Degreed Experiments: Unlocking Hands-On, Adaptive Learning

Taylor Blake

mercredi 16 juillet 2025

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One of the most compelling aspects of Degreed Maestro’s conversational voice AI is its ability to create bi-directional, personalized, and adaptive learning experiences. Yet, we know voice isn’t the ideal interface for every learning task.

Voice AI falls short when:

• You need high fidelity for creating or inputting complex information.
• Visual referencing is crucial.
• You need time and space for in-depth problem-solving.
• Tasks involve non-speaking elements, like typing code or navigating a software interface.

This raised a key question for us: Could we harness the best of voice—its interactivity and adaptiveness—and apply it to non-voice contexts?

Our Latest Experiment: Adaptive Learning Exercises

Our answer became an innovative approach to « learn by doing. » The concept is simple: you start with a learning goal, which is then broken down into progressive requirements or milestones.

From there, AI generates micro-tasks, one at a time, to guide your understanding and practice. You receive immediate, personalized feedback after each attempt, and the next task dynamically adjusts based on your progress and comprehension. 

Early Feedback

• « I thought it was intuitive. I liked how it made you do something after each explanation and task description. » 
• « Overall, it was smooth. The feedback it gave me on my responses was helpful. »

Diversifying Modalities 

Hands-on interaction was critical. We began with text input for various tasks, then expanded to support a code editor for more technical applications.

Next, we integrated webcam and screen recordings. The screen recordings, in particular, proved invaluable, allowing early testers to demonstrate their abilities directly within the context of their work or specific applications. 

Early Feedback

• « I like how it had me not only write but record verbally. What I was reinforcing was what I learned. So those are very good.” 
• “It was definitely interactive and engaging. »

Finally, we added multiple-choice questions because constantly requiring text input can feel burdensome; these questions offer a lighter way to confirm understanding.

With this diverse array of modalities, AI can select the most appropriate format for each task and sequence them progressively, effectively managing cognitive load. In practice, this often means starting with multiple-choice questions to confirm foundational understanding, moving to text or code input for hands-on application, and concluding with webcam or screen recordings to demonstrate mastery.

Creating Tailored Instruction

A significant challenge was finding the sweet spot for instructional support: enough to prevent frustration, but not so much that it created noise. Our current solution involves several configuration options:

• Hints – Available on demand
• AI-Powered Q&A – For real-time clarification.
• Instructional Depth – Customizable from none, to basic, to in-depth.

We also include a « rabbit hole » icon on each task, allowing early testers to deep-dive into specific learning resources for additional context or explanation, if and when they need it. 

Navigating the Nuances 

Making the process truly adaptive came with its own set of challenges. If too open-ended, learners lacked clear expectations regarding time or effort. We opted for an initial structure combined with an adaptive path, ensuring progress was always visible.

Measuring progress against requirements was another hurdle, especially when mastery might take several attempts across an undetermined number of tasks. Achieving the « just right » feeling for adaptiveness required extensive iteration. The system needed to ease up when a learner struggled, chunk tasks into manageable sizes, build upon prior knowledge, and align with examples and instructions without removing the challenge entirely.

From Prototype and Beyond: The Journey Continues

This experiment will continue to evolve—thanks to feedback from more than 50 early testers (thank you!). We anticipate that admins and curators will design learning objectives, integrate these adaptive experiences into broader learning pathways, and benefit from robust reporting and insights. Per early feedback, a core component of this will be the ability to upload existing documentation or training materials to automatically generate and customize learning requirements by leveraging your organization’s unique knowledge and an employee’s unique work. 

As one early tester shared, it’s « a great tool. It’s opened my eyes to how companies can adopt it with proprietary knowledge to really help… an online assistant that will help in real time. »

Ultimately, this experiment has proven to be an exceptionally flexible, engaging, and effective learning tool. Its ability to provide immediate, tailored feedback without increasing administrative burden is invaluable. The AI-driven adaptive progression ensures the difficulty always feels « just right, » while optional deeper instruction empowers learners to customize their support. 

Early Feedback

• « For people who want to learn on the job and quickly need to understand something, yep, absolutely valuable. »
  

What’s Next for Adaptive Learning?

The success of this prototype has naturally sparked exciting, new « what if » possibilities for future development, including:

• Adaptive Assessments: Reimagining how we measure skills and knowledge.
• Adaptive Learning Support & Motivation: Moving beyond adapting instruction and tasks, we envision personalizing the way we support and motivate learners throughout their journey.
• Adaptive Workflows: Instead of screen recordings, which carry data sensitivities, can AI generate copy-cat workflows optimized for practice without real-data risks? 

Get Involved

If you’re interested in experiencing this prototype firsthand, you can:

• DM Taylor Blake on LinkedIn
• Sign up for our Experiments Email List
• Follow Degreed on LinkedIn