Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education
Mary Nusrat, Sarfuddin Bhuiyan, Gahangir Hossain

TL;DR
This paper introduces EQ-20CR, an explainable AI-based educational game using reinforcement learning to improve cybersecurity training by guiding users through adaptive, question-based explanations of security concepts.
Contribution
It presents a novel game-inspired framework that actively elicits minimal evidential facts for cybersecurity decisions, enhancing interactive learning and explainability.
Findings
The framework effectively guides users through cybersecurity concepts.
It adapts question difficulty based on user responses.
The approach improves understanding of security strategies.
Abstract
The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and adaptive approaches to learning, which are often required, are not provided in traditional learning methods. In this article, we present a new educational framework, "Learning to Explain Cybersecurity with Q20 Game", based on explainable AI (XAI), an educational game to enhance interactivity in learning. We propose a novel, game-inspired framework - the Explainable Q20 Cybersecurity Recommender (EQ-20CR), that learns to elicit the minimal set of evidential facts needed to justify cybersecurity defensive action. By casting "Why should I execute this mitigation?" as a 20 questions (Q20) game, a policy-based reinforcement-learning (RL) agent actively queries an environment until it can both (i) recommend the optimal security education and…
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