The problem with one-size-fits-all learning
Most online learning experiences are designed for efficiency.
One course. One sequence of lessons. One quiz. One path through the material.
That approach works, but it assumes everyone learns the same way.
As Ryan explained, the next evolution of online education is personalization. Rather than treating every learner identically, AI creates opportunities to adapt content based on individual performance, knowledge gaps, and retention.
Instead of one size fits all, how can you make it so this is more beneficial to the person who's actually doing it and how they apply that?
The goal is not simply delivering information. It's helping people retain and apply it.
That distinction matters because learning is rarely just about exposure to information. The real challenge is understanding what a learner knows, what they've forgotten, and what they need next.
Why AI matters in education
That challenge helps explain why Ryan sees so much potential in AI. When people think about AI in education, they often picture it creating content: writing lesson plans, generating quizzes, or drafting course materials. Those capabilities are useful, but Ryan believes they only scratch the surface of what's possible.
The more significant opportunity is using AI to create learning experiences that respond to each individual learner. Instead of presenting every student with the same sequence of lessons and assessments, AI makes it possible to adapt the experience as someone progresses. A learner who quickly demonstrates mastery can move ahead, while someone who needs more reinforcement can receive additional practice before moving on.
That's the direction Ryan is pursuing with PressPrimer.
What I'm trying to do is branching in quizzes so that based on your performance or how you answer, it changes what comes next.
Rather than treating assessments as a final checkpoint, they become part of the learning process itself. Each answer provides another signal about what a learner understands, where they need more support, and what will help them make the most progress next.
Helping learners retain what matters
One of the most compelling examples Ryan shared was a learning technique called spaced repetition.
Rather than reviewing every concept equally, learners revisit information when they're most likely to forget it. Research has long shown that this approach improves retention, but managing it manually can be difficult, especially at scale.
Ryan built this capability into his quiz platform. As learners move through a question pool, the system tracks what they answer correctly, where they struggle, and which topics need additional reinforcement. It then uses that information to shape future practice sessions.
The next day they go in and it quizzes them on the things that they're most likely to struggle with.
Instead of treating every learner the same, the platform continuously adapts to individual performance. Someone who has already mastered a topic spends less time reviewing it, while someone who needs additional support receives more opportunities to strengthen their understanding.
Ryan even used the system with his wife while she was taking a university course. By feeding in practice questions and allowing the platform to identify weaker areas, she was able to focus her study time where it would have the greatest impact.
While spaced repetition itself is not new, AI makes it far easier to deliver personalized reinforcement at scale. Rather than relying on a fixed review schedule, learning experiences can continuously adjust based on performance, helping learners spend more time where it matters most.
Helping educators spend more time teaching
Personalization doesn't only affect learners. It also changes how educators manage assessments, feedback, and student progress.
Ryan's approach starts from a different premise. Rather than trying to automate teaching, he's focused on reducing the amount of administrative work that surrounds it.
Many of the AI-powered features in PressPrimer function less like a replacement for an instructor and more like an assistant. When reviewing assignments, for example, educators can define grading rubrics and performance levels. AI can then analyze submissions against those criteria, identify strengths and weaknesses, and suggest feedback for the instructor to review.
The key detail is that the instructor remains in control.
It's kind of like having a grad assistant.
That same philosophy extends throughout the platform. AI can help generate quiz questions, suggest distractors for assessments, draft feedback, identify potential plagiarism concerns, and flag content that may have been generated by AI. In each case, the goal is to provide additional context and recommendations rather than make decisions on behalf of the educator.
For instructors managing large groups of learners, that distinction matters. Time spent reviewing repetitive tasks is time that cannot be spent teaching, coaching, or engaging directly with students. By helping educators move through those workflows more efficiently, AI creates more opportunities to focus on the parts of learning that benefit most from human expertise and judgment.
Expanding beyond quizzes
Much of the conversation around online learning focuses on quizzes and assessments, but Ryan believes assignments deserve more attention.
For many subjects, some of the most valuable learning happens when students are asked to explain their thinking, write in their own words, or apply concepts to real-world scenarios. Those activities are often better suited to essays, short-answer responses, and other assignment-based work than traditional multiple-choice quizzes.
The challenge is that assignments can create significant administrative overhead for educators. In many learning platforms, reviewing submissions still involves downloading files, opening documents in separate applications, adding comments, uploading revisions, and managing feedback across multiple systems.
Over time, that friction discourages instructors from using assignments as frequently as they might otherwise.
Ryan's goal is to keep that entire process inside WordPress.
Using AI-assisted workflows, instructors can review submissions, annotate documents, apply grading rubrics, and generate draft feedback directly within the browser.
Everything happens in the browser, and it's just so much more efficient.
The result isn't simply a faster grading process. By reducing the effort required to review and respond to student work, assignments become a more practical tool for educators to incorporate into their courses. That opens the door to richer forms of assessment and feedback while making the overall learning experience easier to manage.
What agency owners and course creators should pay attention to
While Ryan's examples focused on education, the underlying challenges are familiar to many organizations. Any business that helps people learn, build skills, or demonstrate competency faces similar questions around assessment, feedback, and progress tracking.
One of the recurring themes during the conversation was that AI is making specialized solutions more accessible to smaller teams. Features that might once have required months of development, significant budgets, or enterprise-level software can now be built, tested, and refined far more quickly.
That shift creates new opportunities for course creators, membership site owners, and agencies serving specialized audiences.
That applies to a wide range of organizations, from healthcare providers and professional certification programs to corporate training teams, coaching businesses, and membership communities. As AI-powered tools become more widely available, expectations around those experiences are likely to evolve as well.
For agencies, that creates an opportunity to move beyond simply delivering websites and begin thinking more deeply about the workflows those websites support. Learning experiences, assessments, onboarding programs, certification pathways, and knowledge-sharing systems all become areas where thoughtful implementation can create meaningful value for clients.
The conversation also highlighted another important point: AI delivers the strongest results when paired with subject-matter expertise.
Over the course of the conversation, Ryan repeatedly emphasized a hands-on approach to using AI in both his products and his business.
I tend to hand-hold the AI pretty closely. It's always a conversation and incremental change.
That perspective feels particularly relevant as more organizations experiment with AI. The technology can accelerate development, generate ideas, and assist with execution, but human expertise remains essential for defining goals, evaluating outcomes, and ensuring the final result meets the needs of the people it's intended to serve.
The lesson beyond the technology
Throughout the conversation, Ryan returned to a consistent theme: the most meaningful applications of AI in education are not necessarily the most visible ones.
While much of the public conversation focuses on content generation, Ryan is exploring ways to help people learn more effectively. Whether that's adapting assessments based on performance, reinforcing knowledge at the right moment, assisting instructors with feedback, or making assignments easier to manage, the common thread is personalization.
Educators have long understood the value of tailoring learning experiences to individual needs. The challenge has been delivering that level of personalization consistently and at scale. Ryan's examples showed how AI can help close that gap by giving educators better insight into learner progress, enabling more targeted support, and reducing the administrative work that often competes with teaching itself.
When applied thoughtfully, AI becomes more than a tool for generating content. It becomes a way to make learning more responsive, more personalized, and ultimately more effective for both educators and learners.
