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Augmented Academia Workbook 2
This workbook is your practical companion during the workshop and a handy reference for later use.
You can work directly in Copilot or other AI tools while following along.
This workbook includes accessibility features and ARIA labels for an inclusive learning experience.
🧠 What is GenAI?
Generative Artificial Intelligence (GenAI) is the technology behind tools like ChatGPT, Claude, and Gemini. These tools can create new content based on your instructions.
To put it simply, GenAI is 🧠✨ Your Intelligent Partner in Progress
GenAI is not just a tool, it's a creative co-author, a strategic collaborator, and a thinking partner that enhances your professional capabilities.
Like a productivity assistant, it handles routine tasks, drafts content, and sparks new ideas, freeing you to focus on insight and impact.
It serves as an idea navigator, helping you explore unfamiliar territory, uncover patterns, and generate fresh perspectives.
And as a cognitive toolkit, it equips you with flexible, adaptive capabilities, from writing and analysis to design and coding, tailored to your goals.
Rather than replacing expertise, GenAI amplifies it, offering a dynamic partnership that evolves with your needs and empowers your work.
University of Glasgow GenAI Policy – Quick Guide
The University of Glasgow supports responsible and transparent use of Generative AI in teaching, learning, and research. Below is a summary of the key points from the institutional policy:
✅ Be Transparent: Always state when you have used GenAI in creating teaching materials or research outputs.
✅ Check Accuracy: Review AI outputs for correctness and alignment with current academic standards.
✅ Protect Privacy: Do not enter identifiable student or staff data into public AI tools.
✅ Keep Control: Use AI to support – not replace – your academic judgment.
✅ Use Approved Tools: Prefer University-provided or contracted AI tools to ensure security.
❌ No Plagiarism: Do not use AI to copy from sources without proper citation.
A prompt is the instructions, questions, or requests you give to an AI system.
Understanding how to write prompts is essential for getting reliable, relevant, and efficient results from any GenAI tool.
The prompt-output relationship
Like in everyday situations, AI responds best when you:
Are specific about what you want
Provide context and background
Explain your role and the audience
Specify the format you need
Key differences between vague and clear prompts
This table compares vague prompts with clear prompts and explains why clear prompts work better
Vague Prompt
Clear Prompt
Why Clear Works Better
"Explain photosynthesis"
"Explain photosynthesis for second-year biology students, focusing on the light reactions, using analogies and including a simple diagram description"
Specifies audience, focus, teaching method, and format
"Make this better"
"Improve this slide by reducing text by 70%, adding visual elements for enzyme kinetics, and including an interactive poll question"
Defines specific improvements and measurable outcomes
"Help with my lecture"
"Create a 45-minute lecture outline on DNA replication for first-year students, including learning objectives, activities, and assessment questions"
Clear scope, audience, time frame, and deliverables
Prompt frameworks
Frameworks are structured ways to write prompts that help you get the best results.
Here are the 5 effective frameworks:
This table describes five prompt frameworks including their structure, biology examples, and best use cases
Framework
Structure
Biology Example
Best For
P-T-C-F (Persona-Task-Context-Format)
Persona: Your role Task: What to do Context: Background info Format: Output style
Persona: Cell biology expert Task: Explain mitosis Context: 2nd year students, 50 minute lecture Format: Outline with activities
Content creation, lesson planning
Chain-of-Thought
Ask AI to think step-by-step through complex processes
"Explain photosynthesis step-by-step: 1) Light capture, 2) Water splitting, 3) ATP/NADPH formation, 4) Carbon fixation, showing how each connects"
Complex biological processes, problem-solving
Few-Shot Learning
Provide 2 to 3 examples of your style, then ask for new content
"Here's how I explain enzymes and here's how I explain metabolism. Now explain DNA replication in the same style"
Maintaining consistency across content
Role-Based
AI adopts specific expert persona with defined background
"Act as a molecular biologist with 15 years research experience who teaches undergraduates"
Authoritative content, research connections
CLEAR (Context-Length-Examples-Audience-Role)
C: Background context L: Desired length E: Examples included A: Target audience R: Your role
Context: Teaching genetics Length: 30 minute segment Examples: Include Mendel's peas Audience: First year students Role: Biology lecturer
Comprehensive lesson planning
♿ Accessibility & Inclusive Design in Biology Education
Overview & University Policies
Creating accessible educational content ensures all students can engage with biology learning materials effectively. The University of Glasgow is committed to inclusive education that meets diverse learning needs.
