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Transforming learning and organisations: leveraging the power of AI

MODULE 1. TECHNICAL SKILLS AND DATA LITERACY

Lesson 1. Introduction to AI

Lesson 2. Data Literacy and AI Content Assessment

Lesson 3. AI Tools and Technologies

Lesson 4. AI for Accessing and Organising Information

Lesson 5. Self-assessment quiz

Module 1. Self-assessment quiz

MODULE 2. PEDAGOGICAL APPLICATIONS

Lesson 1. AI in adult learning: purpose, potential and pedagogical value

Lesson 2. Using AI to support learning, teaching and inclusion

Lesson 3. From ideas to practice: Piloting AI in adult education

Lesson 4. Responsible AI in pedagogy: ethics, inclusion and human judgement

Lesson 5. Self-assessment quiz

Module 2. Self-assessment quiz

MODULE 3. AI FOR ORGANISATIONAL PROCESSES

Lesson 1. Understanding AI in Organisational Context

Lesson 2. Process Automation and Workflow Optimisation

Lesson 3. Data Integration and Management

Lesson 4. Predictive Analytics and Decision Support

Lesson 5. Digital Transformation Strategy

Lesson 6. Implementation Planning and Best Practices

Lesson 7. Self-assessment quiz

Module 3. Self-assessment quiz

MODULE 4. ETHICAL CONSIDERATIONS AND SAFETY

Lesson 1. Ethics and safety in AI-supported education

Lesson 2. Bias, fairness and inclusion

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Module 1. Self-assessment quiz

Transforming learning and organisations: leveraging the power of AI
Lesson 5. Self-assessment quiz
Module 1. Self-assessment quiz

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Question 1 of 15

1. Question

AI systems can learn from data and recognize patterns.

True
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False
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Question 2 of 15

2. Question

Who introduced the idea of testing machine intelligence?

Bill Gates
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Alan Turing
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Elon Musk
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Steve Jobs
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Question 3 of 15

3. Question

What technology drove major AI progress in the 2010s?

Basic programming
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Manual calculations
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Deep learning
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Question 4 of 15

4. Question

Which of the following are types of machine learning?

Supervised learning
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Unsupervised learning
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Reinforcement learning
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Manual programming
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Question 5 of 15

5. Question

What does the phrase “garbage in, garbage out” mean in AI?

AI deletes bad data automatically
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Poor-quality data leads to poor outputs
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AI improves bad data over time
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Data has no effect on results
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Question 6 of 15

6. Question

Why do AI systems require large datasets during training?

To store memory like humans
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To avoid using computing power
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To reduce algorithm complexity
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To learn patterns through examples
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Question 7 of 15

7. Question

Which type of data is most structured?

Emails
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Videos
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Spreadsheet tables
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Audio recordings
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Question 8 of 15

8. Question

What is the main purpose of data preprocessing?

To increase hardware speed
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To prepare data for AI systems to use effectively
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To delete all raw data
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To replace AI models
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Question 9 of 15

9. Question

Why should educators compare AI outputs with original source data?

To verify accuracy and detect missing or distorted information
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To increase computation speed
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To train new AI models
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To delete old datasets
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Question 10 of 15

10. Question

All AI tools are equally effective for every task.

True
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False
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Question 11 of 15

11. Question

What is the main purpose of crafting good prompts?

To confuse the AI
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To guide the AI toward accurate and relevant outputs
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To reduce data size
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To eliminate algorithms
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Question 12 of 15

12. Question

Which tasks can AI automate?

Transcribing meetings
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Formatting documents
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Generating reports
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Question 13 of 15

13. Question

Which are examples of search assistants?

Perplexity AI
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Bing Chat
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Google Bard
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Word processor
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Question 14 of 15

14. Question

Which tools are considered AI summarizers?

ChatGPT
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QuillBot
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Canva
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Perplexity AI
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Question 15 of 15

15. Question

Which statements about AI summaries are correct?

They may omit important details
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They are always fully accurate
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They can oversimplify content
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They should replace original sources completely
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