Transforming Workplace Learning With Design Thinking For AI-Powered Microlearning Videos
Summary:
In a fast-paced world with limited time, delivering meaningful learning in minutes may seem impossible. This guide explores how AI and design thinking frameworks—empathize, define, ideate, prototype, and test—can help create accessible, practical microlearning videos that resonate with learners.
Combining Design Thinking With AI For Engaging Microlearning Videos
As an Instructional Designer, I've always been drawn to the challenge of simplifying complex concepts. In education, where time is a luxury and engagement can be elusive, I asked: How can I deliver impactful learning moments in just a few minutes? The answer revealed itself through microlearning videos—bite-sized, learner-centered, and focused. But the real magic happened when I approached content creation with the design thinking framework—a process that transformed problems into stories and solutions.
Design Thinking Principles
1. Empathize
It started with listening. I sat down with educators, students, and staff, hearing their frustrations—like a professor juggling calendars or a staff member overwhelmed by new tools. Their struggles became stories waiting to be told.
2. Define
From these conversations, I reframed problems into scenarios: What does this look like in their world? For the professor, it was the chaotic overlap of meetings. This step gave me clarity and direction.
3. Ideate
With a clear scenario, I partnered with AI tools to brainstorm. Together, we wrote concise scripts, crafted relatable examples, and designed visuals that brought these stories to life. To connect audibly, I used AI voiceovers, ensuring tone and language were clear, engaging, and multilingual.
4. Prototype
I shared early versions of the videos—imperfect but actionable—with small groups. Their feedback was like an editor polishing a rough draft, shaping the narrative until it clicked.
5. Test
Finally, I watched learners interact with the videos. Were they engaged? Did they walk away with answers? Their reactions told me where to refine, ensuring every moment delivered value.
By treating each stage like a storytelling process, design thinking helped me transform challenges into solutions learners could see, hear, and relate to—turning minutes into moments that truly matter.
Crafting Microlearning Videos With Design Thinking And AI: An Overview Of The Process
1. Empathize: Listening To The Community
Every great solution begins with understanding the user. I started with a series of conversations—immersing myself in the experiences of educators, students, and staff. Rather than skimming the surface, I dug deeper to uncover their pain points and needs.
- A professor shared frustrations about syncing calendars for team projects.
- A dean's assistant described feeling overwhelmed by new tools with no clear guidance.
- A staff member highlighted the lack of accessible resources for Spanish-speaking learners.
These insights were more than problems; they were opportunities to create meaningful learning solutions. I documented these conversations as reflections in my journal, keeping the focus objective and empathetic.
To define these problems further, I leveraged an AI chatbot to transform my notes into actionable research questions. For example: How can we teach calendar syncing in a clear, engaging way? From there, I developed a mind map with seven core questions, each addressing a specific need within the community.
2. Define: Turning Challenges Into Scenarios
With a clear understanding of the users' challenges, I reframed each problem as a scenario to ground my approach: For calendar syncing, I visualized: A professor juggling multiple classes and meetings needs a clear, simple guide to merge calendars efficiently.
I then asked the chatbot to refine my ideas: "What steps should a microlearning video include to make calendar merging simple, engaging, and actionable?" The AI response provided a structured outline that became the roadmap for the video content. This step ensured that the problem remained learner-focused and connected to real-world needs.
3. Ideate: Crafting A Vision For The Videos
With the scenario set, I began brainstorming solutions and co-creating with AI to bring the content to life:
Script Development
The next step was bringing my vision to life. I began by drafting a script, and this is where the chatbot truly became my co-creator. It helped me fine-tune the language, ensuring the tone was both professional and approachable while keeping the video relatable. The script was concise—two minutes max—and focused on solving a single problem: merging calendars. For example: "Create a 2-minute script on merging calendars, using real-life scenarios and maintaining a joyful, clear tone."
Visual Design
I turned to an online graphic design tool to create clean, polished slides that aligned with the script. Visuals were purposeful, emphasizing key actions like "sync" buttons and calendar views without overloading the learner.
