The use of games in the workplace is becoming increasingly common as a mechanism for improving behaviours.  A problem exists however in scenarios that are niche enough to require a novel and unique game environment to be created.  When such scenarios are substantial then the traditional gamification approach has struggled.

Customer or public facing roles often fall into this category, and they offer up such a multitude of possible outcomes that gamifying training for these scenarios is difficult.  Researchers at Bar-Ilan University in Israel think they’ve come up with a solution to the problem however.

They have recently published a paper in which they outline how Amazon’s Mechanical Turk was successfully used to create a wide and varied range of game based scenarios for a training simulation.

Their solution was a simple one.  They would ask participants on Mechanical Turk to answer a series of questions about their activities throughout a typical day.  Bonuses were offered to those who provided the most varied level of detail.

This data was then analysed so that it could be categorized according to parameters such as the time of each activity, how many people were engaged in it and various other demographic type information.

Doing this allowed the researchers to assign certain activities to certain personality types at certain periods of the day to form a wide range of narratives for a ‘person’.  This narrative was then tested by asking people to rate how authentic and coherent it appeared to be, and compared these ratings with similar ratings given to individual activities.  Pleasingly, there appeared to be no substantial difference in authenticity ratings for the two.

“We have begun integrating this approach within a scenario-based training application for novice investigators within the law enforcement departments to improve their questioning skills,” say the researchers.

Using their crowdsourced approach, the training managers within this department will now have a much wider range of scenarios with which to train investigators.  By crowdsourcing the generation of these scenarios, it solves a persistent conundrum for the gamification industry, as previously the generation of such a wide range of scenarios would have taken considerable time and resources.  The ability to do this both quickly and affordable therefore is a big step forward.