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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 3. Self-assessment quiz

Transforming learning and organisations: leveraging the power of AI
Lesson 7. Self-assessment quiz
Module 3. Self-assessment quiz

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Question 1 of 15

1. Question

According to the course, AI in adult education is best understood as: (Module 1)

Artificial Intelligence designed to replace educators
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Augmented Intelligence that supports the work of educators
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Automated robotic systems for classroom management
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Advanced computer hardware for digital learning
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Question 2 of 15

2. Question

Which of the following is an example of rule-based automation in adult education? (Module 1)

Predicting which learners may drop out in the coming weeks
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Recommending personalised learning paths based on past activity
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Sending an automatic reminder email to learners absent from two consecutive sessions
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Drafting original learning materials from scratch
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Question 3 of 15

3. Question

What is the recommended first step when starting an AI initiative in your organisation? (Module 1)

Buy the most advanced AI tool currently on the market
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Assess your starting point and identify pain points
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Replace all existing systems with new AI platforms
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Wait until your data is perfectly clean before doing anything
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Question 4 of 15

4. Question

In the automation opportunity matrix, what characterises a ‘quick win’? (Module 2)

High implementation effort and low impact
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Easy implementation and high impact
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High implementation effort and high impact
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Easy implementation and low impact
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Question 5 of 15

5. Question

Which type of automation uses simple ‘if-this-then-that’ logic? (Module 2)

AI-powered automation
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Predictive analytics
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Rule-based automation
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Hybrid automation
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Question 6 of 15

6. Question

Which of these is the LEAST suitable starting point for automation in an adult education organisation? (Module 2)

Sending course reminders to learners
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Generating certificates after course completion
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Making strategic decisions about senior staff hiring
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Processing standard enrolment forms
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Question 7 of 15

7. Question

What does the acronym ETL stand for in data management? (Module 3)

Extract, Transform, Load
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Edit, Test, Launch
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Evaluate, Transfer, Log
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Execute, Train, Learn
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Question 8 of 15

8. Question

Which regulation is most relevant for protecting learner data in the European Union? (Module 3)

FERPA
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GDPR
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HIPAA
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ISO 9001
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Question 9 of 15

9. Question

What is a ‘data silo’? (Module 3)

A secure cloud backup facility
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Information trapped in separate platforms with no connection between them
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A type of advanced machine learning model
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A backup of personal learner records
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Question 10 of 15

10. Question

What is the main difference between descriptive and predictive analytics? (Module 4)

Descriptive predicts the future; predictive describes the past
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Descriptive tells you what already happened; predictive estimates what may happen next
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There is no real difference between them
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Predictive analytics does not use historical data
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Question 11 of 15

11. Question

Which of these is a common pitfall to avoid when using predictive analytics? (Module 4)

Combining model output with professional judgement
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Testing the model regularly for bias
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Treating model output as the final answer with no human review
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Setting up clear data governance from the start
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Question 12 of 15

12. Question

What is the key difference between digitisation and digital transformation? (Module 5)

Digitisation converts existing processes into digital form; transformation rethinks how the organisation works
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They are two terms for the same thing
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Digital transformation only applies to small organisations
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Digitisation is more expensive than digital transformation
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Question 13 of 15

13. Question

What share of the transformation budget should ideally be invested in change management for higher success rates? (Module 5)

Less than 1%
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Around 5%
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At least 20%
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Change management is not really necessary
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Question 14 of 15

14. Question

According to the seven pillars framework, what should drive the choice of technology? (Module 6)

The newest features available on the market
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The recommendations of technology vendors
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The needs of the organisation and its learners
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The lowest possible cost, regardless of fit
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Question 15 of 15

15. Question

What is the recommended typical duration of a pilot programme? (Module 6)

About one week
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Three to six months
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At least five years
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No time limit should be set
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