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1. AI literacy as a core component of AI education

   https://doi.org/10.1002/aaai.70007  

   Tadimalla, Sri Yash; Maher, Mary Lou (June 2025, AI Magazine)

   Abstract As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio‐technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1‐credit general education AI literacy course (primarily for freshmen and first‐year students from various majors), a 3‐credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross‐cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI‐related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio‐technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI. 

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2. Learning with Leadership: Perspectives from a Statewide Research-Practice Partnership Focused on Equity-Oriented Computing Professional Development for K-12 Administrators

   Ryoo, J.J.; Flapan, J.; Hadad, R.; Margolis, J.; Amalong, J.; Aranguren, L.; Campos, E.; Knudson, J.; Lee, M.; Zuchowicz, M. (January 2021, Proceedings of the Annual SIGCSE Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   While the Computer Science for All (CS for All) movement has led to valuable advancements in equity-oriented curricula and teacher professional development, critical questions remain about how to build the capacity of school and district leadership to implement equitable CS education. How can administrators be supported in decision-making practices so that their school policies facilitaterather than hinder CS for All efforts? Our statewide research-practice partnership (RPP)—representing fourteen different urban, rural, and suburban local education agencies (LEAs) across the state—sought to tackle this question by collaboratively developing, implementing, and iteratively improving upon a guide and workshop for administrators seeking to bring CS into their schools, as well as a multi-stakeholder PD for teachers, counselors, and principals. Both researcher and administrator panelists will share how we built an RPP, lessons learned in creating administrator resources, and details about effective multi-stakeholder PD. In line with SIGCSE’s 2021 call, this panel will inform audience members about how RPPs and a focus on leadership can expand computing education opportunities for more students in K-12 public schools. 

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3. Graduate Programs in CS Education: Why 2020 is the Right Time

   https://doi.org/10.1145/3328778.3372517  

   Hambrusch, Susanne; Peterfreund, Alan; Yadav, Aman; Ko, Amy (February 2020, SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   Opportunities for training CS K-12 pre-service and in-service teachers, research in CS Education, and career pathways for PhDs/EdDs in CS education are happening, but often in an uncoordinated way. We advocate that now is the right time for CS and Education to collaborate on developing new joint degree programs in Computer Science Education and to explore joint faculty appointments. High undergraduate enrollment in computing programs and the increasing interest in CS courses from non-majors represent a unique opportunity for starting successful programs. As more of CS undergraduates are undergraduate TAs and see teaching and learning from a non-learner perspective, their interest in education has also increased. The growing interest in CS education, including the need for effecting CS teaching at both K-12 and the undergraduate level, provide interesting job opportunities for CS education researchers. As CS departments develop new undergraduate degree programs and scale class sizes, research on questions like How do we teach effectively computing to different audiences? How can we assess CS learning? What are culturally responsive pedagogies? is important. To answer many of these and related questions, CS departments should be actively engaged in CS Education research, from training graduate students in interdisciplinary programs to research programs. This BOF will provide a platform for the discussion on what such graduate programs – from certificate to a PhD – can and should look like, what challenges exist to creating them, and how students with different backgrounds should get trained in the relevant foundations of CS and Education. 

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4. K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy

   https://doi.org/10.1007/s40593-023-00358-x  

   Wang, N; Lester, J (June 2023, International journal of artificial intelligence in education)

   The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning & Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment. 

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5. Facilitating Advanced Manufacturing Technicians' Readiness in the Rural Economy: A Competency-based Deductive Approach

   Jones, Faye R.; Mardis, Marcia A.; Prajapati, Priyanka; Kowligi, Pallavi R. (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   While rural manufacturing job availability is growing throughout the country, rural communities often lack skilled workers. Thus, it is imperative for employers to validate needed new professional competencies by understanding which skills can be taught on-the-job as well as the knowledge and abilities best gained through classroom learning and experiential learning opportunities. This enhanced understanding not only benefits employers’ hiring practices, but also it can help Career and Technical Education (CTE) programs improve curricula and expand learning opportunities to best meet students’ and employers’ needs. In this study, we triangulated industry competency model content with rural employer perspectives on new advanced manufacturing (AM) professionals’ desired competencies (i.e., the level of skill sophistication in a particular AM work area). To extract competencies for entry-level AM rural jobs, we used a deductive approach with multiple methods. First, we used Natural Language Processing (NLP) to extract, analyze, and compare the U.S. Department of Labor’s AM 2010 and 2020 Competency Models because they reflect the levels and topics AM industry professionals nationally reported as technician needs. Then, we interviewed 10 rural AM employers in North Florida to capture their perceptions of the most important competencies for new middle-skill technicians. Interview transcripts were also processed using NLP to extract competency levels and topics; we compared this output to the AM Competency Model analysis results. We deduced that the most critical competencies identified by rural AM employers required direct classroom instruction, but there was a subset of skills obtainable through on-the-job training or other experiential learning. This study, with the goal of addressing employee shortages and increasing the number of technicians ready for the workforce, has implications for rural community colleges’ AM programs curricula and the role of experiential learning. 

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