Try a short adaptive learning demo and see how adaptive learning can identify knowledge gaps, uncertainty, and learner progress in medical imaging education
Adaptive learning is an educational approach where the learning experience changes in response to what a learner appears to know, where they are struggling, and how their understanding develops over time.
That sounds straightforward.
But what does it actually look like for a radiography student?
We have been building a series of short, interactive learning experiences for medical imaging education. Rather than giving every student the same video, slides, and fixed quiz, the aim is to build a better picture of the learner's developing knowledge and adapt the teaching accordingly.
We have created a small fluoroscopic image quality demo to show the direction we are taking.
Try the adaptive learning demo:
It is deliberately a short example.
The interesting part is not the number of questions.
It is what happens to our understanding of the learner as they answer them.
Traditional online assessment often works like this:
Question.
Answer.
Correct or incorrect.
Next question.
At the end, the student receives a score.
The problem is that a score can hide a great deal.
Imagine two radiography students both achieve 80%.
One has a strong understanding of fluoroscopic image quality but makes two careless mistakes.
The other has significant gaps in their understanding but correctly guesses several answers.
The final score may be identical.
The educational problem is not.
This is one of the questions we are trying to explore with adaptive learning:
How do we estimate what a learner actually knows rather than simply counting correct answers?
A single correct answer is evidence.
It is not proof of mastery.
When a learner answers a question correctly, several explanations are possible.
They may understand the concept.
They may partially understand it.
They may recognise the answer from a previous question.
They may have eliminated two obviously incorrect options.
Or they may simply have guessed correctly.
For that reason, our approach does not treat one correct answer as the end of the story.
The system can build evidence across multiple interactions.
Does the learner answer related questions correctly?
Can they apply the same concept in a different context?
Do they recognise the concept in an image?
Can they distinguish between two similar explanations?
Does their performance remain consistent as the question becomes more difficult?
The aim is to develop an evolving estimate of knowledge rather than awarding mastery because a student clicked the correct answer once.
This is probably one of the biggest misconceptions about adaptive learning.
Correct answer?
Give a harder question.
Incorrect answer?
Give an easier question.
That is branching.
It can be useful, but adaptive learning can go considerably further.
Consider a student learning fluoroscopic image quality.
The student may understand spatial resolution but struggle with temporal resolution.
They may correctly identify image noise but misunderstand why it occurs.
They may remember a definition of magnification but struggle to apply the concept when looking at an image.
These are different knowledge gaps.
An adaptive learning system should try to identify what the learner is struggling with, not simply record that they selected option C instead of option B.
The next teaching interaction can then be selected because it provides useful learning or useful evidence.
Sometimes that might be another question.
Sometimes it might be an image comparison.
Sometimes it might be a short explanation.
Sometimes the learner may need to revisit a prerequisite concept.
The learning path does not need to be identical for every student.
There are facts radiography students need to remember.
We are not trying to remove recall from education.
But medical imaging practice requires considerably more than remembering definitions.
A student may be able to define spatial resolution perfectly and still struggle to identify loss of spatial resolution in an image.
They may recall the factors affecting fluoroscopic dose but make a poor decision during a simulated procedure.
They may understand image intensifier theory but struggle to critique the resulting image.
Knowing a fact and applying knowledge are related, but they are not always the same educational task.
This is why our adaptive learning experiences can examine knowledge through different question and interaction types.
The aim is to ask:
Can the learner recall it?
Can they recognise it?
Can they explain it?
Can they apply it?
Can they make a decision using it?
For medical imaging education, those distinctions matter.
The obvious answer is to provide more teaching.
The more difficult question is: what teaching?
Giving the learner the same explanation again may not solve the problem.
Perhaps they misunderstood a word.
Perhaps they are missing prerequisite knowledge.
Perhaps they know the underlying physics but cannot connect it to the image.
Perhaps they have developed a persistent misconception.
Adaptive teaching should try to respond to the pattern.
For example, a learner who repeatedly struggles with image noise may be directed towards a focused explanation and a new visual example.
A learner who understands noise but struggles when exposure factors are introduced may need the relationship between image quality and technique reinforced.
Another learner may already demonstrate secure understanding and gain little from repeating the same foundation material.
