Taker, Maker, Shaper
A Practical AI Integration Framework for Instructional Designers
As my post last week showed, how we might integrate artificial intelligence (AI) into the instructional design process is no longer a question or discussion about the future— it's a very real, present-day reality and (for some) a conundrum about how to keep up.
95.3% of instructional designers use AI in their day to day work.
There is no doubt in my mind that AI offers huge potential to transform how we instructional designers, create, implement, and evaluate learning experiences.
But, navigating the vast landscape of AI tools and use cases is incredibly daunting.
To help cut through the noise and inspired by this recent article by Mckinsey, I’d like to share a practical framework to empower instructional designers to experiment with and integrate AI in their day to day work: the Taker, Maker, Shaper AI framework for instructional designers.
In this week’s post, you’ll find a summary of the framework and tips on how you can start using it to integrate AI into your process today.
Let’s go!
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Understanding AI Archetypes
AI archetypes are essentially personas: conceptual models that we can use to understand how to integrate AI into our life and work.
AI archetypes communicate different ways that AI can contribute to or enhance how we live and work, from automating routine tasks to generating innovative solutions.
When it comes to integrating AI into instructional design, I propose three AI archetypes:
1. Taker: The Automator
The Taker AI archetype is a copilot that automates or streamlines tasks by taking over certain functions, e.g. data analysis or content creation.
Think of the Taker as your AI apprentice which, with the right instructions and feedback, can take control of functional aspects of the instructional design process so that you can concentrate on the more strategic and high-value aspects of the process.
2. Maker: The Innovator
The Maker AI archetype is a copilot that can generate new and innovative solutions, or ideas, often creating outputs that didn't exist before, e.g. by designing new products or developing innovative strategies.
Think of the Maker as your AI co-worker who helps your to generate ideas and make informed decisions, in the process enriching and diversifying your instructional design toolkit and skill set.
3. Shaper: The Optimiser
The Shaper AI archetype is a copilot that optimise or enhances processes and decisions by analysing data and patterns to provide recommendations, e.g. by using data-driven insights to tailor and improve processes and services.
Think of the Shaper as your personal data analyst - a specialist capable of providing you with insights to measure success and impact, and fine-tune your work.
AI Archetypes in Action
What could these AI archetypes look like in the context of instructional design?
1. The Taker
Taker AI enables us to automate and streamlining routine, structured tasks, freeing us up to concentrate on more strategic elements of projects. In practice, this could look like:
Analysis: Automate learner needs assessments using AI to analyse survey data and feedback, identifying trends and gaps efficiently.
Design: Generate draft learning objectives and content outlines, speeding up the initial stages of the process.
Development: Use AI to rapidly create accessible content, such as auto-generated captions for videos, saving time and ensuring inclusivity.
Implementation: Use AI to provide “always on” learner feedback..
Evaluation: Use AI for quick analysis of feedback and performance data, enabling timely course adjustments.
2. The Maker
Maker AI helps us to generates new ideas, or solutions, pushing the boundaries of traditional instructional design. In practice, this could look like:
Analysis: Use AI to generate multiple complex learner profiles by triangulating insights like performance data, review data and semantic analyses.
Design: Use AI to make evidence-based decisions about the optimal instructional strategy for your learner(s) and goal.
Development: Use AI to develop hyper-differentiated experiences and materials, mapped to multiple complex learner profiles
Implementation: Build GPTs which are capable of behaving of virtual instructors for personalised guidance, driving learner motivation and mastery of objectives.
Evaluation: Use predictive analytics to predict learning outcomes dnd optimise your designs before they are launched.
3. The Shaper
Shaper AI enhances and optimises the learning analysis, design, delivery & evaluation process, using data-driven insights to inform and tailor learning experiences. In practice, this could look like:
Analysis: Experiment with predictive profiling, using historical performance data, learning behaviours, semantic analyses and even social media trends to anticipate individual and group learning needs.
Design: Experiment with AI’s ability to dynamically alter the difficulty, content and tone of learning experiences to optimise for motivation and mastery.
Development: Experiment with real-time content generation and adaptation, using data about industry trends, organisational changes and other mission-critical insights to update learning materials in real-time.
