AI for L&D (ChatGPT and MORE!)

To get the most out of this course, you’ll need a few essentials:

• A computer with a reliable internet connection (obvs).

• Access to ChatGPT Plus (or a similar large language model like Claude or Copilot).

• Slack, which we’ll use to access the course community and collaborate with others. You can find it at edifai-commuity.slack.com

• And most importantly, an open mind. AI is all about exploration and experimentation!

ADDIE is an instructional design framework that is the backbone of this course. Throughout the next five modules, we will explore how AI can help you:

• Analyze: Crunch data sets, identify patterns, and even help you write surveys or questionnaires.

• Design: Conduct subject matter research, create lesson plan drafts, and help you brainstorm ideas.

• Develop: Generate text, visuals, and even interactive elements for your learning materials.

• Implement: chatbots, translate materials, and provide automated support for learners.

• Evaluate: Build rubrics, analyze feedback, and create data visualizations to measure your program’s impact.

It's crucial you understand this framework, as it will help you as we progress through the program.

Here's three quick tips to help you make the most of our time together!

During this programme, you can choose one of two options:

1. Work on a real life L&D project of your choosing

2. Choose from one of the three fictional briefs we are providing.

If you choose to go with the latter, you then have three options:

1. A half-day leadership training program on dealing with conflict (and Non-Violent Communications (NVC))

2. A six week blended program on improving psychological safety for new managers

3. A series of eLearning courses on improving sustainability within procurement

If you choose to work with one of our fictional scenarios, please download the project briefs and familiarise yourself with what is required.

At the end of each module, we'll ask a few questions to test you've been paying attention to ;)

Let's get started with a few prep questions!

Before we dive into using AI for Analysis, let's take a quick look at what we will be covering in this module.

Throughout this course, we will be using a simple framework that will help you ensure you are always writing good prompts. We call it CREATE:

• C – Character: Define the AI’s role or perspective. Who do you want it to be?

• R – Request: Be clear and specific about what you need.

• E – Example: Provide examples to help guide the AI’s response.

• A – Audience: Tailor the output to your audience—what tone or style should it have?

• T – Type: Specify the format or structure of the output, like a table, a list, or a narrative.

• E – Extras: Add any additional instructions, like word limits or asking the AI to clarify before answering.

Now let's put this into practice by using the CREATE framework to build a Training Needs Analysis is seconds. Below is the prompt we use (also in your prompt handbook):

You are an HR manager for a large bank.

I’d like you to create a short survey that helps us to better understand what members of our finance team are keen to receive education on

Firstly I’d like you to write 5x key statements questions, about working in finance, using a likert scale. Then include three prompting qualitative questions about what they would like for future development.

The people completing this survey all work in finance related roles.

Present the questions in bullet points, with multiple-choice options for the quantitative ones.

Ask me any further questions for clarification before you create this.

Now it's your turn to give this a go. If you are working on your own project, please remember to not upload anything confidential into ChatGPT.

If you are working with one of our fictional briefs, you can download the sample datasets below. Make sure to open them and familiarise yourself with the data, before uploading it.

Here's the full prompt that we use in the video:

You are a data analyst for an L&D team

Analyse the attached qualitative responses from a training needs survey

Identify the top five recurring themes, provide a short explanation for each, and suggest one actionable recommendation based on the themes.

I will be reviewing this before presenting it to my manager

Present your findings in bullet point format.

Ignore any outlier responses.

A short case study on how Cornerstone are using Generative AI to reinvent the way they analyze data and personalise learner journeys.

Let's recap everything that we have covered in module 1, and take a moment to reflect on the following question:

"How can you use data to make more informed decisions as an L&D leader?"

Remember to share your reflection in our Slack community, and hear from others.

Before we dive into using AI for Design work, let's take a quick look at what we will be covering in this module.

In this section, we’re going to focus on conducting subject matter research—quickly, effectively, and without the hours of scrolling. If you’ve ever designed a workshop, you know how hard it can be to find the right, credible information.

Enter Perplexity.ai

Some other research tools we love include:

• YouTube Summary: A Chrome plugin that quickly transcribes any video on YouTube

• Scholarcy: An academic research tool that summarizes long documents into digestible chunks

• Elicit: An AI-powered tool for academic research that summarises papers, identifies themes, and suggests related studies.

• Research Rabbit: Helps you find related research like a playlist—start with a few papers, and it suggests relevant works to build a web of useful content.

• Consensus: An AI search engine that pulls answers directly from academic research, giving you evidence-based results.

