REN-A.I.: A Video Game for AI Security Education Leveraging Episodic Memory

Abstract

Education in cybersecurity is crucial in the current society and it will be extended into the artificial intelligence (AI) area, called AI security, in the near future. Although many video games for education in cybersecurity have been designed, we have two problems for education in AI security: a helpful design of a video game for users to learn cybersecurity is still unclear and there is no game for AI security, to the best of our knowledge. In this paper, we design a video game for education in AI security, REN-A.I., to address the above problems. In designing REN-A.I., we built some hypotheses: simulating damage caused by attacks on AI and the effectiveness of their countermeasures through a video game helps a user to improve awareness of AI security with the episodic memory of the user itself. We focus on game scenarios and game functionalities to learn AI security with episodic memory in accordance with the above hypothesis. We conducted a questionnaire survey with 48 users to evaluate REN-A.I.. As a result, we confirm that both game scenarios and game functionalities are effective for learning with episodic memory. Specifically, 74% of users consider game scenarios effective, and 81% of users consider game functionalities effective. Our survey results have revealed two suggestions for beneficial design aspects in video games for education in cybersecurity. In particular, users who read game scenarios in REN-A.I. can learn AI security by the game more effectively than the other users. Furthermore, the functionality for accuracy deterioration due to attacks in REN-A.I. is effective even for users who do not read the game scenario. REN-A.I. is publicly available (https://www-infosec.ist.osaka-u.ac.jp/software/ren-ai/REN-AI(EN).html).