Academy

Context

In its broadest sense, artificial Intelligence (AI) is the ability or attempt of an artificial system to simulate human intelligence through the optimisation of mathematical functions. In short, AI is the future of information technology and it offers significant potential in terms of training development for new and interactive training experiences.
This course aims to equip future creators of AI solutions in the domain of training development with a complete set of tools to create advanced and personalised AI experiences using mainstream, market-available platforms. Participants will be brought up to speed to keep pace with the evolution of AI and thus transform training development in their industries through autonomous application of AI tools.
The course focusses on enabling participants to use mainstream AI platforms autonomously. Participants will learn to create Custom GPTs and configure AI agents using widely-available tools. All examples and exercises utilise market-standard platforms.

Training items & course structure

Day 1: Foundations & analyse phase

Theoretical foundations

• History of generative AI: exploring models, natural language processing (NLP), generative pre-trained transformers (GPT), and AI Gen.
• Introduction to mainstream AI platforms (OpenAI, Anthropic Claude) for training development.
• Privacy & AI ACT: regulatory and ethical implications.

ADDIE focus – Analyse phase

• Using AI to extract training needs from regulatory requirements, official documents (e.g., operational suitability data – OSD), and internal feedback.

Practical application

• Import/export: techniques for importing and exporting documents into AI platforms.
• Training content creation: initial setup and document management.
• Using AI experts: introduction to configuring domain-specific AI training assistants.

Day 2: Prompt engineering & define phase

Theoretical foundations

• Prompt engineering: foundations to create effective prompts for training applications.
• NLP vs. generative AI: differences and applications in training development.

ADDIE focus – Define phase

• Leveraging AI to generate course structures, syllabi, and training programmes.

Practical application

• Welcome message: creating personalised AI agent introductions.
• Contexts & menus: implementing initial questions and personalised interaction flows.
• Training content creation: management and customisation of AI agent responses.

Day 3: Advanced configuration & develop phase

Theoretical foundations

• Deep thinking: exploring advanced reasoning capabilities for complex training scenarios.
• Custom GPTs and AI assistants: creating personalised AI agents without custom coding.

ADDIE focus – Develop phase

• Creating training materials with AI assistance (presentations, lesson plans, support documents).

Practical application

• Advanced features: implementing sophisticated training support functions.
• Aesthetics & sharing: customisation and deployment options for AI assistants.
• Using AI experts: advanced configuration of domain-specific AI training assistants.

Day 4: interactive learning & implement phase

Theoretical foundations

• AI as interactive tutors and learning companions.
• Real-time feedback mechanisms and adaptive learning support.

ADDIE focus – Implement phase

• Deploying AI agents as training assistants and interactive learning support tools.

Practical application

• Configuring AI agents for learner interaction and quizzing.
• Implementing AI-based learning support functions.
• Testing and refining deployed AI training assistants.

Day 5: Evaluation, integration & project finalisation

Theoretical foundations

• VR & open badges: overview of integration with virtual reality and certification systems.
• Future trends and continuous improvement with AI.

ADDIE focus – Evaluate phase

• Utilising AI to analyse participant feedback and assess training effectiveness.

Practical application

• Creating AI-powered feedback analysis tools.
• Project finalisation: completion and refinement of created AI training solutions.
• Integration strategies for ongoing autonomous use.

Training objectives

• Understanding AI fundamentals and their application to training development.
• Enabling participants to implement AI solutions autonomously in training development.
• Creating AI agents and applying them in real training contexts across the full ADDIE lifecycle.
• Developing independence in using mainstream AI tools for training purposes.

Administration

• Duration: 5 days (30 hours).
• Certificate: All participants will receive a certificate of attendance.

Perspective participants
Personnel involved in the instructional systems design of training organisations.

Prerequisites
No specific prior knowledge is required to attend the training, other than English language and basic computer knowledge.