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1. Biologically Inspired Design For High School Engineering Students (Work in Progress)

   https://doi.org/10.18260/1-2--42371  

   Alemdar, Meltem; Baptiste_Porter, Dyanne; Rehmat, Abeera; Helms, Michael; Towner, Alexandra; Moore, Roxanne; Rosen, Jeffrey; Varnedoe, Julia; Weissburg, Marc (June 2023, ASEE Conferences)

   NA (Ed.)

   Biologically inspired design (BID) has gained attention in undergraduate and graduate engineering programs throughout the United States, and more post-secondary institutions are beginning to implement it into their engineering curriculum [1], [2]. However, little has been done to introduce BID concepts more formally into the K-12 curriculum. Consequently, a research study funded by the National Science Foundation focused on developing a BID integrated engineering curriculum for high school students. The curriculum is designed to integrate BID into the engineering design process (EDP) by leveraging analogical design tools that facilitate a transfer of biological strategies to design challenges. This enables students to understand both the engineering problem and the biological system that could be used to inspire design solutions. In this paper, we describe students’ application of BID integration in the engineering design process and their experiences utilizing BID as they solve design challenges. The curriculum was pilot tested in two 9th grade engineering classrooms across two schools during Spring 2022. Data was collected from four groups of students (n=12) enrolled in the engineering courses across two schools. The study includes classroom observations, student artifacts, and student focus groups. We utilized qualitative content analysis, a descriptive approach to analyzing student data [3], [4], to uncover the meaning and presence of text, messages, images, and transcriptions of dialogues [4]. In this study, we aim to capture the evidence of students’ experiences and engagement with BID concepts. The preliminarily findings illustrate that student groups enjoyed BID activities presented in the curriculum as they promoted students’ exploration of biological systems. BID integration allowed students to view nature differently, which some students indicated they had not previously employed for their design solutions. Although some students mentioned BID activities that helped them during the brainstorming phase of the design process, they were unable to explain BID integration in their final design solutions, unless prompted by the teacher. Furthermore, across the student groups, students indicated that prototype and test was the most engaging stage of the EDP since during this stage they were able to test their designs. This research is novel in its focus on understanding high school students’ experiences with the integration of BID in engineering and has important implications for diversifying engineering in K-12 education. 

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2. Integrating Natural Language Processing in Middle School Science Classrooms: An Experience Report

   https://doi.org/10.1145/3626252.3630881  

   Katuka, Gloria Ashiya; Chakraburty, Srijita; Lee, Hyejeong; Dhama, Sunny; Earle-Randell, Toni; Celepkolu, Mehmet; Boyer, Kristy Elizabeth; Glazewski, Krista; Hmelo-Silver, Cindy; Mcklin, Tom (March 2024, Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE))

   With the increasing prevalence of large language models (LLMs) such as ChatGPT, there is a growing need to integrate natural language processing (NLP) into K-12 education to better prepare young learners for the future AI landscape. NLP, a sub-field of AI that serves as the foundation of LLMs and many advanced AI applications, holds the potential to enrich learning in core subjects in K-12 classrooms. In this experience report, we present our efforts to integrate NLP into science classrooms with 98 middle school students across two US states, aiming to increase students’ experience and engagement with NLP models through textual data analyses and visualizations. We designed learning activities, developed an NLP-based interactive visualization platform, and facilitated classroom learning in close collaboration with middle school science teachers. This experience report aims to contribute to the growing body of work on integrating NLP into K-12 education by providing insights and practical guidelines for practitioners, researchers, and curriculum designers. 

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3. Principles for AI Education for Elementary Grades Students

   https://doi.org/10.1145/3502717.3532143  

   Ottenbreit-Leftwich, Anne; Glazewski, Krista; Jeon, Minji; Jantaraweragul, Katie; Hmelo-Silver, Cindy; Scribner, Adam; Lee, Seung; Mott, Bradford; Lester, James (July 2022, 27th ACM Conference on Innovation and Technology in Computer Science Education)

   AI is beginning to transform every aspect of society. With the dramatic increases in AI, K-12 students need to be prepared to understand AI. To succeed as the workers, creators, and innovators of the future, students must be introduced to core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students present significant challenges, such as connecting to prior knowledge, and developing curricula that are meaningful for students and possible for teachers to teach. To lay the groundwork for elementary AI education, we conducted a qualitative study into the design of AI curricular approaches with elementary teachers and students. Interviews with elementary teachers and students suggests four design principles for creating an effective elementary AI curriculum to promote uptake by teachers. This example will present the co-designed curriculum with teachers (PRIMARYAI) and describe how these four elements were incorporated into real-world problem-based learning scenarios. 

