Competitive Agents and Adaptive Difficulty Within Educational Video Games

Abstract

The entity players compete with is an important element of competitive mechanisms. However, this crucial element is barely investigated within educational video games, as educational psychology research focuses mainly on supportive role models (e.g., pedagogical agents, intelligent tutorial systems). Nevertheless, the influence on learning must be explored, as interaction with an opponent might accompany the whole learning process. Thus, an experiment was conducted comparing three forms of social competition with an emphasis on external valid applications. More specifically, playing against a human competitive agent, playing against an artificial competitive agent, and playing against an artificial leaderboard were compared. Additionally, methods of adaptive difficulty adjustment were included within these groups to harness the potential of artificial systems. The results of the study (N = 102) revealed a beneficial effect of adaptive mechanisms on learning performance and efficiency. Furthermore, a difference in play behavior could be observed. The participants reported a lowered feeling of shame, increased empathy, and behavioral engagement when facing competitive agents. In contrast, calculations revealed no significant impact on mental strains by potentially demanding social competitors. These results highlight the potential for the future development of adaptive game systems and help choose the optimal implementation of social competition within different educational video games.

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