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1. AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum

   https://doi.org/10.1109/FIE61694.2024.10893159  

   Tadimalla, Sri Yash; Maher, Mary Lou (October 2024, IEEE)

   This research-to-practice paper presents a curriculum, “AI Literacy for All,” to promote an interdisciplinary under-standing of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools such as ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. The non-technical component of AI literacy has often been limited to social and ethical implications, privacy and security issues, or the experience of interacting with AI. In AI Literacy for all, we emphasize a balanced curriculum that includes technical as well as non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI. This approach not only broadens students' perspectives but also prepares them to think critically about integrating AI into their future professional and personal lives. 

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2. 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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3. 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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4. 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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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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