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4. “What Makes ChatGPT Dangerous is Also What Makes It Special”: High-School Student Perspectives on the Integration or Ban of Artificial Intelligence in Educational Contexts

   https://doi.org/10.46328/ijte.651  

   Famaye, Tolulope; Bailey, Cinamon Sunrise; Adisa, Ibrahim; Arastoopour Irgens, Golnaz (March 2024, International Journal of Technology in Education)

   The emergence of ChatGPT, an AI-powered language model, has sparked numerous debates and discussions. In educational research, scholars have raised significant questions regarding the potential, limitations, and ethical concerns around the use of this technology. While research on the application and implications of ChatGPT in academic settings exists, analysis of the perspectives of high-school students are limited. In this study, we use qualitative content analysis to explore the perspectives of high-school students regarding the integration or ban of ChatGPT in their schools through the lens of the Technology Acceptance Model (TAM2). Data was sourced from students’ comments to a New York Times Learning Network article. Findings revealed that students' perceptions about integrating or banning ChatGPT in schools are influenced by their assessments of the technology’s usefulness, personal experiences, societal technology trends, and ethical considerations. Our findings suggest that student perspectives in this study align with those of educators and policymakers while also possessing unique perspectives that cater to their specific needs and experiences. Implications emphasize the significance of an inclusive decision-making process around the integration of AI schools in educational contexts, including students alongside other stakeholders. 

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5. AI Learning Priorities for All K-12 Students

   Twarek, Bryan; Koressel, Jacob; Gardner-McCune, Christina; Touretzky, David (May 2025, Computer Science Teachers Association)

   Rapid changes in artificial intelligence (AI) require changes in how and what is taught about AI to K-12 students. These changes will ensure that students are prepared to be smart consumers and competent creators of AI, as well as informed citizens. To meet this need, CSTA, in partnership with AI4K12, spearheaded the Identifying AI Priorities for All K-12 Students project. The project gathered experts – including teachers, researchers, administrators, and curriculum developers – to articulate priorities for AI education. This report summarizes the result of that effort. The project had four goals: 1. Identify priorities for AI learning across each K-12 grade band. As a result of a collaborative, iterative process, the project articulated five categories for AI learning: Humans and AI, Representation and Reasoning, Machine Learning, Ethical AI System Design and Programming, and Societal Impacts of AI. 2. Suggest updates to the AI4K12 Guidelines. Advances in generative AI necessitate updates to the AI4K12 Guidelines. This is especially true for Big Idea #4: Natural Interaction, since generative AI represents a substantial advance in the ability of AI to interact with humans. Similarly, generative AI raises many ethical questions relevant to Big Idea #5: Societal Impacts. 3. Advance the research agenda for K-12 AI education. Priorities for research in AI education include the importance of supporting teachers, promoting inclusive and student-centered pedagogies, developing appropriate tools, gaining a better understanding of AI’s impact on learning, and ensuring equity in AI education. 4. Share promising practices across the AI and CS education communities. Participants shared their work in AI education. While the practices described varied, there were some common themes. Concerns about ethics and responsible AI were foregrounded, and hands-on learning activities were featured prominently. Meeting the needs of all children was a key concern, with approaches and tools that are widely accessible as well as engaging for all students. As a result of these common themes, we offer related recommendations for AI curriculum and instruction. The report also includes an exploration of the tensions and challenges that emerged from the project, such as the difficulty of categorizing, organizing, and prioritizing learning content. Preparing students to succeed personally and professionally in a world powered by computing will require rigorous, high-quality, and equitable learning opportunities in AI education. This project sought to determine priorities for AI education for all students to learn as part of a robust foundation in computer science, as well as options for more comprehensive study of AI. Within and across these priorities, two themes stand out. First, all students need to explore the personal, societal, and environmental impacts – both positive and negative – of AI. Second, students need to develop a broad conceptual understanding of how AI works: a frequent refrain from the project’s participants was that students need to understand that “AI isn’t magic.” While implementing high quality AI education, at scale, for all students will be challenging, the work already undertaken by convening participants demonstrates that there are elements of a foundation in place, one that can be built upon to ensure that all students are prepared to flourish in a world powered by computing. 

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