An artificial intelligence framework for feedback and assessment mechanisms in educational Simulations and Serious Games

Abstract

Simulations and Serious Games are powerful e-learning tools that can be designed to provide learning opportunities that stimulate their participants. To achieve this goal, the design of Simulations and Serious Games will often include some balance of three factors: motivation, engagement, and flow. Whilst many frameworks and approaches for Simulation and Serious Game design do provide the means for addressing a combination of these factors to some degree, few address how those factors might be affected by the presence of an out-of-game tutor. It is the position of some researchers that the presence of real-world tutors in a Simulation or Serious Game experience can be shown to have a detrimental effect on motivation, engagement, and flow as a continuously changing state for the participant from in-game to out-of-game breaks immersion.

The focus of this study was to develop a framework for the design of Simulations and Serious Games that could provide the means to mitigate some of these identified negative effects of real world tutor. The framework itself, referred to as the Wrongness Framework, uses artificial intelligence techniques and practices to provide internal feedback and assessment to the participant as a foundation for the creation of a rudimentary in-game tutor.

To achieve this goal it was necessary to develop the Wrongness Framework to include not only the findings of other scholars and researchers on the topic of feedback and assessment but also to introduce original refinements to existing artificial intelligence mechanisms. To test the abilities of the Wrongness Framework it was applied to two unique case studies each with a different purpose and scope. The first, the AdQuest case study, was a graphic design Serious Game scenario testing the ability of the Wrongness Framework's assessment mechanisms by having 102 postgraduate design students submit graphics for a luxury brand advertisement. These graphics were then assessed by the Wrongness Framework against expectations found in the Wrongness Framework's Intelligent System Knowledge Bank. The students were then surveyed for their responses to their assessments and individual rating scores for each design were taken. The second case study, Promasim, explored the possibilities of feedback tone and efficacy for non-player characters in a project management simulation. This was achieved with the use of expert interviews by both academics and working professionals to provide the information of experienced project managers to develop experiential interaction events for the Simulation.

Despite the results of these case studies a full case for the success of the Wrongness Framework could not be made. However, many of the identified challenges for the Wrongness Framework were met and, as such, a case can be made that an adequate foundation for the framework has been successful and has provided the case for further refinement.

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ORCID for Ashok Ranchhod: orcid.org/0000-0003-4269-8825

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