Legal Requirement: Under the Equality Act 2010, educational institutions must make reasonable adjustments to ensure students with disabilities can access learning materials and participate fully in education.
University of Glasgow Accessibility Standards
✅ All digital content must meet WCAG 2.1 AA standards
✅ Alternative formats available within 5 working days of request
✅ Captions required for all video content
✅ Text alternatives for all images and graphics
✅ Keyboard navigation support for interactive elements
WCAG Guidelines Integration with AI Tools
When using AI to create educational content, ensure outputs meet Web Content Accessibility Guidelines (WCAG) standards.
AI Prompting for Accessible Content Creation
Accessibility-First Content Prompt:
Create [biology content] that meets WCAG 2.1 AA accessibility standards:
VISUAL ACCESSIBILITY:
- Use high contrast colors (minimum 4.5:1 ratio for normal text)
- Provide descriptive alt text for all images and diagrams
- Use clear, readable fonts (minimum 12pt, sans-serif recommended)
- Ensure content is readable when magnified to 200%
COGNITIVE ACCESSIBILITY:
- Use clear, simple language appropriate for [student level]
- Break complex information into digestible chunks
- Provide definitions for technical terms
- Include summary sections and clear headings
MOTOR ACCESSIBILITY:
- Ensure all interactive elements are keyboard accessible
- Provide adequate click/touch targets (minimum 44px)
- Include focus indicators for navigation
- Avoid content that requires precise timing or coordination
SENSORY ACCESSIBILITY:
- Provide text alternatives for audio content
- Include captions for video materials
- Don't rely solely on color to convey information
- Offer multiple ways to access the same information
Include specific suggestions for accommodating students with:
- Visual impairments (screen readers, magnification)
- Hearing impairments (captions, transcripts)
- Cognitive differences (clear structure, simple language)
- Motor impairments (keyboard navigation, alternative inputs)
Content Auditing with AI
Use AI to systematically review existing content for accessibility barriers.
Accessibility Audit Prompt:
Review this biology education content for accessibility barriers:
[Insert your content here]
Analyze for:
VISUAL BARRIERS:
- Images without alt text or with insufficient descriptions
- Poor color contrast combinations
- Text embedded in images that screen readers cannot access
- Overly complex visual layouts
COGNITIVE BARRIERS:
- Dense paragraphs without clear structure
- Technical language without definitions
- Missing headings or poor heading hierarchy
- Information overload without clear priorities
NAVIGATION BARRIERS:
- Links without descriptive text ("click here" vs "download photosynthesis diagram")
- Missing skip navigation options
- Unclear page structure
- Non-intuitive information flow
INTERACTION BARRIERS:
- Elements that require mouse-only interaction
- Time-limited activities without extensions
- Complex drag-and-drop without alternatives
- Missing feedback for user actions
Provide specific recommendations for improvement with examples of better alternatives.
Assistive Technology Integration
Ensure your AI-generated content works seamlessly with assistive technologies.
Screen Reader Compatibility
Content Type
Accessibility Requirement
AI Prompt Addition
Images/Diagrams
Descriptive alt text
"Include detailed alt text describing both content and function"
Tables
Clear headers and structure
"Create tables with proper headers and caption descriptions"
Interactive Elements
Keyboard navigation
"Ensure all interactions work with keyboard-only navigation"
Documents
Proper heading structure
"Use hierarchical headings (H1, H2, H3) for clear document structure"
Practice Exercises
Exercise: Accessible content creation
Challenge: Create an accessible version of your old lab protocol or lecture notes.
