How Learning Designers Are Using AI for Analysis
A practical guide on how to 10X your analysis process using free AI tools, based on real use cases
Hey folks!
In the ever-evolving field of learning and instructional design, AI is becoming an increasingly indispensable ally. From streamlining complex processes to providing deep insights, AI tools are transforming the way learning designers approach the end-to-end analysis, design, development, delivery, and evaluation process.
This week, I spent some time exploring the analysis part of the process with a large, global team of learning designers. I also kicked off the June cohort of my AI Learning Design bootcamp. In both case, we explored what great analysis looks like before asking the million-dollar question: how might AI help us to optimise the analysis part of our process?
In this week’s newsletter, I share a summary of how learning designers are using free-to-use AI tools to both automate and augment aspects of the analysis process, based on my experience on the ground.
How Learning Designers Use AI in Analysis
There are three key areas where AI tools make a significant impact on how we tackle the analysis part of the learning design process:
Understanding the why: what is the problem this learning experience solves? What’s the change we want to see as a result?
Defining the who: who do we need to target in order to solve the problem and achieve the intended goal?
Clarifying the what: given who our learners are and the goal we want to achieve, what concepts and skills do we need to teach?
Analysis is an historically tricky part of the process, requiring the necessary time and skills to gather and analyse data (often two things that we learning designers don’t have).
For this reason, AI seems to be becoming an increasingly indispensable ally in the analysis process. By leveraging AI, learning designers can enhance both the speed and depth of all three stages of the analysis part of the process.
Use Case 1: Understanding the Why
Aka, defining the problem, root cause and goal.
Understanding the business problem and aligning it with strategic goals ensures that the training addresses the right issues and provides measurable ROI.
Business Problem:
Perplexity: use it to conduct initial research on industry trends, helping to accurately frame the problem. By understanding common issues within the industry, designers can better pinpoint the root cause of the problem.
ChatGPT 4o & Gemini: use these tools to turn a high level request, e.g. “leadership training to improve leadership in the sales team” into a robust problem statement by running “five whys” analyses and identifying a range of potential root causes to validate with stakeholders.
ChatGPT 4o & Gemini: these tools can also assist in drafting and refining questions for problem-definition interviews with stakeholders based on the initial high level request. This ensures that data collection is comprehensive and targeted, making it easier to gather the necessary information.
Claude & ChatGPT 4o: aggregating and analysing stakeholder inputs is crucial. These tools can help to identify common themes and root causes by processing large volumes of quantitate and qualitative data quickly.
Business Goal:
ChatGPT 4o & Claude: use these tools to analyse the organisation’s vision, mission, and KPIs and align the training request with strategic goals. If we run this training, what impact will it have on high level goals? This alignment ensures that the training is relevant and supports the organisation’s objectives. It can also help to define key metrics for evaluating success and impact.
Perplexity: use Perplexity to find relevant data and real-world examples of how other organisations have successfully tackled similar problems. Perplexity can also be a great way to access relevant industry reports, all of which helps us to create a compelling and realistic business goal statement.
Use Case 2: Knowing the Who
Aka, defining learner profiles.
Knowing who our target learners are and what motivates them ensures that the training is engaging and relevant, leading to higher participation and better outcomes.
Demographics:
ChatGPT 4.0 & Gemini: use these tools to analyse relevant existing HR data on who your learners are, their age, location, role, responsibilities and other practical info to build a profile of what your learners need to be able to engage with the learning.
ChatGPT 4.0 & Gemini: you can also use these tools to create and distribute structured surveys to gather intel on learner demographics and ensure that the collected data is comprehensive.
ChatGPT 4.0 & Claude: use these tools to analyse, theme and aggregate the data you generate into one or more learner demographics profiles.
Psychographics:
ChatGPT 4.0 & Gemini: use these tools to analyse sources which provide a window into your learners’ motivations, career paths, and goals. This might include job applications, LinkedIn profiles and discussions on Teams / Slack.
ChatGPT 4.0 & Gemini: you can also use these tools to create and distribute structured surveys to gather intel on learner psychographics and ensure that the collected data is comprehensive.
Fathom: use this tool to record and and summarise learner interviews, providing deeper insights into learners’ aspirations and motivations. By capturing qualitative data from these interviews, Fathom helps paint a clearer picture of the learner's mindset and motivations.
ChatGPT 4.0 & Claude: use these tools to analyse, theme and aggregate the data you generate into one or more learner psychographics profiles.
Use Case 3: Defining the What
Aka, defining what we need to include in the learning experience (and why).
Clarifying what learners need to know and be able to do to achieve the goal ensures that the training is focused and effective, preventing cognitive overload and maintaining engagement. It also helps to ensure that we align the training with the business goal, which in turn optimises it for impact and Return on Investment.
Current Knowledge & Skills:
ChatGPT 4.0, Gemini & QuizGecko: use these tools to create pre-course activities and surveys to gauge learners' current knowledge, capability and confidence levels.
ChatGPT 4.0, Gemini & Claude: use e these tools to analyse existing performance data and other data, e.g. discussion channels / forums, to identify knowledge gaps and common challenges.
Perplexity: use Perplexity to zoom out and analyse common challenges among your target group in relation to your goal, helping to design training that addresses these specific issues effectively.
Required Knowledge & Skills:
Consensus & Perplexity: use these tools help create a detailed knowledge and skills map for the training, outlining what concepts and skills are essential.
ChatGPT 4.0, Gemini, & Claude: use these tools to compare the knowledge and skills map you create with existing performance data, thus defining key areas of focus and ensuring that the training content is relevant and comprehensive.
Conclusion
While the most common use case of AI in learning and instructional design remains content creation, some of the most powerful use cases often lay outside of the development phase.
In small, cutting edge pockets of the learning design world, AI tools are beginning to impact both the speed and quality of the broader end to end process. In analysis phase, AI is helping learning designers to ensure that their groundwork is thorough, data-driven, and aligned with both organisational and learner needs, ultimately leading to more effective and impactful training programs.
Given the impact it can potentially have on our effectiveness and - ultimately - our learners’ outcomes, I believe that embracing AI for analysis is not just a trend: it’s a necessity for modern instructional design.
I’d love to hear your thoughts!
Happy designing,
Phil 👋
PS: If you want to learn how to leverage AI across the end to end learning and instructional design process, apply for a place on my AI Learning Design Bootcamp.
PPS: Here’s a review of the bootcamp from a student in my May cohort.