Biblio

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

Cybersecurity competitions have been shown to be an effective approach for promoting student engagement through active learning in cybersecurity. Players can gain hands-on experience in puzzle-based or capture-the-flag type tasks that promote learning. However, novice players with limited prior knowledge in cybersecurity usually found difficult to have a clue to solve a problem and get frustrated at the early stage. To enhance student engagement, it is important to study the experiences of novices to better understand their learning needs. To achieve this goal, we conducted a 4-month longitudinal case study which involves 11 undergraduate students participating in a college-level cybersecurity competition, National Cyber League (NCL) competition. The competition includes two individual games and one team game. Questionnaires and in-person interviews were conducted before and after each game to collect the players' feedback on their experience, learning challenges and needs, and information about their motivation, interests and confidence level. The collected data demonstrate that the primary concern going into these competitions stemmed from a lack of knowledge regarding cybersecurity concepts and tools. Players' interests and confidence can be increased by going through systematic training.