We started this innovation because we saw a real gap between students’ constant online activity and their understanding of online safety. Students found cybersecurity abstract and boring, while teachers cared deeply but lacked effective, engaging ways to teach it. Traditional lessons weren’t connecting—students needed hands-on, story-driven, visual experiences, not just theory. Our goal with SHIELD is to transform cybersecurity education from a lecture into an engaging, practical experience that actually builds awareness and changes behavior. In short, we started this innovation to make online safety matter to students and give teachers the tools to make it meaningful.
In practice, our innovation combines teacher-guided classroom sessions with AI-generated, narrative-based gamified challenges. Teachers receive targeted training to use Large Language Models (LLMs) to create age-appropriate, contextually relevant scenarios. The learning revolves around Sara, a fictional student who experiences cyberbullying, including exclusion, impersonation, and digital harassment.
Students engage in an AI-assisted escape room, working in collaborative groups to solve challenges embedded in digital artifacts like social media screenshots, deepfakes, and ethical dilemmas. Success depends not on memorization but on critical thinking, collaborative reasoning, emotional awareness, and ethical decision-making. Key topics such as recognizing digital threats, preserving online privacy, and responsible intervention are explored through these interactive narratives.
Beyond the classroom, students can access a self-paced version of the game to revisit scenarios independently, consolidate learning, and apply strategies at their own pace. This flexible approach encourages autonomy, supports different learning styles, and provides insight into how well knowledge is retained. Overall, the innovation transforms cybersecurity education into an immersive, engaging, and meaningful experience rather than a traditional lecture.
Our innovation has been spreading through a combination of hands-on teacher training and open sharing of resources. Educators first participate in workshops focused on using Large Language Models (LLMs) to design interactive storytelling, create learning challenges, and develop adaptive learning pathways. These workshops emphasize pedagogical principles to ensure AI-enhanced narrative gamification is meaningful and effective. After training, teachers create their own games, which are then shared as open educational resources, allowing other educators to adopt, adapt, and expand the approach. This model encourages organic growth, collaboration, and wider access to engaging cybersecurity education.
We have evolved our innovation by actively involving teachers as creators of their own gamified experiences using the SHIELD methodology. This approach empowers educators to design contextually relevant, narrative-driven cybersecurity challenges tailored to their students’ needs, rather than relying solely on pre-made content. By doing so, the SHIELD project has expanded from a single structured program into a flexible, scalable framework that supports teacher creativity, promotes adaptation to different learning environments, and encourages continuous innovation in cybersecurity education.
To try the SHIELD methodology, a teacher or educator should start by participating in a hands-on workshop or training session, where they learn how to design interactive, narrative-based cybersecurity challenges using the SHIELD approach. These workshops guide educators in creating story-driven, gamified learning experiences and show how to use tools like Large Language Models (LLMs) to develop contextually relevant scenarios. After the training, they can create their own games, tailored to their students, and access existing SHIELD materials and open educational resources to adapt or expand their activities. This process allows educators to implement engaging, practical cybersecurity lessons
If you just want to implement it, then try the resources on our web page: https://dig-ed.org
