Despite the appeal of such an approach, barriers do exist. The most basic form of scenario is a linear path, with any digression either terminal (“you failed, you’ll have to start again”) or inconsequential, where regardless of your action some external agent makes it all ok (“your boss manages to catch your report before you send it to the client.”). In either case, the experience is very limited and does not allow real exploration or constructivist learning.
More advanced scenarios require either a rich branching structure, or an underlying model to generate the requisite depth of interaction. There have been some such attempts made in the past that have suffered from high cost and long development time. Building detailed models of a domain is a very difficult and time-consuming process. In some cases, organizations have been able to take advantage of pre-existing models; this can be useful for some domains, but it doesn’t scale across content lines, even if the problems of licensing and adapting the interface were solved. More limited branching scenarios suffer from a limited lack of replay, and production costs have similarly overwhelmed the approach.
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What’s desired is a scalable process that can develop meaningful knowledge application across the content requirements, provide high replay value, and be developed on a cost-effective budget and time-schedule.
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