The concept of Artificial Intelligence (AI) was first described by John McCarthy in 1956, as "the science and engineering of making intelligent machines". Research in AI involves producing machines to automate tasks requiring decision making. It is easy to imagine a future where computers do many more “intelligent” things than they currently do. That said, while the overall adoption of AI is still quite modest more than 50 years in, it is reasonably widespread, and it’s techniques are very applicable to eLearning.  In eLearning, it is relatively straightforward to modify the path of learners based on their success with sections of content. For example, review materials may be automatically presented to persons who do not perform well on a quiz, and they may not be able to continue to subsequent sections until they pass a quiz. The skills of students can be analyzed over time, so that some students may be initially steered down a unique path based on past measurements of aptitude. Naturally, the primary limitation is the practicality of devoting resources to such efforts, and as a result, most available eLearning does not incorporate AI techniques.

It is worthwhile for Instructional Designers to spend some time becoming familiar with the science of AI, as a means to open themselves to the possibilities of developing adaptive training through the abilities of computers.

On concept related to AI that is worth developing an appreciation of is “fuzzy logic.”  Humans excel at fuzzy logic, which can be thought of as decision making that is highly intuitive. Computers, on the other hand, are generally poor at fuzzy logic (though researchers in AI are continuously working to close the gap). A good example of this is the ability of humans to read text with many typographical errors, phonetic spelling, poor grammar, or sloppy handwriting, none of which are easily done by a computer. The excellence of people at fuzzy logic, and of computers at computational tasks, is a powerful combination in training design (and elsewhere).

The concept of Artificial Intelligence (AI) was first described by John McCarthy in 1956, as "the science and engineering of making intelligent machines". Research in AI involves producing machines to automate tasks requiring decision making. It is easy to imagine a future where computers do many more “intelligent” things than they currently do. That said, while the overall adoption of AI is still quite modest more than 50 years in, it is reasonably widespread, and it’s techniques are very applicable to eLearning.  In eLearning, it is relatively straightforward to modify the path of learners based on their success with sections of content. For example, review materials may be automatically presented to persons who do not perform well on a quiz, and they may not be able to continue to subsequent sections until they pass a quiz. The skills of students can be analyzed over time, so that some students may be initially steered down a unique path based on past measurements of aptitude. Naturally, the primary limitation is the practicality of devoting resources to such efforts, and as a result, most available eLearning does not incorporate AI techniques.

It is worthwhile for Instructional Designers to spend some time becoming familiar with the science of AI, as a means to open themselves to the possibilities of developing adaptive training through the abilities of computers.

On concept related to AI that is worth developing an appreciation of is “fuzzy logic.”  Humans excel at fuzzy logic, which can be thought of as decision making that is highly intuitive. Computers, on the other hand, are generally poor at fuzzy logic (though researchers in AI are continuously working to close the gap). A good example of this is the ability of humans to read text with many typographical errors, phonetic spelling, poor grammar, or sloppy handwriting, none of which are easily done by a computer. The excellence of people at fuzzy logic, and of computers at computational tasks, is a powerful combination in training design (and elsewhere).