Accessibility And Voiceovers
AI-powered voice generators allowed me to create audio narrations in both English and Spanish, ensuring the content was inclusive and accessible to all learners. I adjusted the tone and pacing to match the flow of the visuals. Syncing the voiceover with the visuals was a delicate process, but with careful timing, the result was flawless, offering a smooth, immersive learning experience for all viewers.
Together, these elements formed a coherent, engaging, and learner-centered video prototype.
4. Prototype: Bringing The Vision To Learners
Once the microlearning video was ready, I shared it with the community using a multi-faceted approach:
Easy Access
I uploaded the video to our training hub, ensuring it was searchable, well-organized, and categorized for quick access. On the video platform, I optimized the title and description for searchability, making sure anyone looking for help with calendar integration could easily stumble upon it.
Community Engagement
But my favorite part came when the video became part of a community of practice session. This is when participants could explore the challenges together, share their experiences, and collaborate in real time. Watching learners click play, absorb the information, and immediately put it into practice was the moment I knew all the effort had paid off. During a community of practice session, learners watched the video, shared insights, and discussed challenges collaboratively. Seeing them immediately apply the content validated its impact.
The experience also sparked the creation of a feedback loop around the use of calendar tools. As learners shared their thoughts and challenges, I was able to gain fresh insights, which in turn helped me refine the video content to better meet their needs. This stage wasn't just about delivery—it was about observing, gathering feedback, and refining the solution further. It was a continuous cycle of improvement that not only enhanced the learning experience but also deepened my understanding of how to better support the community's evolving needs.
5. Test: Refining Through Continuous Feedback
The true power of design thinking lies in its iterative nature. Feedback came in almost instantly. Many users praised the clarity of the videos, while others suggested additional features or support for different languages. Using AI tools once again, I analyzed their input to refine the next set of videos. Each iteration became more polished, inclusive, and tailored to the learners' evolving needs.
The AI-powered workflow for crafting slides and scripts made these adjustments effortless. Changes were implemented on the fly, keeping the content fresh, responsive, and in sync with the audience's needs. This approach also ensured the training remained relevant, adapting quickly to new technological rollouts and keeping learners connected and invested throughout the experience.
Using AI tools, I analyzed the feedback and made real-time adjustments:
- Scripts were updated.
- Visuals were fine-tuned.
- New videos were produced to address evolving needs.
What made this process truly unique was the immediacy—it all happened in real time during the course delivery. Users saw their suggestions seamlessly integrated into the videos, creating a sense of collaboration and ownership. This dramatically boosted engagement as participants felt like co-creators of the course.
This immediate response created a feedback loop where learners felt heard and involved—transforming them into active collaborators. By integrating their suggestions, the videos remained fresh, relevant, and engaging.
The Impact: Solutions That Empower
What began as conversations transformed into solutions that empowered learners. These microlearning videos were not just tools—they became bridges connecting users to knowledge in a way that was accessible, timely, and actionable.
The design thinking process—empathize, define, ideate, prototype, and test—ensured that every video addressed a real need and delivered immediate value. But the process didn't stop there. Each step fed into the next, creating a cycle of improvement. With every iteration, feedback became fuel, turning challenges into opportunities and evolving content into an ever-better version of itself.
AI was the catalyst that amplified this journey. It streamlined scripting, refined visuals, and ensured inclusivity through multilingual voiceovers, making the process faster and smarter. AI tools didn't replace creativity—they expanded it, helping me explore ideas I hadn't imagined and test solutions quickly.
Together, design thinking and AI formed a system in constant motion—an ongoing story of learning, refining, and innovating. Each video was a stepping stone, building toward a larger vision of accessible, learner-centered education that evolves alongside the world it serves. By staying iterative and learner-focused, I discovered new ways to make learning impactful, one microlearning video at a time.
Originally published at eLearning Industry.com