• QuillBot: A tool for paraphrasing and refining content quickly without losing clarity.

• Jenni.ai: Assists in drafting content by weaving in research and aligning the output with your tone and style.

• Zotero (with AI Plugins): An excellent research management tool enhanced with AI for organising, summarising, and citing papers.

In this lesson we introduce Blooms Taxonomy for helping you to structure learning outcomes in a meaningful way.

Bloom’s gives us a structured way to think about learning objectives, breaking them into levels of complexity—starting with basic understanding and moving up to higher-order skills. It uses verbs like:

• Understand: Explain or describe concepts.

• Apply: Use knowledge in real-world situations.

• Analyse: Break down ideas into components.

• Evaluate: Make judgements based on criteria.

• Create: Build or design something new.

Let's bring everything we've learned together, and use Perplexity.ai to help us create session outcomes for the project we are working on.

Here's the full prompt we use in the video:

You are an experienced instructional designer. Using Bloom’s Taxonomy, write five session outcomes for a Time Management for Remote Working program. Include a mix of verbs from Bloom’s levels of understanding, applying, and analysing. Structure each outcome as a clear sentence starting with the verb, and ensure they are measurable and learner-focused.

Let's recap everything that we have covered in module 2, and take a moment to reflect on the following question:

"How will tools like Perplexity change the way you work with SME’s?"

Remember to share your reflection in our Slack community, and hear from others.

Let's quickly test your knowledge on what we covered in Module 2

Before we dive into using AI for Development work, let's take a quick look at what we will be covering in this module.

Let's go back to the CREATE framework and ChatGPT, but this time use them to write a video script for the learning you are building.

Below is the full prompt we use in the video (also in your prompt handbook)

• Character: You are an experienced instructional designer creating a 3-5 minute training video script for a Time Management for Remote Working course. You are a helpful, knowledgeable guide.

• Request: Write a script that introduces the importance of time management, explains the Eisenhower Matrix as a prioritisation tool, and ends with a reflective question.

• Example: Include a scenario where a remote worker uses the Eisenhower Matrix to organise their tasks effectively.

• Audience: Remote workers balancing competing priorities. Keep the language clear, relatable, and practical.

• Type: A video script with clear sections—intro, explanation, example, and reflective wrap-up.

• Extras: Include cues for visuals or pauses where necessary.

Let's learn another useful framework for using AI to generate images. We call in the Prompt Hamburger (which you can download as a lesson resource.

Below is the prompt we use in the video:

A clean, professional digital illustration of a remote worker sitting at a tidy desk with a laptop open. The laptop screen displays the Eisenhower Matrix labeled ‘Do First,’ ‘Schedule,’ ‘Delegate,’ and ‘Don’t Do.’ Sticky notes and task cards surround the laptop. The worker is relaxed and focused, wearing casual work attire. The setting is a modern home office with natural light streaming in through a window, plants, and minimal decor. The lighting is bright and calm, with soft, modern colours like whites, greys, and greens. The mood is productive, organised, and focused.

You'll need access to Microsoft Copilot in Office 365 to try this out for yourself, but even if you don't have access, it's worth a watch to see where AI content generation is heading.

Below is the prompt we use in the video (also in your prompt handbook):

You are an experienced instructional designer creating a 10-slide PowerPoint presentation for a workshop on Time Management for Remote Working. Build a deck that includes key sections: a title slide, an agenda, an explanation of the Eisenhower Matrix, practical tips, an example of how a remote worker uses the Eisenhower Matrix to prioritise tasks, a reflective question for participants, and a summary. The audience is remote workers balancing competing priorities, so keep the language professional, clear, and practical. Ensure the slides have structured content, logical flow, and a clean, modern design with simple visuals.

Below are some other awesome tools that can take your learning design to the next level. Experiment with some (or all) as you put together training resources for your program, and share the results in the Slack channel.

• Udio - Music generation

• HeyGen - AI video avatars

• NotebookLM - Create a podcast from any documents or files

• Sora - AI Video Generation (from the makers of ChatGPT)

Now it's your turn to try it all out. Working on your project, do the following:

1. Script your content: Use the CREATE framework to write a script for your training video, module, or session.

2. Create supporting visuals: Generate images using tools like DALL·E or MidJourney that align with your content.

3. Enhance with media: Experiment with tools like Heygen for AI avatars, Udio for music, or ElevenLabs for voiceovers to add that extra layer of polish.

And again, share your creations in the Slack community, along with anything you've learned along the way.