Your task: Write a prompt to make your protocol accessible.
Key outcome: Learn complex AI prompting techniques to create comprehensive, accurate, and engaging biology educational content that meets pedagogical standards and learning objectives.
Understanding the three core advanced prompting frameworks
Framework 1: Chain-of-Thought (CoT) prompting
What it is: A technique where you ask AI to show its reasoning process step-by-step, leading to more accurate and comprehensive responses.
Why it works in life sciences: Complex biological processes involve multiple interconnected steps that students often struggle to connect coherently. CoT mirrors how expert biology teachers explain concepts by building understanding progressively.
Basic Structure:
Explain [biological concept] using step-by-step logical reasoning:
1. Start with [foundational principle]
2. Connect to [intermediate concept]
3. Build to [complex application]
4. Conclude with [real-world relevance]
For each step, explain WHY this process evolved this way and HOW it connects to the next step.
Hands-on practice scenario: Cell biology lecture creation
Your challenge: Create comprehensive content for a 90 minute lecture on "Protein Synthesis: From Gene to Function" for second year biology undergraduates.
Learning objectives:
Explain the molecular mechanisms of transcription and translation
Analyse the relationship between protein structure and function
Evaluate the impact of mutations on protein synthesis
Connect protein synthesis to current biotechnology applications
Exercise: Chain-of-Thought for protein synthesis
Standard prompt (ineffective):
Explain protein synthesis for undergraduate biology students.
Your task: Rewrite this using Chain-of-Thought prompting
Template to complete:
Explain protein synthesis using step-by-step logical reasoning for second year biology undergraduates:
1. Foundation: [What should students understand first?]
2. Transcription mechanics: [How does the process begin?]
3. RNA processing: [What happens next and why?]
4. Translation setup: [How do the components come together?]
5. Translation process: [What are the key steps?]
6. Post-translation: [How does the process conclude?]
7. Regulation: [How is this controlled?]
8. Real-world applications: [Why does this matter today?]
For each step, explain WHY this process evolved this way and HOW it connects to the next step. Include common student misconceptions and address them directly.
Practice space:
View solution example
Explain protein synthesis using step-by-step logical reasoning for second year biology undergraduates:
1. Foundation: Start with the central dogma and why cells need to make proteins - cells must convert genetic information into functional molecules to survive and respond to environment
2. Transcription mechanics: Detail how RNA polymerase reads DNA template to create mRNA - this separation allows regulation and protects DNA
3. RNA processing: Connect how mRNA is modified (5' cap, 3' poly-A tail, splicing) and why this matters for stability and nuclear export
4. Translation setup: Show how ribosomes, tRNAs, and mRNA come together - this complexity allows for regulation and accuracy
5. Translation process: Walk through initiation, elongation, and termination - each step has quality control mechanisms
6. Post-translation: Explain protein folding, modifications, and targeting - this determines final protein function
7. Regulation: Connect how cells control protein synthesis at multiple levels - this allows cellular responses to changing conditions
8. Real-world applications: Link to genetic engineering, gene therapy, and drug development - show how understanding leads to medical breakthroughs
For each step, explain WHY this process evolved this way and HOW it connects to the next step. Address common misconceptions like 'DNA directly makes proteins' or 'all genes are always active'.
Framework 4: Inverted‑Pyramid / 5 W’s
What it is: A news-style structure that puts the most important information first, guided by the 5 W’s (Who, What, When, Where, Why) plus How.
Why it works in education: Helps students produce clear, accurate, and concise summaries of research and lab outputs; ideal for press releases, module announcements, and assessment briefs.
