Personalized Educational Games - Developing agent-supported scenario-based training

Abstract

In many professions people make decisions under risky and stressful circumstances (e.g. the police force, military, or medical services). In such professions, practical training is often difficult, because erroneous decisions may result in grievous consequences. Yet learners need to gain experience in order to become good decision makers. Scenario-based training (SBT) is an effective training form to provide learners with the experience they need. During SBT, learners are presented with interactive role-playing exercises staged in a simulated environment. SBT has several limitations that prohibit learners from engaging in autonomous, personalized training. For instance, it requires considerable logistic and organizational efforts, and it is often difficult to adjust the scenario once it starts playing. To overcome these limitations, this research investigates a design for automated SBT: a Personalized Educational Game (PEG). A PEG employs a virtual environment to stage the training scenarios. In addition, a PEG employs artificial intelligence techniques to control the events in the simulated environment, as well as the behavior of the characters in the scenario. The scenarios in a PEG are tailored to learners’ individual needs, enabling them to develop their competencies at their own level and pace. For this, a PEG keeps track of relevant information about the learner, uses it to determine a suitable learning goal and difficulty level, and automatically generates a scenario that fits those needs. A PEG also supports and exploits the expertise of instructors. For instance, instructors are able to determine the learning goal, author a scenario, or define the behaviors of the characters. The human instructor is not excluded from or replaced by the PEG, but rather the PEG allows for variable levels of automation, depending on instructors’ preferences and availability. A PEG presents scenarios to the learner as a playable storyline in a virtual world where the learner can interact with virtual characters controlled by intelligent agents. A PEG can execute adaptations while the scenario is playing to ensure that the scenario remains suitable for the learner’s individual competency levels. After a learner has finished playing the scenario, a PEG aims to help learners in acquiring a deeper understanding of the events taking place in the simulated environment. For this, it encourages learners to reflect on their performance and to provide explanations for their decisions. If learners are unable to provide adequate explanations themselves, a PEG provides hints and additional instructional explanations. Most of the PEG’s functions (described above) have been evaluated in pilot studies. In these studies it was found that: (1) realtime adjustments are beneficial for the learning value of the scenarios; (2) the automatically generated scenarios were at least of a quality comparable to scenarios produced by laymen, yet not as good as the ones produced by domain experts; and (3) the quality of both the authoring process and the resulting scenarios increased as a result of a support function that used instructors’ selected learning goals to offer them suggestions for the contents of a scenario. Two remaining functions require additional testing.