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1. A Socially Relevant Focused AI Curriculum Designed for Female High School Students

   https://doi.org/10.1609/aaai.v36i11.21546  

   Alvarez, Lauren; Gransbury, Isabella; Cateté, Veronica; Barnes, Tiffany; Lédeczi, Ákos; Grover, Shuchi (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students. 

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2. Beyond black-boxes: teaching complex machine learning ideas through scaffolded interactive activities

   Broll, Brian; Grover, Shuchi (January 2023, The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23))

   Existing approaches to teaching artifcial intelligence and machine learning (ML) often focus on the use of pre-trained models or fne-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our AI & Cybersecurity for Teens suite of curricular activities aimed at high school students and teachers. 

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3. Exploring Approaches for Teaching Cybersecurity and AI for K-12

   https://doi.org/10.1109/ICDMW60847.2023.00198  

   Cai, Yu; Youngstrom, Drew; Zhang, Wenbin (December 2023, IEEE International Conference on Data Mining workshops)

   As cybersecurity and AI become increasingly important, introducing these subjects to younger learners is critical. However, limited attention spans pose challenges for primary and secondary school students when learning these complex topics. By employing tools such as drones and Raspberry Pis, students can actively engage in learning cybersecurity and AI knowledge. This paper investigates the instructional benefits of Raspberry Pi and Drone platforms in K-12 education. The integration of hands-on activities through Raspberry Pi and Drone for Cybersecurity and AI content was implemented and evaluated at GenCyber summer camps in Michigan Technological University. The findings highlight the GoPiGo drone and Raspberry Pi as efficient instructional tools for teaching cybersecurity and AI to this age group. Additionally, hands-on tasks are essential for reinforcing understanding and maintaining student interest. 

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4. Using Mathematically-Grounded Metaphors to Teach AI-Related Cybersecurity

   Knijnenburg, Bart P.; Bannister, Nicole; Caine, Kelly (August 2021, IJCAI-21 Workshop on Adverse Impacts and Collateral Effects of Artificial Intelligence Technologies (AIofAI), Montréal, Canada)

   null (Ed.)

   This position paper describes our research project to improve middle school students’ use of security “best-practices” in their day-to-day online activities, while enhancing their fundamental understanding of the underlying security principles and math concepts that drive AI and cybersecurity technologies. The project involves the design and implementation of a time- and teacher-friendly learning module that can be readily integrated into existing middle school math curricula. We plan to deploy this module at a high-needs, rural-identifying middle school in South Carolina that serves underrepresented students 

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5. Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives

   ARIS, A. (January 2022, 2022 ASEE Annual Conference & Exposition)

   As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field. 

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