Implementation: Experiment with designing & delivering data-powered intelligent tutors, which understand and dynamically respond to each learner's unique needs, preferences, and goals.
Evaluation: Experiment with dynamic learning optimisation. Instead of simply employing predictive analytics for assessment, Shaper AI could continuously analyse learning data to identify not only which parts of a course are most and least effective but also why. It could then recommend specific improvements or even implement minor adjustments automatically, such as modifying assessment questions, altering content delivery pace, or introducing new examples to clarify complex concepts.
Implementing the Taker, Maker, Shaper AI Framework: A Step-by-Step Guide
So, how do you get started on your journey to AI experimentation and integration? Here are my tips:
Step 1: Start with the Taker Archetype
Begin by listing your day to day “jobs to be done” and ask: if I had an apprentice, which tasks would I delegate, how and why? Then, for each task you’d like to delegate, explore what can be automated or enhanced using AI.
Impact: efficiency gains.
Examples:
Analysis: Automate learner needs assessments using AI to analyse survey data and feedback, identifying trends and gaps efficiently.
Design: Generate draft learning objectives and content outlines, speeding up the initial stages of the process.
Development: Use AI to rapidly create accessible content, such as auto-generated captions for videos, saving time and ensuring inclusivity.
Implementation: Use AI to provide “always on” learner feedback..
Evaluation: Use AI for quick analysis of feedback and performance data, enabling timely course adjustments.
Step 2: Experiment with the Maker Archetype
Once you’re confidently and effectively working with Taker AI, explore Maker applications.
Start by listing your day to day “jobs to be done” and ask: if I had an “always on” colleague to push my practice and impact, how would I work with them and why? Then, for each task you’d like to collaborate on, explore what sorts of AI tools could be used and how.
Impact: innovation gains.
Examples:
Analysis: Use AI to generate multiple complex learner profiles by triangulating insights like performance data, review data and semantic analyses.
Design: Use AI to make evidence-based decisions about the optimal instructional strategy for your learner(s) and goal.
Development: Use AI to develop hyper-differentiated experiences and materials, mapped to multiple complex learner profiles
Implementation: Build GPTs which are capable of behaving of virtual instructors for personalised guidance, driving learner motivation and mastery of objectives.
Evaluation: Use predictive analytics to predict learning outcomes dnd optimise your designs before they are launched.
Step 3: Optimise & Personalise with the Shaper Archetype
Finally, once you’re confidently and effectively working with Taker & Maker AI, explore applications of Shaper AI.
Again, start by listing your day to day “jobs to be done” and ask: if I had a personal data analyst, how would I work with them and why? Then, for each task you’d like to work on with a data analyst, explore what sorts of AI tools could be used and how.
Impact: impact gains.
Analysis: Experiment with predictive profiling, using historical performance data, learning behaviours, semantic analyses and even social media trends to anticipate individual and group learning needs.
Design: Experiment with AI’s ability to dynamically alter the difficulty, content and tone of learning experiences to optimise for motivation and mastery.
Development: Experiment with real-time content generation and adaptation, using data about industry trends, organisational changes and other mission-critical insights to update learning materials in real-time.
Implementation: Experiment with designing & delivering data-powered intelligent tutors, which understand and dynamically respond to each learner's unique needs, preferences, and goals.
Evaluation: Experiment with dynamic learning optimisation. Instead of simply employing predictive analytics for assessment, Shaper AI could continuously analyse learning data to identify not only which parts of a course are most and least effective but also why. It could then recommend specific improvements or even implement minor adjustments automatically, such as modifying assessment questions, altering content delivery pace, or introducing new examples to clarify complex concepts.
Conclusion
The integration of AI into instructional design represents a significant leap forward in how we design, deliver and evaluate the impact of learning experiences.
By understanding and applying the Taker, Maker, and Shaper archetypes, as instructional designers we can strategically leverage AI - not only enhancing our workflows but also redefining the possibilities of instructional design, teaching and learning.
Happy experimenting!
Phil 👋
PS: If you want to explore the impact of AI on instructional design and hone your AI skills, check out my Learning Futures newsletter and my AI Learning Design Bootcamp.
