Designing Scalable, Performance-Based Learning Using AI-Enabled Instructional Design

EXPLORE

Executive Summary

Clinical and operational teams needed accurate learning solutions aligned to Medicare STAR measures. Learners demonstrated gaps in applying complex Medicare STAR requirements consistently across workflows, creating downstream compliance and quality risks. While clinical partners were aware of these measures, they struggled with accurate application.

To address these gaps, I designed a blended learning solution leveraging AI-enabled instructional design workflows to accelerate content development while maintaining quality, clinical accuracy, and performance alignment. I focused on creating modules that supported judgment under real-world constraints, and designed them to support rapid updates and performance support. I leveraged AI to accelerate content drafting, visual prototyping, and iteration. This allowed for more time to be focused on instructional strategy, validation with SMEs, and learner experience.

I used AI tools to:

Reduce course development time by approximately 25%
Enable faster iteration on high-priority STAR-related learning needs
Maintain instructional consistency across multiple learning assets

Business Context and Audience

This initiative supported enterprise healthcare teams responsible for understanding and applying Medicare STAR requirements within complex, high-stakes workflows.

Learners included clinical, operational, and quality stakeholders who needed to translate regulatory guidance into consistent day-to-day decisions.

The learning environment required:

Rapid updates as guidance evolved
High accuracy and compliance
Practical application over theoretical knowledge

Performance Gap and Learning Need

Analysis revealed that the primary gap was not awareness, but inconsistent application of STAR requirements in real-world scenarios. Learners struggled to apply guidance accurately under time pressure and across varying contexts, creating variability in performance outcomes.

The instructional challenge was to design learning that:

Supported judgment and decision making
Reduced cognitive load
Prioritized performance support over content recall

Instructional Strategy

I designed learning experiences grounded in adult learning principles and performance-based instructional design to address these gaps:

Modular content architecture to enable rapid updates and reuse
Performance support tools to reinforce correct application at the point of need
Blended delivery combining self-paced learning with applied practice

AI was intentionally used to accelerate early design phases allowing more time for:

Instructional strategy refinement
SME collaboration and validation
Learner experience optimization

AI amplified efficiency so design work could remain the priority.

Learning Solutions

The final solution included:

Self-paced eLearning modules focused on applied decision making
Interactions aligned to real workflows
Job aids and performance support tools for ongoing reference
Reusable learning components designed for scalability across initiatives

This approach ensured learning assets could evolve alongside regulatory and business needs without full redesign cycles.

Evaluations and Outcomes

Evaluation focused on both efficiency and effectiveness:

Development efficiency: Course production timelines reduced by about 25%
Stakeholder feedback: Improved turnaround time for updates and increased confidence in instructional consistency
Learning quality: Maintained instructional standards while increasing speed and scalability

This solution enabled faster response to high-priority learning demands and improved alignment between learning assets and business needs.

My Role and Takeaways

I served as the lead instructional designer, owning the solution end-to-end:

Conducted analysis and identified performance gaps
Designed instructional strategy and learning architecture
Partnered closely with clinical SMEs and stakeholders to validate accuracy
Consulted on appropriate use of learning vs. performance support
Integrated AI thoughtfully to improve efficiency without compromising quality

This role required balancing speed, accuracy, and instructional integrity while collaborating across functions and managing competing priorities

Professional Development

I am currently exploring Google Flow to create instructional videos using copy from courses I previously created. The video below is an example of a sentence from a slide in this course, reimagined using Google Flow. While the technology is not accurate yet or cost effective, in a few short years I could see instructional designers using this tool to create courses or instructional videos. This could change the way instructional designers produce learning content in the future.

If technology improves at the rate it has been for the last few years, I could see instructional designers step away from traditional video creation platforms like Camtasia to fully adopt AI video platforms. For me AI is a tool which helps me design effective and engaging instruction. I am continuously developing my abilities in AI by exploring new platforms, attending professional development trainings on AI, and reading white papers and research papers on LLMs.