Let's recap everything that we have covered in module 3, and take a moment to reflect on the following question:

"What AI tools would you like to continue exploring? If you had half a day a week to experiment with AI, where would you start?"

Remember to share your reflection in our Slack community, and hear from others.

Test what we've covered in Module 3

Translating materials into different languages has never been easier than with the power of Generative AI. In this video we explore how to translate both written materials and video content in just a single click, using tools like DeepL and HeyGen.

A slight segue, but a really cool feature we love in Powerpoint is the AI powered coach - and the biggest bonus is that you don't even need a Copliot license.

Arizona State University is leveraging Generative AI to transform their learning for both students and staff. They've been implementing tools like:

• AI Writing Companion: A tool leveraging ChatGPT to help students improve their scholarly writing, making drafting and refining assignments easier and faster.

• ASU GPT Library: A growing library of Custom GPTs designed specifically for the needs of ASU’s students and faculty. These GPTs help with tasks like course alignment checks, meeting summaries, and providing additional learning support.

Let's recap everything that we have covered in module 4, and take a moment to reflect on the following question:

"What’s a project you are currently rolling out where AI could help you save time?"

Remember to share your reflection in our Slack community, and hear from others.

Lets quickly test what you learned in Module 4

Before we dive into using AI for Evaluation work, let's take a quick look at what we will be covering in this module.

In this video, we explore how to use AI to create a rubric, based off the Kirkpatrick Model for evaluation.

The Kirkpatrick model is broken into four levels:

1. Reaction: How did learners respond to the training? Did they enjoy the experience and find it useful?

2. Learning: What knowledge or skills did learners gain?

3. Behaviour: Are learners applying what they’ve learned on the job?

4. Results: What measurable impact did the training have on organisational goals?

Below is the prompt we use in this module, and if you'd like to try it out for yourself, we've also attached the full slide deck from this eLearning course.

You are an instructional design expert creating a rubric to evaluate learner performance across the ADDIE framework. Use the Kirkpatrick Model as the foundation for evaluation and reference the content and materials from the provided slide deck. Build a rubric with clear, actionable criteria for each stage of ADDIE (Analyze, Design, Develop, Implement, Evaluate), using three levels: Below Expectations, Meets Expectations, and Exceeds Expectations. Format the output as a table with one column for the ADDIE stage, a second column for the criteria, and three additional columns for each performance level (Below Expectations, Meets Expectations, and Exceeds Expectations). Ensure the criteria are specific, practical, and relevant to measuring success at each stage.

Now it's time to build a formative assessment, based on our rubric.

Below is the prompt we use in the video:

"You are an instructional design expert creating a formative assessment to evaluate learner progress across the ADDIE framework. Use the rubric we’ve just created, which aligns with the Kirkpatrick Model and the ADDIE stages (Analyze, Design, Develop, Implement, Evaluate). For each stage, generate 1–2 assessment questions or tasks that align with the rubric criteria. Each question should clearly test whether a learner is performing at Below Expectations, Meets Expectations, or Exceeds Expectations. Ensure the assessment is practical, measurable, and directly tied to the learning outcomes of each ADDIE stage."

Hive Learning is a great example of hyper-personalisation for design.

In this case study we explore how they are using technology to deliver:

• Multi-modal learning: Combining video, text, audio, and interactive elements to cater to different learning preferences and contexts.

• Just-in-time programmes: Providing the right content, at the right time—whether that’s a microlearning module before a big meeting or quick tips to address a specific challenge on the job.

Let's recap everything that we have covered in module 5, and take a moment to reflect on the following question:

"How can you evaluate the impact of your learning programmes better?"

Remember to share your reflection in our Slack community, and hear from others.

Test what you've learned in Module 5

Let's quickly recap everything we've covered in this journey:

• Analyze: In Module 1, you gathered insights to understand learner needs, conducted research with tools like Perplexity.ai, built TNA surveys, and analysed qualitative data to uncover key themes.

• Design: In Module 2, you shaped those insights into learning experiences—crafting outcomes with Bloom’s Taxonomy and designing lesson plans while exploring content creation tools.

• Develop: Module 3 was all about bringing ideas to life. You created scripts using the CREATE framework, generated visuals with DALL·E, and produced videos using tools like HeyGen—faster and more creatively than ever before.

• Implement: In Module 4, you delivered your ideas. You translated materials with DeepL and HeyGen, then built your very own GPT.

• Evaluate: In module 5 we used AI to create rubrics, formative assessments and got back into data visualization.