As AI education expands in K-12 classrooms, there is a growing need to understand how AI learning experiences shape students’ motivational and affective orientations toward AI. Ability beliefs and intentions to persist are key constructs in AIED because they influence learners’ engagement in learning environments and continued participation. This study examines how a project-based AI learning experience affected these outcomes among middle school students. Ninety students completed a 10-hour AI module embedded within their science classes, in which they learned foundational AI concepts and designed conversational AI agents. Pre- and post-survey analyses showed increased ability beliefs but decreased intentions to persist in AI learning. This divergent pattern suggests a complex motivational response to hands-on AI instruction, indicating that gains in perceived competence do not necessarily translate into sustained persistence intentions. We discuss implications for the design of AI learning environments and for theoretical models of motivation and engagement in AI-supported learning contexts.

Museums play a critical role in promoting public understanding of emerging technologies like artificial intelligence (AI), but it is unclear what design features lead to learning about AI in museums. We contribute a design research exploration of how embodiment and creativity foster AI literacy in museum exhibits. We present design prototypes of three museum exhibits-DataBites, Knowledge Net, and LuminAIx-that aim to teach middle schoolers about AI. We present results from a qualitative analysis of an in-museum study in which we examined participants' understanding of and interest in AI through interviews and video recordings. Our findings illuminate how creativity fosters interest in AI and how different forms of embodiment contribute to learning about AI. We recommend that AI museum exhibits utilize creative and personally relevant activities to engage middle schoolers, support hybrid conceptualizations of AI, and leverage tangible interaction to make AI concepts approachable.

Science undergraduate students benefit from gaining computing skills due to the growing overlap between scientific discovery and computational methods. One approach is to encourage students majoring in science to pursue a computing minor; however, only a few seem to undertake this endeavor. Financially disadvantaged students face additional barriers that may further deter them from this pursuit. Unfortunately, there is limited research on the benefits, limitations, and barriers related to earning a computing minor. This study aims to explore the perspectives of undergraduate college students from the United States in a scholarship program designed to aid low-income science majors in completing an information science and technology (IS&T) minor. Using an exploratory qualitative research approach, semi-structured interviews were conducted with seven students in a scholarship program. Based on the findings, three main recommendations for program stakeholders are proposed. First, explicitly explain the unique advantages of gaining computing knowledge for science majors. Second, ensure students have a foundational understanding of computing and related study skills. Third, guide students in strategically planning their course sequence to optimize their time and workload.

In a partnership between four universities, the Georgia Department of Education, and nine Georgia school districts, we developed a 9‐week middle school elective called “Living and Working with Artificial Intelligence,” and a professional development (PD) program for prospective middle school AI teachers. To ensure that our curriculum could meet the needs of all learners, we recruited a diverse set of districts that included rural districts serving mainly White students, urban districts that were majority African American, and suburban districts serving a mix of Hispanic and African American students. Now in its fourth year, our “AI for Georgia” project (AI4GA) has provided PD to 20 teachers and AI education to over 1600 students. The AI4GA curriculum does more than foster AI literacy: It empowers students to view themselves as creators of AI‐powered technology and to think about future career options that involve the use of AI. The project is now expanding to schools in Texas and Florida. In this article, we review the history of the project, discuss our co‐design process with our teachers, and present results from studies of teacher PD and student learning.

The rapid pace of technological advancement and its immersive impact on our society underscores the need for all students to acquire fundamental knowledge and skills in computer science. Teachers are critical to the efforts to expand access and equity in computer science education. Developing teachers’ knowledge, practice, and professional identity is one of the key standards for effective teachers. This study delves into the landscape of American computer science educators from the lens of teacher identity. Using survey data from 2,337 educators, we further developed and tested a measure of teacher identity specific to teachers of computer science. By investigating their sense of professional identity, this study identified five distinct profiles of computer science educators: committed, confident, and well-resourced proponents; committed, confident, but under-resourced proponents; moderates; unconfident and under-resourced proponents; and uncommitted, unconfident, and under-resourced skeptics. We further examined the professional backgrounds and teaching contexts of those educators in each profile. This study contributes a validated tool for understanding computer science teacher identity, offering empirical insights informing the preparation and development of effective educators for teaching computer science in schools.

