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Artificial
Intelligence

Overview

Artificial Intelligence (AI) utilizes programming algorithms to simulate thought processes and reasoning that produce behavior similar to humans. A successful implementation of AI could be tested using a Turing Test approach, in which a human interacts with an interface that could have either a human or computer on the other end. The test is considered successful if the human is unable to determine whether there is a computer or a human on the other end.

AI is made up of intelligent agents that perform functions within an environment. Intelligent agents have sensors that are used to perceive changes in the environment and utilizes effectors to produce actions on the environment. The agent is typically programmed to perceive certain changes in the environment and can learn from those perceptions by testing its actions on the environment. In this manner complete AI entities are typically made up of multiple intelligent agents that have specific functions within its own domain.

The application of AI within e-learning can produce the potential of creating realistic environments with which students can interact. The student essentially would interact with the intelligent agents which in turn effect or perceive changes in the simulated environment. The intelligent agents would then communicate perceived changes in the environment back to the student who then makes decisions based upon their own perceptions of the environment.

For example, in technical troubleshooting applications intelligent agents could be used to produce an environment in which the learner must diagnose a problem. The learner is provided with all the resources that would normally be available on the job, such as a voltmeter, oscilliscope, tools, clipboard, etc. The User Interface would provide access to these resources, but not provide any clue as to the order in which each resource should be used. The resources could be simple objects that simply interact with the environment, i.e. the voltmeter would simply receive the voltage measurement and display the results to the learner. The intelligent agents would be programmed to manipulate the environment based on the usage of the resources, and to monitor how the resources are used in order to measure performance. This is different than just having the student follow a procedure to diagnose a problem because the simulated environment could react differently to different approaches to the problem. The learner is not given any indication as to what to do next, so it truly monitors how the learner reacts to different situations and how the student adapts to unexpected output.

This produces the most realistic environment in which a student can safely learn technical skills as well as soft skills such as problem solving. Such an environment provides a valuable opportunity to acquire experience before applying in real world situations.

Applying AI to new types of situations still requires detailed programming which makes it expensive to implement in certain e-learning applications. It can be worthwhile however if a framework can be constructed that allows adaptability to multiple situations simply by changing content, but leaving the intelligent agents themselves intact.

AI does have the potential to be applied in learning programs that can accelerate the learning process.