Here are some tips for teaching expert learners.
As employees and learners gain experience performing tasks and learning information, they develop mental representations known as mental models or a schema. These mental models are used by individuals to drive performance, make decisions and interpret a situation. These models are not necessarily completely accurate understandings but they are useful representations of how the elements of a situation, concept or idea interact with one another and what outcomes may be anticipated. As people learn and continue to perform a task, they develop efficient and flexible models that are determined by their experience and what they need to do. 
While these mental models are overwhelmingly helpful, at times, they can work against a more experienced learner. For example, in a previous blog entry I noted that expert chess players have access to over 50,000 configurations of Chess pieces on a board and can easily remember where chess pieces are mid-game because of these patterns. However, when presented with a completely randomized pattern of chess pieces outside of the 50,000 configurations possible, it turns out that novice chess players could more easily memorize and recall the position of the chess pieces. This is what is known as “Expertise Reversal Effect.” 
The expertise reversal effect indicates that certain instructional approaches that work well for novices will actually suppress learning for an expert in the subject matter. So the question becomes, what is the best method for teaching experts in a certain domain of knowledge?
First, don’t overload experts with extraneous information, Ruth Clark points to a study where novice learners needed HIgh Coherent text to understand a subject but more experienced learners need Low Coherent text. Low Coherent text is text with little redundancy, no preview sentences and no extra explanations. The results seem to be tied to the need to reduce cognitive load. Experts already know the schema or mental model and don’t need that information. 
Provide information to experts that contains the essentials without a great deal of extra information.
Next, provide expert learners with more problem-based (or challenge-basd) learning rather than worked out examples. Let the experts apply their knowledge to novel situations to predict and anticipate the outcome. The more varied the examples and situations, the more they will have to apply their schema to solve the problem. Additionally, present something outside their schema to alert them to the fact that their expertise may actually create blind spots in their ability to see problems as indicated above in the chess example.
Provide a problem-set that experts need to work through and present information outside the schema to reveal gaps in the experts ability. Most of the time, experts are interested in the exceptions and not the rules or every day.
Finally, when teaching executives or highly experiences managers, treat the subject as a “scale up” and not a new topic.
For example, if you tell a mid-level executive you are going to teach them about leadership, their reaction is “hey I already know about leadership, that’s why I am a where I am in this organization.” But if you position it differently, for example, “you are going to learn to scale your leadership abilities from leading 100 people domestically to managing 1000 across the globe” then the mid-level executive is more likely to focus on the learning experience because it is something new to them. Position learning events to experienced individuals as a “scale up” to their existing skill set.
- Don’t overload experts with extraneous information. Keep to critical information they need to know. (no history lesson, no interesting stories, quick and to the point, they know the history and stories)
- Use a problem-based or challenge-based approach to designing and delivering the learning event (anyone thinking “game-based approach”?.
- Position learning events as a “scale up” to an existing skill set.
- Spend time on the exceptions to the schema and point out possible flaws in existing schema–things the experts might miss because they don’t look outside their schema.
 Clark, R., Nguyen, F. & Sweller, J. (2006) Efficiency in Learning: Evidence-based guidelines to manage cognitive load. Pfeiffer. Chapters 10 and 11.
 Gagne, R. & Glasser, R. (1987) Foundations in Learning Research. In Instructional Technology: Foundations. Eds. Gagne. Published by Lawrence Erlbaum Associates. London. Pages 68-74.