethical awareness, and professional judgement. By the end, you’ll have the literacy to navigate AI integration thoughtfully—knowing when AI enhances your work and when it undermines it.
How this course works
Active engagement required
This course develops capability through practice, not passive reading. Each lesson includes activities using your actual academic work—your research, your teaching, your writing. You won’t complete hypothetical exercises; you’ll develop literacy whilst making progress on real projects.
Expect 40+ practice activities across the 11 lessons. Most take 5-15 minutes. Some ask you to try approaches and compare results. Others prompt reflection on your current practice. All are designed to build capability through application.
Structured progression
The course moves through four modules:
- Foundation (Lessons 1-2): Conceptual understanding of what AI is and how to communicate effectively with it
- Substitution (Lessons 3-5): Integrating AI into existing workflows for routine tasks
- Adaptation (Lessons 6-8): Reshaping practice around AI capabilities for genuinely different approaches to academic work
- Transformation (Lessons 9-11): Making AI a permanent part of professional infrastructure through context development, taste cultivation, and structural integration
This progression mirrors how expertise develops across all literacy domains—from foundational competence through sophisticated application to reflective practice.
Honest about limitations
You’ll learn when AI creates false efficiency, when it adds overhead rather than saving time, and how to recognise meaningful engagement versus superficial assistance. Not every academic task benefits from AI collaboration. Developing literacy means knowing the difference.
Self-paced but focused
Most participants complete the course over 4-6 weeks, spending 2-3 hours per week. Each lesson takes 40-75 minutes and includes progress tracking so you always know where you are. Work at your own pace, but commit focused time rather than scattered attention.
Practice with real work
Activities use your actual academic materials. When a lesson asks you to practice argument development, you’ll work on your actual argument. When teaching content creation, you’ll develop materials for your actual teaching. This means time spent isn’t separate from your workflow—you’re making progress on real projects whilst developing literacy.
What you’ll need
Technical requirements
- Access to a generative AI platform (ChatGPT, Claude, or similar)
- Ability to copy and paste text
- Your own academic work to practice with
- A dedicated space for your Action Journal (see below)
That’s it. No special software, no technical expertise beyond basic computer use.
Requirement: Your Action Journal
This course isn’t just about reading—it’s about building capability through deliberate practice. Before you start Lesson 1, you need to create a dedicated space for your reflections and commitments. This is your Action Journal.
Options for your Action Journal:
- A physical notebook on your desk (recommended)
- A dedicated document in Notion, OneNote, or Google Docs
- The Notes app on your phone or computer
- Any other system you access daily as part of your workflow
Why this matters:
Throughout this course, you’ll encounter “pause and reflect” prompts asking you to document your thinking, make specific commitments, and plan concrete actions. When you type responses into a form field in your browser, that learning often stays trapped in the webpage—disconnected from your actual work.
When you write these reflections in your own workspace—the notebook that sits on your desk, the document you access daily, the notes system you already use—you’re physically moving knowledge into your workflow. Your reflections live alongside your to-do list, your project notes, your actual academic work.
This isn’t busy-work. This is how transfer of learning happens. Ideas that stay in browser tabs don’t change practice. Ideas documented in your workspace become action.
What you’ll document:
The reflection prompts throughout the course ask you to:
- Identify specific people, tasks, or projects where you’ll apply concepts
- Commit to concrete actions with specific timeframes
- Assess your current practice honestly and note patterns
- Plan how frameworks transfer to your actual work context
- Document your developing literacy as evidence of growth
These aren’t abstract questions about what you thought of the lesson. They’re commitments that bridge the gap between learning and doing.
Before you proceed to Lesson 1:
Open your Action Journal now. Write today’s date and the heading “AI Literacy Development Course - Starting Point.” Then answer:
- What do I hope to achieve through this course?
- What specific aspect of my academic work feels most overwhelming right now?
- How would I describe my current relationship with AI tools?
This establishes your baseline. You’ll revisit it at course completion to recognise how far you’ve developed.
