THE QUEST GAME-FRAME: BALANCING SERIOUS GAMES FOR INVESTIGATING PRIVACY DECISIONS

Digitalisation permeates all areas of social life. The use of digital games in research settings to analyse social phenomena is thereby no exception. However, games that can successfully achieve research ob- jectives and at the same time create an engaging experience require thoughtful balancing. When inves- tigating decision-making, for example, asking players directly about their reasoning in the game is breaking the game flow and prone to distorting influences from the game experience. This paper presents the design science (DS) process of a quest-based game-frame (QGF) oriented on the investigation of privacy decision-making. The design-empirical cycle of the QGF is outlined and applied to design two privacy decision scenarios for investigating reflection tendencies. The conducted binational experiment reflects the behaviour of 78 educators, university students and high-school students from Austria and Norway in online ordering security and fake news sharing while monitoring the game flow. Results demonstrate the potential of the QGF for unobtrusively investigating privacy decisions while maintaining high fluency of performance. Significant differences between educators and high-school students are found in time spent for reflection before making online security decisions. Additionally, Norwegian high-school students show a low awareness when deciding on real/fake news sharing

Pedagogical agents: Influences of artificially generated instructor personas on taking chances

Educational institutes are currently facing the new normality that an ongoing pandemic situation has brought to teaching and learning. Distributed learning with content that blends over several platforms and locations needs to be created with didactic expertise in a feasible manner. At the same time, the possibilities for creating and distributing digital content have developed rapidly. Advanced computing supports the creation of artificial images, natural speech, and even natural-looking but non-existent persons. Since such generative content is often also published under a Creative Commons license, it presents as viable option for designing learning content, assignments, or instructions for tasks. However, there is still limited evidence on how, for example, generated pedagogical agents (tutors) influence behaviour and decisions. This study investigated the influences of artificially generated tutor personas in a decision-making task distributed internationally on the Google Play store. The field experiment extended the balloon analogue risk task (BART) with instructions from generated persona photographs to evaluate potential influences on risk-taking behaviour. In a between-subject design, either a female tutor, a male tutor, or no tutor picture at all was presented during the task. The results (N=74) show a higher risk propensity when displaying a male artificial instructor compared to a female instructor. Participants also proceed with greater caution when instructed by a female tutor as they reflect longer before initiating the next step to pump up the balloon. Further lines of research and experiences from the distribution of an investigative instruction app on Google Play are summarised in the conclusive implications