Low learning retention rates. Short engagement statistics. Quiet onboarding value.
As a learning designer, this is a nightmare scenario. You’ve invested time, research, and craft into a module. Yet learners aren’t connecting with it. The content is solid. The visuals are clean. So, what went wrong?
More often than not, the culprit is a learning design philosophy that lacks intersubjectivity.
But what is intersubjectivity?
Intersubjectivity is a concept from cognitive and social science that gives insight into why some learning sticks and why some doesn’t. Let’s explore.
Intersubjectivity is the understanding that the other person whom you are talking doesn’t have the same background and experiences as you have. This tells us not to assume that the other person has unsaid knowledge or context that you have to understand what you are saying.
In other words, intersubjectivity is the recognition that meaning and understanding have to be co-constructed.
What is intersubjectivity in learning design?
In learning design, intersubjectivity means this: your learner does not share your background, assumptions, or prior knowledge and your module must be built with that gap in mind. What you intend to communicate and what your learner actually understands are shaped by their prior knowledge, cultural context, cognitive style, and lived experience. The gap between those two is where learning fails.
Often, it happens that the modules are designed for the learner who satisfies your assumptions. While some learners will certainly fall in that category, not all will. Each learner has a different level of understanding and knowledge. This in turn means each learner has different needs and learns differently.
A very busy professional will not have time to sit through overly elaborate explanations. A bored professional needs to be engaged. The skeptical learner needs to be convinced.
In cases like this, content alone isn’t enough. Design makes the difference.
Each learning module you create needs to be designed with the learner in mind. The first-time learner will need a more in-depth module while the refresher course for the experienced learner has to be concise.
“We need to think about how our learners are accessing it. What do they need? How do we put ourselves in our learners’ shoes so that we can simulate that experience?”
How AI Can Help Address Gaps in Intersubjectivity aka How to design training for different learner types using AI
The good news? You don’t have to do this alone. AI can help. But only if you use it with intention.
Here are four ways it can support your design process.
Research: The first step to designing for intersubjectivity is to understand your learners deeply. AI can synthesize large volumes of learner data, behavioral research, and audience profiles into actionable insights far faster than manual review. From here, you can ask it to identify patterns across different learner segments.
Data Analysis: AI can compare your existing module against learner performance data to flag where understanding breaks down. The sections which have high drop-off, where questions are consistently failed, where learners rush without engaging are your learning gaps.
Module Creation & Adaptation: Once you know the gaps, AI can help you fill them. It can write a concise version of your content for the experienced learner. A more detailed version for the beginner. It can suggest different analogies, examples, and explanations for different cultural or professional contexts. One topic. Multiple entry points.
Feedback & Iteration: After deployment, AI can analyze learner responses and flag where intersubjectivity is still failing. This is how your design becomes a living, improving thing rather than a one-time deliverable.
Risks of Using AI
AI is useful. But it comes with risks. Every L&D designer needs to know these before getting started.
Data privacy and learner confidentiality: Learner data fed into AI tools may include sensitive personal or organizational information. Always check whether the tool stores, trains on, or shares your inputs. Use anonymized or synthetic data where possible.
Copyright and IP: AI-generated content may draw on copyrighted material without clear attribution. Any content you publish should be reviewed for originality, especially case studies, statistics, or quotes that AI produces confidently but without sourcing.
The “default learner” problem doesn’t disappear: AI trained on broad datasets may reflect biases toward certain learner types. A 2026 arXiv study found that even when learning context is provided to LLMs, substantial misalignment remains between AI instructional decisions and what expert educators would choose. Human oversight is non-negotiable.
Over-reliance reducing design creativity: Research cited by Dr. Philippa Hardman found that excessive AI use correlated with a 32% reduction in unique assessment designs among instructional designers. AI should accelerate your thinking, not replace it.
AI Prompts for Instructional Designers: Navigating Intersubjectivity
For learner analysis:
“Here is a set of learner feedback and completion data [paste data]. Identify where learners are likely losing shared understanding with the designer. What assumptions does the module appear to make about prior knowledge?”
For persona-based design:
“I’m designing a module on [topic] for [audience]. Create three brief learner personas: one who is time-poor and experienced, one who is skeptical of the topic’s relevance, and one who is new to the subject. For each, suggest one design adjustment that would improve their engagement.”
For content gap analysis:
“Review this learning objective and this module excerpt [paste content]. What background knowledge does this content assume the learner already has? Where might meaning break down for someone without that context?”
For alternative explanations:
“Rewrite this explanation [paste text] for someone who has no prior experience with this topic, using an analogy from everyday life.”
The Bottom Line
Intersubjectivity isn’t a theory to admire from a distance. It’s a design standard.
The gap between what you intend and what your learner understands is where learning fails. AI can help you see that gap. It can help you close it faster. But the judgment and the empathy? That’s still yours.
Your role as a designer isn’t shrinking. It’s sharpening. And that’s a good thing.
At Apposite, we help organizations design learning experiences that improve learner retention, engagement, and real workplace performance.
Want to see how leading organizations apply intersubjectivity in learning design? Watch our video and explore how smarter design drives better learner retention.