Your Action Journal is where learning becomes capability. Treat it accordingly.
Time commitment
- 10-12 hours total across 11 lessons
- Most lessons: 40-75 minutes each
- Activities: 5-15 minutes each (40+ total across course)
- Recommended pace: 2-3 lessons per week
Mindset requirements
- Willingness to practice with your actual work
- Openness to honest self-assessment
- Commitment to active engagement rather than passive reading
- Acceptance that literacy develops through sustained practice, not quick fixes
- Discipline to document reflections in your Action Journal rather than skipping “pause and reflect” moments
How to approach the course
Complete lessons in order
Each lesson builds on previous concepts. The progression from foundation through substitution and adaptation to transformation is developmental. Skipping ahead will create gaps in understanding.
Do the activities
The activities are where literacy develops. You can skip them and read the lessons, but you won’t build the capability the course aims to develop. Literacy comes from practice, not passive consumption.
Use your Action Journal consistently
Every “pause and reflect” prompt throughout the course asks you to document in your Action Journal. Don’t type responses into browser forms or skip these moments. Open your journal, write the date and lesson number, and complete the reflection. These documented commitments are how learning transfers from course content to actual practice. Your journal becomes evidence of your developing literacy.
Use the self-assessment tools
Many lessons include calibration activities that help you recognise patterns in your current practice. These aren’t busy-work—they create awareness about where literacy development will have the most impact.
Engage with reflection prompts seriously
Reflection throughout each lesson documents your developing understanding and creates context for future work. These prompts help you articulate what you’re learning and why it matters for your practice. Write specific commitments with concrete timeframes rather than vague intentions.
Be honest about what works
The course repeatedly asks you to evaluate whether approaches actually save time or add value. Developing literacy means recognising when AI doesn’t help, not convincing yourself every technique must be useful.
Apply concepts immediately
Don’t wait until completing the course to start applying what you learn. Each lesson develops specific capabilities you can use immediately in your work.
What makes this course different
Most “AI for academics” courses focus on functional skills—here’s how to write prompts, here’s how to use AI for specific tasks. This course develops comprehensive literacy across six dimensions:
- Access and recognition: Understanding when and where AI is relevant
- Critical evaluation: Assessing quality, reliability, and limitations
- Functional application: Using AI effectively across contexts
- Creation and communication: Generating meaningful outputs through collaboration
- Ethical awareness: Understanding implications for integrity and values
- Contextual judgement: Developing professional taste about meaningful engagement
This multidimensional approach means you’re not just learning tools—you’re developing the integrated capability to navigate AI engagement thoughtfully across all aspects of your scholarly practice.
A note on literacy development
Literacy isn’t binary (literate/illiterate) or fixed (once achieved, always maintained). It’s developmental and requires ongoing cultivation.
You’ll begin this course wherever you are in your current AI engagement—whether that’s complete novice or experienced user with incomplete mental models. You’ll progress through increasing sophistication at your own pace. And you’ll continue developing literacy beyond this course through practice, reflection, and adaptation.
The course provides frameworks, techniques, and conceptual foundations. Literacy develops through sustained practice and accumulated experience. Think of this as establishing foundations for ongoing development rather than achieving a final state of “being literate.”
What to expect
From yourself: Active engagement, honest self-assessment, willingness to practice with real work, and acceptance that developing literacy takes focused time.
From the course: Clear explanations, practical frameworks, honest discussion of limitations, structured activities for capability development, and guidance for ongoing literacy cultivation.
From AI: Imperfect collaboration requiring your critical evaluation, complementary capabilities that extend your thinking whilst requiring your scholarly judgement, and outputs that need verification and adaptation to serve your goals.
Ready to begin?
The first lesson establishes conceptual foundations: what generative AI actually is and how to approach it as language-based cognitive extension rather than software to operate.
This understanding shapes everything that follows. The mental model you develop in Lesson 1 determines what’s possible in all subsequent engagement.
Take your time with foundational concepts. They’re worth getting right.
Welcome to developing AI literacy for academic practice. Let’s begin.