The ‘Development of AI-based Virtual Tutors’ course puts you in the at the forefront of digital education, a booming sector with high demand for workers. With the unstoppable advance of artificial intelligence, virtual tutors have become a a key tool for personalising learning and enhancing the educational experience. This course will enable you to master the fundamentals of smart tutoring in digital environments, ranging from the distinction between conversational assistants and educational tutors, to the personalisation of learning through AI. You will learn how to design effective virtual tutors, integrating language models and learning platforms, whilst you evaluate and improve your virtual tutor with a critical and ethical perspective. This approach prepares you to tackle the Current challenges in online education, ensuring that you purchase essential skills for innovation and leadership in the field of digital education.
Development of AI-based Virtual Tutors
AI Academy
3 pm
Introduction
Objectives
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Understanding the fundamentals of the smart tutoring.
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Analyse differences between conversational assistants and educational tutors.
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Designing virtual tutors for personalise learning.
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Selecting and training language models for tutoring.
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Integrating tutorial systems in learning platforms.
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Assessing pedagogical effectiveness with accurate indicators.
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Apply ethics y data protection regulations in AI.
Table of Contents
TEACHING UNIT 1. MASTER THE BASICS OF SMART TUTORING IN DIGITAL ENVIRONMENTS
1. What are virtual tutors?
2. Differences between conversational assistants and educational tutors
3. Models of educational interaction in virtual environments
4. Personalising learning through artificial intelligence
5. Simulated cognitive functions in tutorial systems
6. Principles of instructional design in tutoring systems
7. The role of the smart tutor in online education
8. Current limitations of AI-based tutoring
TEACHING UNIT 2: LEARN HOW TO DESIGN EFFECTIVE VIRTUAL TUTORS
1. Functional architecture of an intelligent tutorial system
2. Selection and training of language models
3. Integration with LMSs and learning platforms
4. Designing conversation flows geared towards learning
5. Techniques for monitoring and assessing pupils’ progress
6. Tailoring content to individual profiles and learning styles
7. Implementation of smart feedback modules
TEACHING UNIT 3. EVALUATE AND IMPROVE YOUR VIRTUAL TUTOR USING CRITICAL AND ETHICAL JUDGEMENT
1. Performance indicators in tutorial systems
2. Methods for assessing pedagogical effectiveness
3. Collection and analysis of usage data
4. Strategies for continuous improvement in online tutoring
5. Teaching supervision in automated contexts
6. Risks of dependency, misinformation or over-automation
7. Data protection and privacy regulations
8. Ethics in the use of artificial intelligence in education