Built to reveal the reasoning behind decisions.
Most assessment tools stop at what learners can recall. Meandrix starts with what they decide.
How this got started.
I am a Registered Nurse, university educator, and Senior Fellow of the Higher Education Academy. My work has focused on professional education, clinical learning, and how students learn to make sound decisions in practice. Across that work, I kept coming back to one difficult question:
How do we know whether a learner can actually apply what they know?
Written exams test recall. OSCEs are powerful, but expensive, infrequent, and difficult to scale. Case studies have value. However, they often capture the final answer rather than the reasoning behind it. What was missing was a way to assess moment-by-moment judgement at scale, while still giving learners the personalised feedback they need to grow.
I went looking for a digital tool that could assess applied judgement at scale without losing authenticity, feedback, or educator control. I found tools for content, tools for exams, and tools for simple branching. But I could not find something built to follow how learners reason through an unfolding situation.
AI did not create that problem. It exposed it. Recall-based assessment was already limited in how well it showed whether learners could apply knowledge in context. When polished answers can be generated in seconds, and information can be accessed almost instantly, the final answer alone becomes weaker evidence of learning.
That gap is why I started building Meandrix. I wanted to create a platform for authentic, situational assessment where decisions carry weight, consequences matter, and feedback supports growth.
Dr. Gary Crowfoot
- Registered Nurse · PhD (Nursing)
- Senior Fellow of Advance HE
- Founder, Meandrix Education Systems Pty Ltd
The position that Meandrix takes.
Four principles that shape every product decision.
Assessment should go beyond recall.
Recall has a place. However, it is not sufficient to evidence professional capability. The stronger signal is whether a learner can apply knowledge in a situation that reflects the complexity of real practice. This means interpreting information, making decisions, responding to consequences, and explaining their reasoning.
AI did not break assessment. It exposed the limits of recall-based tasks.
The pressure created by generative AI is real and urgent. But assessment had significant challenges long before AI arrived. Recall-based tasks were always limited in how well they could show whether learners would apply knowledge in real-world situations AI just made these limitations impossible to ignore.
Integrity by design.
The first response to AI is often invigilation or proctoring. That may have a place. However, it cannot be the whole answer. Authentic, decision-based assessment that responds to learner choices is harder to outsource because the work is not just producing an answer. It is interpreting information, reasoning through options, acting on decisions, and responding to consequences. When additional integrity assurance is required, it should be proportionate, accessible, and aligned with local policy.
Educators stay in control.
Meandrix is not built around generic scenario libraries. The authoring is yours. The context is yours. The standards reflected on the page are the standards your discipline requires. AI accelerates the build, but educators retain full editorial control.
Want to talk?
I'd like to hear from you if anything here resonates, or if you're wondering whether Meandrix fits your context. Bring a question, or a situation from your practice and let' turn it into an Encounter.