This post was inspired by the publication “The Need For Learning Engineers (and Learning Engineering)“.
Below is our comment that represents our vision and our Learning Engineering practice:
In general, Engineering is supposed to apply well-defined scientific knowledge, formal/mathematical theories. Such theories have precisely formulated terms and definitions, axioms and theorems, laws, frameworks, models, methods, criteria, …
In contrast, Learning Sciences are still ill-defined, empirical, and not formal/mathematical theories. They are more like Art. Their main method is probe and trial, find what works, reuse and refine it.
Meanwhile, it is possible to upgrade this Art of Learning Sciences with Wisdom of Interdisciplinary Methodology and Systems Approach, Order of Exact Sciences, Power of Artificial Intelligence, and Intelligent Tutoring Solutions.
On this rock-solid foundation, we, at iTutorSoft, have defined a general model (framework/engine) of Adaptive Personalized Deep Learning for its specific practical applications with unlimited accuracy and efficacy at scale. For easy mass implementation of our solution we developed a cloud technological platform CLARITY. Now the users of our platform can do, what is called, Learning Engineering.