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1. Beyond Black-Boxing: Building Intuitions of Complex Machine Learning Ideas Through Interactives and Levels of Abstraction

   https://doi.org/10.1145/3501709.3544273  

   Broll, Brian; Grover, Shuchi; Babb, Derek (August 2022, The 2022 ACM Conference on International Computing Education Research V.2 (ICER ’22))

   Existing approaches to teaching artificial intelligence and machine learning often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe advanced ML topics, such as optimization and adversarial examples, can be learned by early high school age students given appropriate support. Our approach focuses on 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. 

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2. Cybersecurity Education in the Age of AI: Integrating AI Learning into Cybersecurity High School Curricula

   https://doi.org/10.1145/3545945.3569750  

   Grover, Shuchi; Broll, Brian; Babb, Derek (March 2023, 54th ACM Technical Symposium on Computer Science Education)

   Artificial Intelligence (AI) and cybersecurity are becoming increasingly intertwined, with AI and Machine Learning (AI/ML) being leveraged for cybersecurity, and cybersecurity helping address issues caused by AI. The goal in our exploratory curricular initiative is to dovetail the need to teach these two critical, emerging topics in highschool, and create a suite of novel activities, 'AI & Cybersecurity for Teens' (ACT) that introduces AI/ML in the context of cybersecurity and prepares high school teachers to integrate them in their cybersecurity curricula. Additionally, ACT activities are designed such that teachers (and students) build a deeper understanding of how ML works and how the machine actually "learns". Such understanding will aid more meaningful interrogation of critical issues such as AI ethics and bias. ACT introduces core ML topics contextualized in cybersecurity topics through a range of programming activities and pre-programmed games in NetsBlox, an easy-to-use block-based programming environment. We conducted 2 pilot workshops with 12 high school cybersecurity teachers focused on ACT activities. Teachers' feedback was positive and encouraging but also highlighted potential challenges in implementing ACT in the classroom. This paper reports on our approach and activities design, and teachers' experiences and feedback on integrating AI into high school cybersecurity curricula. 

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3. Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning (Work in Progress)

   Xu, G.; Zabner, D.; Cross, J.; Nadler, D.; Coxon, S.; Engelkenjohn, K. (June 2023, ASEE annual conference exposition proceedings)

   Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical. 

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4. Bringing Social Computing to Secondary School Classrooms

   https://doi.org/10.1145/3626252.3630795  

   Bolante, Kianna; Chen, Kevin; Chen, Quan Ze; Zhang, Amy (March 2024, ACM)

   Social computing is the study of how technology shapes human social interactions. This topic has become increasingly relevant to secondary school students (ages 11--18) as more of young people's everyday social experiences take place online, particularly with the continuing effects of the COVID-19 pandemic. However, social computing topics are rarely touched upon in existing middle and high school curricula. We seek to introduce concepts from social computing to secondary school students so they can understand how computing has wide-ranging social implications that touch upon their everyday lives, as well as think critically about both the positive and negative sides of different social technology designs. In this report, we present a series of six lessons combining presentations and hands-on activities covering topics within social computing and detail our experience teaching these lessons to approximately 1,405 students across 13 middle and high schools in our local school district. We developed lessons covering how social computing relates to the topics of Data Management, Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and Bias, Misinformation, and Online Behavior. We found that 81.13% of students expressed greater interest in the content of our lessons compared to their interest in STEM overall. We also found from pre- and post-lesson comprehension questions that 63.65% learned new concepts from the main activity. We release all lesson materials on a website for public use. From our experience, we observed that students were engaged in these topics and found enjoyment in finding connections between computing and their own lives. 

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5. Hands-on immunology: Engaging learners of all ages through tactile teaching tools

   https://doi.org/10.3389/fmicb.2022.966282  

   Harris, Felix R.; Sikes, Michael L.; Bergman, Michael; Goller, Carlos C.; Hasley, Andrew O.; Sjogren, Caroline A.; Ramirez, Melissa V.; Gordy, Claire L. (August 2022, Frontiers in Microbiology)

   Ensuring the public has a fundamental understanding of human–microbe interactions, immune responses, and vaccines is a critical challenge in the midst of a pandemic. These topics are commonly taught in undergraduate- and graduate-level microbiology and immunology courses; however, creating engaging methods of teaching these complex concepts to students of all ages is necessary to keep younger students interested when science seems hard. Building on the Tactile Teaching Tools with Guided Inquiry Learning (TTT-GIL) method we used to create an interactive lac operon molecular puzzle, we report here two TTT-GIL activities designed to engage diverse learners from middle schoolers to masters students in exploring molecular interactions within the immune system. By pairing physical models with structured activities built on the constructivist framework of Process-Oriented Guided Inquiry Learning (POGIL), TTT-GIL activities guide learners through their interaction with the model, using the Learning Cycle to facilitate construction of new concepts. Moreover, TTT-GIL activities are designed utilizing Universal Design for Learning (UDL) principles to include all learners through multiple means of engagement, representation, and action. The TTT-GIL activities reported here include a web-enhanced activity designed to teach concepts related to antibody–epitope binding and specificity to deaf and hard-of-hearing middle and high school students in a remote setting and a team-based activity that simulates the evolution of the Major Histocompatibility Complex (MHC) haplotype of a population exposed to pathogens. These activities incorporate TTT-GIL to engage learners in the exploration of fundamental immunology concepts and can be adapted for use with learners of different levels and educational backgrounds. 

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