Structure:
1) Lead: Who + What + When + Where + Why/How (core finding/significance)
2) Key Details: methods, scope, one quantitative result
3) Context: prior work, limitations, uncertainties
4) Next Steps: implications, future work, calls to action
Prompt Template (copy‑ready):
Write a 40–60 word inverted‑pyramid lead using the 5 W’s for this study:
[Paste key facts or abstract]
Constraints:
- First sentence must contain: Who, What, When, Where, Why/How
- Second sentence: one quantitative detail and a limitation or uncertainty
- Use plain language suitable for second year undergraduates
Short Practice:
Task A: Convert this abstract to a 2‑sentence lead (≤70 words).
Task B: Expand to a 150‑word news brief that keeps the original lead intact.
[Paste abstract]
Framework 2: Few-Shot Learning
What it is: A method where you provide a few examples of the desired output style, and AI learns to replicate that style in new content.
Why it works in education: Ensures consistency and adherence to specific communication standards, especially useful in collaborative teaching environments.
Example (two‑sentence lead):
University of Glasgow researchers reported on 26 Oct 2025 at the Wolfson Wohl Cancer Research Centre that a new multi‑omic blood test detects early pancreatic cancer, potentially enabling earlier treatment. In a 1,200‑patient multicentre trial, the assay achieved 92% sensitivity and 88% specificity, but requires external validation before clinical adoption.
🎨 Content Creation with GenAI
Workflow overview
Key outcome: Acquire skills to effectively use GenAI for creating engaging and educationally valuable biology content, including presentations, quizzes, and interactive materials.
Task: Transform an existing slide deck on mitosis into an interactive learning module using GenAI.
Objectives:
Enhance content interactivity and engagement
Apply best practices in accessible and inclusive design
Utilize AI tools for content adaptation and enhancement
Before you begin
Ensure you have the original slide deck on mitosis and access to GenAI tools. Review the University of Glasgow GenAI policy and accessibility standards.
Prompting for slide transformation
Transformation Prompt:
Revise the following slide content to create an interactive learning experience:
SLIDE CONTENT:
[Paste your slide content here]
TRANSFORMATION GUIDELINES:
- Convert text-heavy slides to concise bullet points
- Add interactive elements: questions, polls, or discussions
- Suggest relevant images, diagrams, or videos
- Ensure accessibility: alt text for images, captions for videos
- Provide a summary slide with key takeaways
OUTPUT FORMAT:
- Slide 1: [Revised content]
- Slide 2: [Interactive element suggestion]
- Slide 3: [Image/diagram description and alt text]
- Slide 4: [Video suggestion and captioning guidance]
- Slide 5: [Summary of key takeaways]
Practice scenario: Lab presentation on cellular respiration
Your task: Create a 10-slide PowerPoint presentation on cellular respiration for a first-year biology class.
Use the following resources:
Lecture notes on cellular respiration
GenAI tools for slide design and content generation
University of Glasgow branding guidelines
Learning objectives:
Understand the key stages of cellular respiration
Identify the inputs and outputs of each stage
Explain the role of cellular respiration in metabolism
Assessment: Submit your presentation for feedback on content, design, and accessibility.
📚 References — Prompt Frameworks
Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. NeurIPS 2020. Link
Wei, J., Xia, C., Kozareva, Z., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022. Link
Wang, X., Wei, J., Schuurmans, D., et al. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ICLR 2023. Link
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. NeurIPS 2022. Link
Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training Language Models to Follow Instructions with Human Feedback. NeurIPS 2022. Link
Sanh, V., Webson, A., Raffel, C., et al. (2022). Multitask Prompted Training Enables Zero-Shot Generalization. ICLR 2022. Link
Liu, P., Yuan, W., Fu, J., et al. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in NLP. ACM Computing Surveys, 55(9), 1–35. DOI
Pöttker, H. (2003). News and Its Communicative Quality: The Inverted Pyramid—When and Why Did It Appear? Journalism Studies, 4(4), 501–511. DOI