The goal is not to make learning easier.
The goal is to make the teaching more relevant to the learner's current understanding.
Image quality is a useful example because it connects physics, equipment, image interpretation and clinical decision-making.
Students may need to consider concepts such as spatial resolution, temporal resolution, noise, contrast and magnification.
These concepts do not exist independently in clinical imaging.
Changes to imaging technique can affect the resulting image.
Clinical requirements affect decisions.
Image quality must often be considered alongside radiation dose.
That makes fluoroscopic image quality a useful area for exploring adaptive medical imaging education.
Our current demo is intentionally small.
It is not intended to represent a complete fluoroscopy curriculum.
It is a way of showing how a short learning experience can begin responding to the learner rather than simply presenting content.
Ideally, it should not feel like an algorithm is examining them.
The student answers questions, interacts with images and receives teaching.
Behind the learning experience, their interactions contribute to a developing picture of their understanding.
A learner struggling with one concept may receive additional support.
A learner demonstrating stronger knowledge may progress to more complex application.
Different areas of knowledge can be considered separately.
In medical imaging, a student could be developing strongly in anatomy while struggling with positioning.
They may understand radiation protection theory but have difficulty applying it during a clinical decision.
A single percentage score can hide those differences.
Adaptive learning gives us the opportunity to make them more visible.
The same information that supports adaptation for students may also provide better information for educators.
Instead of seeing:
Average quiz score: 74%
an educator could potentially see:
Spatial resolution: secure
Temporal resolution: developing
Image noise: common cohort misconception
Magnification: strong recall but weak application
That is a very different conversation.
It may help an educator decide what needs to be retaught, where simulation time could be focused and which learners may benefit from additional support.
The purpose is not to replace the radiography educator.
It is to give the educator a clearer picture of learning.
We are at the beginning of this work.
The fluoroscopic image quality demo is deliberately a small example, but it shows the direction Virtual Medical Coaching is taking with adaptive medical imaging education.
Rather than simply asking whether a student passed a quiz, we are interested in a more useful question:
What does this learner appear to understand, and what should we teach next?
Try the demo:
I would genuinely be interested to hear what radiographers, medical imaging educators, and students think.
What works?
What does not?
And where would you like to see this approach applied next?
Adaptive learning in radiography education uses learner interactions to build an evolving picture of understanding and adjust teaching, feedback, or learning pathways accordingly. Instead of every student receiving the same fixed sequence, the experience can respond to areas of strength, uncertainty, or difficulty.
One correct answer should not automatically be treated as proof of mastery. Adaptive learning can examine patterns across multiple questions, related concepts, question difficulty, and different types of applications. Consistent evidence provides a stronger indication of understanding than one isolated correct response.
No. Recall can be assessed, but medical imaging learning also involves recognition, application, image interpretation, and decision-making. Adaptive learning experiences can use different interaction types to explore whether a learner can apply knowledge rather than simply remember a definition.
A normal quiz usually presents a fixed set of questions and calculates a score. Adaptive learning uses learner responses as evidence about developing knowledge. That information can influence subsequent questions, feedback, explanations, or learning activities.
It is a simple form of adaptation, but adaptive learning can be more sophisticated. The system can consider which concept is being assessed, patterns across previous interactions, possible prerequisite gaps, and whether the learner can apply knowledge in different contexts.
Yes. A correct answer may result from knowledge, partial understanding, recognition, elimination of incorrect options or guessing. This is why mastery should ideally be estimated using a pattern of evidence rather than a single answer.
Medical imaging students must connect anatomy, physics, image quality, positioning, radiation protection, and clinical decision-making. A student can be strong in one area and struggle in another. Adaptive learning can help identify those differences and provide more targeted learning support.
No. The aim is to give educators better information about learner progress and knowledge gaps. Educators remain responsible for teaching, professional judgement and supporting students. Adaptive learning can provide additional evidence to help them decide where teaching time may be most useful.
No. The current demo is intentionally short and is designed to show the direction Virtual Medical Coaching is taking with adaptive learning. Future learning experiences can contain larger question banks, different interaction types, and more detailed learner and educator analytics.
The Virtual Medical Coaching fluoroscopic image quality adaptive learning demo is available at:
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