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1. Cybersecurity Education with Generative AI: Creating Interactive Labs from Microelectronic Fundamentals to IoT Security Exploitation

   Badal, K; Yuan, X; Fu, H; Hanna, D; Gorski, J (November 2025, Journal of the Colloquium for Information System Security Education)

   Creating engaging cybersecurity education materials typically requires months of development time and specialized expertise. This paper describes how we used generative AI to address this challenge. We utilized Claude AI to generate a complete interactive platform that teaches students basic microelectronics through IoT hacking. Through iterative prompting, we generated more than 15,000 lines of functional code, including interactive visualizations, Python security tools, and gamified quizzes with real-time leaderboards. The curriculum guides students through the evolution of computing—from vacuum tubes to modern IoT devices—then helps them apply this foundation to discover real vulnerabilities. We implemented this platform at a GenCyber summer camp with 40 participants, where students identified actual security issues in AmpliPi audio systems—opensource network audio devices designed for multi-room audio distribution—including password weaknesses and denial of service flaws. The entire development process took only three weeks instead of the typical several months. The AI produced quality educational content, although we reviewed everything for technical accuracy and ethical considerations. During the camp, students remained engaged through competitive elements and hands-on labs, learning both theoretical concepts and practical skills. The students used AI-generated tools, including working implementations of SlowLoris and dictionary attacks, to test real systems. Our experience demonstrates that generative AI can efficiently create effective cybersecurity education materials that remain technically current. All materials are publicly available on GitHub for educational use. This approach could help educators stay on track with the rapidly evolving technology despite traditional curriculum development constraints. 

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2. Gamifying Virtual Exploration of the Past 350 Million Years of Vertebrate Evolution

   https://doi.org/10.3389/feduc.2022.836783  

   Mead, Chris; Bruce, Geoffrey; Taylor, Wendy; Buxner, Sanlyn; Anbar, Ariel D. (March 2022, Frontiers in Education)

   Surviving Extinction is an interactive, adaptive, digital learning experience through which students learn about the history of vertebrate evolution over the last 350 million years. This experience is self-contained, providing students with immediate feedback. It is designed to be used in a wide range of educational settings from junior high school (∼12 years old) to university level. Surviving Extinction ’s design draws on effective aspects of existing virtual field trip-based learning experiences. Most important among these is the capacity for students to learn through self-directed virtual explorations of simulated historical ecosystems and significant modern-day geologic field sites. Surviving Extinction also makes significant innovations beyond what has previously been done in this area, including extensive use of gamified elements such as collectibles and hidden locations. Additionally, it blends scientifically accurate animations with captured media via a user interface that presents an attractive, engaging, and immersive experience. Surviving Extinction has been field-tested with students at the undergraduate, high school, and pre-high school levels to assess how well it achieves the intended learning outcomes. In all settings we found significant gains pre- to post-activity on a knowledge survey with medium to large effect sizes. This evidence of learning is further supported with data from the gamified elements such as the number of locations discovered and total points earned. Surviving Extinction is freely available for use and detailed resources for educators are provided. It is appropriate for a range of undergraduate courses that cover the history of life on Earth, including ones from a biology, ecology, or geology perspective and courses for either majors or non-majors. Additionally, at the high school level, Surviving Extinction is directly appropriate to teaching adaptation, one of the disciplinary core ideas in the Next Generation Science Standards. Beyond providing this resource to the educational community, we hope that the design ideas demonstrated in Surviving Extinction will influence future development of interactive digital learning experiences. 

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3. A multimodal approach to support teacher, researcher and AI collaboration in STEM +C learning environments

   https://doi.org/10.1111/bjet.13518  

   Cohn, Clayton; Snyder, Caitlin; Fonteles, Joyce_Horn; T_S, Ashwin; Montenegro, Justin; Biswas, Gautam (September 2024, British Journal of Educational Technology)

   AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross adifficulty threshold,and (2) thepoint of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topicCollaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI.What this paper addsWe extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration.We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom.From our discussions with the teacher, we define two types ofinflection pointsto address students' STEM+C difficulties—thedifficulty thresholdand theintervention point—and discuss how thefeedback latency intervalseparating them can inform educator interventions.We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline.Implications for practice and/or policyOur case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback.Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment.Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula. 

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4. Enhancing student experience and learning with iterative design in an intelligent educational game

   https://doi.org/10.1111/bjet.13526  

   Hare, Ryan; Ferguson, Sarah; Tang, Ying (October 2024, British Journal of Educational Technology)

   With increasing interest in computer‐assisted educa‐ tion, AI‐integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first‐year undergraduate student responses to an intelligent educational system that applies multi‐agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer‐assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom. Practitioner notesWhat is already known about this topicAI‐integrated systems show promise for personalizing learning and improving student education.Existing research has shown the value of personalized learner feedback.Engaged students learn more effectively.What this paper addsStudent opinions of and responses to an HCI‐based personalized educational system.New insights for practical implementation of AI‐integrated educational systems informed by years of student observations and system improvements.Qualitative insights into system design to improve human–computer interaction in educational systems.Implications for practice and/or policyActionable design principles for computer‐assisted tutoring systems derived from first‐hand student feedback and observations.Encourage new directions for human–computer interaction in educational systems. 

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5. AI-human partnership to help students write science explanations

   https://doi.org/10.22318/icls2024.782327  

   Puntambekar, S; Gnesdilow, D; Passonneau, RJ; Kim, C (June 2024, Proceedings of the 4th Annual Meeting of the International Society of the Learning Sciences 2024)

   Hoadley, C; Wang, XC (Ed.)

   In this paper, we present a case study of designing AI-human partnerships in a realworld context of science classrooms. We designed a classroom environment where AI technologies, teachers and peers worked synergistically to support students’ writing in science. In addition to an NLP algorithm to automatically assess students’ essays, we also designed (i) feedback that was easier for students to understand; (ii) participatory structures in the classroom focusing on reflection, peer review and discussion, and (iii) scaffolding by teachers to help students understand the feedback. Our results showed that students improved their written explanations, after receiving feedback and engaging in reflection activities. Our case study illustrates that Augmented Intelligence (USDoE, 2023), in which the strengths of AI complement the strengths of teachers and peers, while also overcoming the limitations of each, can provide multiple forms of support to foster learning and teaching. 

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