General Manager
When I put a research reference beside a claim about learning, I want the reader to be able to tell exactly what travelled from the study into the claim. A finding about one intervention cannot become a promise about our product simply because both involve practice.
Practice-Native Learning is the methodology I am developing at Altaius around realistic decisions, consequences, evidence and feedback. Its research foundations predate the name. The task is to use those foundations carefully and test our own implementation.
Lacerenza and colleagues' 2017 meta-analysis found positive leadership-training effects across learning, transfer and results. Its moderator findings favoured needs analysis, feedback, practice within multiple methods and spaced sessions. It also favoured on-site, face-to-face delivery that was not self-administered. That last finding matters when interpreting the paper for digital learning. It does not validate an autonomous AI substitute. [1]
I take this as a reason to examine the whole programme. Who needs to improve which decision? What guidance will they receive? Where will they practise again? A simulation can be one component of a well-supported programme, rather than being asked to carry the entire development process.
McGaghie and colleagues reported an overall effect-size correlation of 0.71, with a 95% confidence interval of 0.65 to 0.76, for medical simulation with deliberate practice compared with traditional clinical education. Their focus was specific clinical skill acquisition. The result is not a 71% performance gain and cannot be carried across to leadership training as an expected effect. [2]
For our design work, this provides a reason to investigate structured practice with feedback. It leaves open the question of how well a particular leadership scenario, scoring approach and delivery programme will work for a particular group.
I would write a design brief with three separate entries: the published finding, the feature it motivates and the local test needed to evaluate that feature. This prevents a reference list from becoming decoration.
For example, a programme might include repeated negotiation attempts with a facilitator. Its local evaluation should examine whether learners improve on a new case, whether feedback points to relevant actions, and whether managers observe the intended behaviour afterwards. Replaying an identical conversation until its preferred wording is memorised would answer a narrower question.
The same discipline applies to the word deliberate. I would want a defined target, an appropriate challenge, informative feedback and a reason for the next attempt. Repetition without those features may be useful exposure, but I would not label it deliberate practice simply because the learner repeats an activity.
This is an illustrative design record, not a published intervention or an Altaius client result. Its value is that a reviewer can challenge the connection between the target, the exercise and the evidence.
None of these papers studied Practice-Native Learning under that name. They do not establish an Altaius completion rate, a financial return, a universal improvement percentage or equivalent outcomes across languages. Our methodology is research-informed; its particular implementations still require evaluation.
I want the reader to know both why we chose the design and what could show that it needs to change. That is a more useful foundation for a learning methodology than attaching a large research number to a claim it was never intended to support.