Expressive computer science (CS) learning environments teach coding through the creation of an artifact, such as audio or video output. EarSketch is an expressive CS learning environment designed to teach computing through music production, mixing and arranging sounds using code. In this paper, we explore the accessibility challenges of using EarSketch for learners who are Blind and Visually Impaired (BVI). We present key findings from co-design studies with teachers and students at an institution specializing in BVI education, focused on gathering both groups' unique perspectives about EarSketch's ability to support teachers' curricula, students' workflows using the system with accessibility software, and challenges faced by users who are BVI.

The rapid expansion of K-12 computer science education highlights the urgent need for well-prepared teachers. The Computer Science Teachers Association (CSTA) facilitates the development of local teacher professional learning communities (PLCs) through CSTA chapters. This study investigated the types of support CSTA chapters provide, how teacher leaders establish local PLCs and engage teachers of computer science, and the challenges encountered in this process. The investigation included multi-year focus group interviews with chapter leaders and teacher member surveys. The findings reveal that CSTA chapters serve as vital resources of professional support, amplify teachers' voices, and nurture their professional identities in teaching computer science. This study provides a nuanced understanding of local PLCs for computer science educators, informing future endeavors in teacher preparation and development.

Broadening participation of Latinx students in computer science (CS) is paramount in today's STEM educational landscape.Latinx represent the fastest growing population in the U.S. but remain under-represented in computer science.The Remezcla project was developed to tackle issues of broadening participation of Latinx students in CS through an informal learning program.The current paper describes the program components and provides evaluation results from the pilot summer program implementation, held virtually in Atlanta and Puerto Rico during the COVID pandemic.Preliminary evaluation results suggest these one-week summer camps were effective in impacting pre-post students' sense of belonging, self-efficacy, and intention to persist in computer science.Results reveal gender differences across several constructs with important implications for future studies.*5-point Likert scale items ('Strongly disagree' to 'Strongly agree') ** 7-point Likert scale items ('Not at all' to 'A great deal') ***5-point Likert scale items ('Never' to 'Always') # original survey used "informática."It was replaced by "computación" following cognitive interviews with students in Puerto Rico

As generative AI rapidly enters everyday life, educational interventions for teaching about AI need to cater to how young people, in particular middle schoolers who are at a critical age for reasoning skills and identity formation, conceptualize and interact with AI. We conducted nine focus groups with 24 middle school students to elicit their interests, conceptions of, and approaches to a popular generative AI tool, ChatGPT. We highlight a) personally and culturally-relevant topics to this population, b) three distinct approaches in students’ open-ended interactions with ChatGPT: AI testing-oriented, AI socializing-oriented, and content exploring-oriented, and 3) an improved understanding of youths’ conceptions and misconceptions of generative AI. While misconceptions highlight gaps in understanding what generative AI is and how it works, most learners show interest in learning about what AI is and what it can do. We discuss the implications of these conceptions for designing AI literacy interventions in museums.

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.

Black women remain severely underrepresented in computing despite ongoing efforts to diversify the field. Given that Black women exist at the intersection of both racial and gendered identities, tailored approaches are necessary to address the unique barriers Black women face in computing. However, it is difficult to quantitatively evaluate the efficacy of interventions designed to retain Black women in computing, since samples of computing students typically contain too few Black women for robust statistical analysis. Using about a decade of student survey responses from an National Science Foundation–funded Broadening Participation in Computing alliance, we use regression analyses to quantitatively examine the connection between different types of interventions and Black women’s intentions to persist in computing and how this compares to other students (specifically, Black men, white women, and white men). This comparison allows us to quantitatively explore how Black women’s needs are both distinct from—and similar to—other students. We find that career awareness and faculty mentorship are the two interventions that have a statistically significant, positive correlation with Black women’s computing persistence intentions. No evidence was found that increasing confidence or developing skills/knowledge was correlated with Black women’s computing persistence intentions, which we posit is because Black women must be highly committed and confident to pursue computing in college. Last, our results suggest that many efforts to increase the number of women in computing are focused on meeting the needs of white women. While further analyses are needed to fully understand the impact of complex intersectional identities in computing, this large-scale quantitative analysis contributes to our understanding of the nuances of Black women’s needs in computing.

