This past month, I dove deep into the world of andragogy and AI—a venture so intense it completely sidetracked my October writing plans. Breaking my 2024 writing streak wasn’t ideal, but, like a good dose of medicine, sometimes the benefits of stepping back outweigh the costs. So, here I am in November, back in action, with insights worth sharing that (hopefully) make up for that missed post.
In January, a small startup brought me on board to design and develop a new certificate program. After completing a series of needs assessments and surveys, I began drafting the curriculum in March. The data was clear: we needed a hybrid learning program that would leverage micro-lessons, structured along outcome-based learning paths, each leading to a certificate. At the client’s request, I introduced the program to stakeholders at their May conference, where it received valuable feedback. With the data validated and design approved, I began the development phase in September.
I’ll save the story of the unique dynamics of startups, co-founders, and budding entrepreneurs for another time. Today, I want to share the many pivots and potholes that marked the journey of bringing andragogy and AI together in this project.
Situation 1: Stakeholder Opinions in Flux
Challenge
Client stakeholders had conflicting and frequently shifting opinions, with little interest in achieving consensus.
Approach
To handle this, I used ChatGPT 4.0 to organize stakeholder feedback into key themes, which I then compared against foundational andragogical principles. This approach allowed me to clarify the educational rationale behind each direction.
AI’s Contribution
AI’s role was invaluable here: it removed emotional biases from strongly held opinions, helping me achieve a balanced, objective analysis with far less effort. By using AI, I could focus on my expertise, drafting well-grounded recommendations quickly and efficiently.
Situation 2: Balancing Competing Priorities
Challenge
While the subject matter experts (SMEs) were enthusiastic about collaborating, their primary job responsibilities often took priority, limiting the time and attention they could dedicate to this project.
Approach
To address this, I used ChatGPT 4.0 to simulate the perspective of a subject matter expert and review the curriculum holistically. This allowed me to proactively identify gaps without requiring the SMEs’ constant involvement.
AI’s Contribution
With AI’s insights, I could pinpoint areas needing SME input more efficiently. This meant I could focus their limited availability on specific issues, making our collaboration more targeted and time-effective.
Situation 3: Streamlining Learning Objectives and Course Outlines
Challenge
Creating Bloom’s Taxonomy-aligned learning objectives and developing detailed, curriculum-specific course outlines with subject matter experts (SMEs) required significant time and strict adherence to instructional design standards—standards that SMEs may not always fully understand.
Approach
To address this, I used ChatGPT 4.0 to draft initial Bloom-aligned learning objectives and generate tailored course outlines for SME review and revision.
AI’s Contribution
AI significantly reduced the time and effort typically needed for developing learning objectives and course outlines, accelerating the pace at which SMEs could provide feedback. It also saved me countless hours on curriculum updates, streamlining the entire process.
Situation 4: Adapting Content for Diverse Learner Personas
Challenge
The curriculum had to accommodate a range of learner personas across certificate paths—some learners were technically skilled and familiar with the subject matter, while others were non-technical and needed to adapt or adopt new knowledge in a technology-focused field.
Approach
To address these varied needs, I used Nolej to create lesson-specific activities, NotebookLM (Experimental) to develop alternate formats for public print and web content, and Airmeet to provide private video content in adaptable formats.
AI’s Contribution
AI tools accelerated the application of andragogical practices such as inclusion and accessibility, enhancing engagement and retention by supporting diverse learning preferences and levels of familiarity with the subject.
As the project progressed, it became clear that integrating AI tools wasn’t just enhancing the quality of work—it was also transforming the efficiency of each stage. By leveraging AI, I was able to streamline processes that traditionally required extensive time and effort, allowing for more focused and impactful collaboration. The table below highlights the time savings achieved in this project compared to a past project of similar scope and complexity, underscoring the substantial impact of AI on time to programmatic impact.
By leveraging AI tools throughout this project, I achieved substantial time savings across key areas. The table below compares the time required for similar tasks on a past project (completed without AI) to the time required for this project (completed with AI). The “Reduced By” column highlights the percentage decrease in time, demonstrating how AI accelerated programmatic impact in each situation.
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These time savings reflect more than just faster project completion. By reducing the effort required for these tasks, AI enabled me to focus on strategic aspects of program development, enhancing both the quality and accessibility of the final product. This efficiency ultimately allowed the project to stay close to its original timeline, even with the challenges along the way.
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