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Free Download AI-Driven Design & Innovation in Learning (2026)
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 55m | Size: 223.98 MB
Use AI across learning analysis, experience design, personalisation, content and assessment - with human oversight.
What you'll learn
Explain the five practical AI capabilities in L&D: generate, transform, retrieve, analyse and automate.
Identify where AI belongs in learning work, and where the consequence of a wrong output makes it inappropriate.
Understand why performance goals, not content requests, should start every AI-assisted learning project.
Build an objective-first brief and an approved source pack before you generate a single word.
Recognise the omission blind spot and review AI output for what is missing, not only what is wrong.
Personalise pathways, examples, pace, feedback and support using evidence rather than assumptions about groups.
Know how to scale human oversight with consequence, and who owns accuracy, quality and escalation.
Prepare a bounded AI pilot with a baseline, defined measures, stop conditions and a real decision gate.
Discuss impact using seven signals: learning, performance, engagement, quality, efficiency, fairness and trust.
Understand how to scale AI practice through standards, reusable workflows, governance and continuous review.
Requirements
No coding, data science or AI engineering background is required.
No specific AI tool, platform or subscription is needed - the course is deliberately tool-neutral.
No prior experience with AI is assumed.
Some familiarity with training, learning design or L&D work is helpful but not essential.
A current or upcoming learning project to think about as you watch is useful, but optional.
Description
AI changed one thing almost overnight: producing learning content became easy. An outline, a lesson draft, twenty quiz items, three scenario variations, a regional version - minutes of work. The hard part never moved. A beautifully written course can still be unnecessary. A personalised pathway can still rest on the wrong assumption. An AI-generated assessment can still measure the wrong thing. And a faster workflow can still change nothing about how people perform.
This course is about closing that gap. It is a 60-minute, theory-based, tool-neutral course for the people who design learning and are now expected to have an answer about AI. Rather than comparing models or chasing whichever product is popular this month, it follows one organisation - Northwind Group, a fictional logistics and warehousing company with 6,400 employees across three countries and a nine-person L&D team - through every stage: too many requests, a growing catalogue, managers asking for training when the real problem is elsewhere, and leadership asking whether any of it is changing anything.
You do not need a technical background. No coding, no data science, no AI tool subscription, no prior AI experience. If you can describe a learning need and review a draft, you can do this course.
Throughout the course you will learn
• The five practical AI capabilities in L&D - generate, transform, retrieve, analyse, automate - and the different level of control each one needs
• The Source Test: how far the model is operating from approved truth, and how much review that distance demands
• The Monday Morning Test and the Five-Person Check for separating a real capability gap from a process, tool, incentive or environment problem
• How to design around moments of need - before, during and after work - instead of adding more modules
• Why the rubric comes before the AI feedback, and what vague AI coaching looks like when it doesn't
• The Evidence Test for personalisation: adapt on what the learner demonstrated, never on assumptions about their group, shift, role or age
• Why a diagnostic score is a snapshot, not a permanent label, and how to keep adaptation visible, explainable and overridable
• Source packs and the objective-first brief - learner, action, evidence, constraints - that prevent most bad AI output
• The omission blind spot: why "what is missing?" is a better review question than "is this correct?"
• Privacy, copyright and fairness in practice, including the Consequence Rule for scaling human oversight to risk
• The One-Slice Rule for bounded pilots, with baselines, stop conditions and a real decision gate: scale, fix, or stop
• The seven signals of impact - learning, performance, engagement, quality, efficiency, fairness, trust - and why efficiency measured on drafting alone is false efficiency
By the end, you will be able to look at any learning workflow and say clearly where AI adds value, where it creates risk, what the human still owns, and how you would know whether the result is genuinely better. You will be able to map an AI-assisted workflow with approved inputs, review points and escalation; evaluate output for accuracy, completeness, fairness, privacy and policy fit; and scope a bounded pilot with measures your leadership will accept.
The principle that runs through all ten sections: use AI to improve the quality and speed of professional learning work - not to bypass evidence, judgement, or human accountability.
Who this course is for
Learning designers and learning experience designers
Instructional designers
Corporate trainers and facilitators
L&D managers and heads of learning
Curriculum developers
Learning technology and LMS professionals
Teachers, lecturers and educators exploring AI
Subject-matter experts who review learning content
HR and talent development professionals
Compliance and safety training leads
Consultants advising organisations on AI in learning
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