Multi-pronged programs that involve students in a combination of proven interventions (i.e., tutoring other students, building community, developing skills, etc.) constitute one pedagogical approach to increasing the number and diversity of computing professionals. In this manuscript, we evaluate the efficacy of one such multi-pronged program, the STARS Computing Corps, a Broadening Participation in Computing Alliance program funded by the National Science Foundation. These analyses improve upon previous efforts to assess the efficacy of STARS by examining dosage effects of the program, adding controls for students' initial intentions to pursue computing, and conducting these analyses at various points in a student's participation in STARS. We also conduct analyses to determine the efficacy of various STARS activities. Controlling for students' initial intentions to persist in computing, we find robust evidence that spending more time each week on STARS' activities positively predicts students' intentions to persist in a computing career, and that STARS has a heightened positive impact on Black and Hispanic students. We do not find evidence that the number of semesters a student spends in STARS is predictive of computing persistence, nor do we find differences in the efficacy of various STARS activities. In sum, these results suggest that STARS has a positive impact on students' intentions to persist in computing and that multi-pronged programs like STARS should focus on the intensity of participation (as opposed to the length of participation or a particular activity) to increase students' desire to persist in computing careers.

To engage diverse populations of students who may not self-select into computing courses, a curriculum for a middle school music technology + computer science course that addresses learning standards for both subjects was developed and deployed. Students who engage with the curriculum learn modern music production techniques and computational thinking concepts. This is through a mix of traditional approaches to music technology education (digital audio workstations) and computational approaches via a culturally relevant learning platform that introduces students to coding through music production and remixing. This poster reflects on the last two years of curriculum design and deployment, teacher training, and student and educator engagement and feedback to provide insight into the teaching (and learning) of computational thinking in the music technology classroom.

Background & ContextContinuously developing teachers' knowledge, practice, and professional identity is one of the key standards for effective computer science (CS) teachers.ObjectiveThis study aims to understand the landscape of CS teachers in the United States, the professional identity they hold, and how their background and teaching context are associated with their CS teacher identity.MethodUsing data of 3540 teachers, we performed a two-step cluster analysis to reveal homogeneous subgroups of CS teachers. The relationship between teachers' backgrounds and their CS teacher identity was also assessed.FindingsThis study identified four profiles of CS teachers based on their professional identity. . CS teacher identity is strongly associated with teachers' Computer Science Teachers Association membership, teaching responsibility, teaching experience, and their exposure to CS coursework.ImplicationsMore professional support is needed for CS teachers, especially for early-career CS teachers, elementary school teachers, and teachers with multiple responsibilities and little CS background.

Creativity is one of the most crucial skills for success in the 21st-century workforce. Specifically, creativity is an important skill to have in science, technology, engineering, and mathematics (STEM)-related fields, and more empirical studies are needed to assess and improve creativity in STEM-related learning environments. In this study, we designed and validated an automated, unobtrusive, formative assessment of creativity in EarSketch, a computational music remixing platform where students learn to write Python or JavaScript code to create pieces of music. Using an existing data set of EarSketch projects (n = 53), we addressed two research questions: (Research Question 1) To what extent is the automated assessment of creativity that we designed in EarSketch psychometrically sound (focusing on validity and reliability), and (Research Question 2) what variables (i.e., divergent thinking, complexity, and self-report variables) predict students' creativity in EarSketch? Our main findings show that (a) the automated assessment of creativity has reasonable convergent validity (r = .47) and discriminant validity; (b) the automated assessment of creativity has a reliability estimate of .70; and (c) divergent thinking and the students' confidence in learning how to code significantly predicted students' creativity scores in an external, consensual assessment of creativity by EarSketch experts. Providing learning environments that can assess and support essential skills such as creativity alongside other STEM-related skills such as programming and computational thinking holds great promise for developing the next generation of the workforce who is not merely aware of STEM concepts and principles, but is creative and innovative in pursuing STEM solutions.

Effective professional learning communities (PLCs) are important in supporting teacher learning. This study investigated computer science (CS) teacher leaders' perspectives on the lessons and the challenges in supporting CS teachers through local PLCs. We purposefully selected ten CSTA chapters and conducted focus group interviews with the chapter leaders between 2020 and 2022. Our findings indicated that these PLCs offered social-emotional support, continual networking opportunities, and rich professional learning resources. Also, they amplified teachers' voices and supported CS teachers' professional identity building. To engage CS teachers, the teacher leaders built trust, collaborated with other PLCs or organizations, and set an inclusive PLC culture. These PLCs had challenges in recruitment, leadership development and transition and building group identity.