This Birds-of-a-Feather (BOF) session provides a space for LGBTQ+ educators, researchers, students, and allies to connect, share, and collaborate. It builds upon the strong foundation of previous successful BOFs focused on cultivating and sustaining the LGBTQ+ community (Kivuva et al., 2025) and the broad interest shown in panels on advancing LGBTQIA+ voices (DuBow et al., 2024). Recognizing the diverse needs within our community, this session is built around four concurrent, facilitated breakout groups, allowing attendees to self-select into the conversation most relevant to them. The importance of affinity spaces for ''gender-based community building'' and fostering a sense of belonging is a key aspect of broadening participation (Swackhamer et al., 2022). The session will begin with a collective welcome and introduction to ground us in a shared sense of community before self-selecting into roundtables. The four roundtable themes are: (1) An affinity space for sharing personal experiences from the past year; (2) Supporting LGBTQ+ students and building departmental community; (3) Advancing LGBTQ+-focused research in CS education; and (4) Professional development focused on grant writing and career advancement. This unique structure ensures that, whether attendees are seeking peer support, pedagogical strategies, research collaboration, or career mentorship, they will find a dedicated space. Participants will leave with new connections and targeted, actionable insights to support their personal and professional lives.

As generative AI increasingly integrates into society and education, the number of institutions implementing AI usage policies and offering introductory AI courses is rising. These introductory AI courses mustn't replicate the "gateway/weed-out" phenomenon observed in introductory computer science courses like CS1 and CS2. Literature in computer science education suggests that interventions such as summer camps, bridge courses, and socio-technical courses have improved the sense of belonging and retention among students from underrepresented groups, thereby broadening participation in computer science. Building on previous work to create a socio-technical curriculum for all ages and education levels, this paper presents a course for teaching introductory AI concepts that adopts a socio-technical approach, complete with weekly activities and content designed for broad access. The course has been taught as a 1-credit general education course, primarily for freshmen and first-year students from various majors, and a 3-credit course for CS majors at all levels.This paper provides a curriculum and resources to teach a socio-technical introductory AI course. This approach is important because it not only democratizes AI education across diverse student backgrounds but also equips all students with the critical socio-technical multidisciplinary perspective necessary to navigate and shape the future ethical landscape of AI technology.

This work-in-progress innovative practice paper builds on existing studies highlighting the significant lack of diversity in the field of artificial intelligence (AI),with a particular emphasis on the role of identity in shaping biases, inequalities, and ethical considerations within AI systems. As AI becomes increasingly integrated into society, it is essential to critically examine its impact on disabled individuals. This paper advocates for the adoption of an anti-ableist framework, emphasizing the inclusion of disability perspectives throughout the AI development and deployment processes. Central to this position is a critical examination of the AI identity ecosystem, which includes the creators of AI, the technologies they produce, and the societal implications of these technologies-all viewed through a lens that prioritizes disability rights and perspectives. We introduce a conceptual framework designed to center disability within the AI ecosystem, particularly in educational settings. This framework aims to teach students the importance of accessibility and anti-ableism in AI, equipping them with the tools to integrate these principles throughout their work. By promoting an anti-ableist approach and integrating accessibility education, this paper seeks to inform future policies and initiatives in human-centered AI. It contributes to the ongoing discourse on ethical AI, urging a reevaluation of how disability is integral to AI's identity and its future trajectory.

This full paper presents an innovative board game, “Game of Life: Educational Pathways” developed as an engaging pedagogical tool to explore the theories of social reproduction, opportunity structures, and educational attainment through the concept of capital. Drawing inspiration from the traditional “Game of Life,” this adaptation provides a participatory learning experience that delves into the complexities of educational pathways, privilege, socioeconomic factors like capital, and their cumulative impact on life outcomes. The game challenges players to navigate through a series of life stages and decision points related to education, where their choices and the inherent randomness of dice rolls simulate the unpredictable nature of life's opportunities. By simulating the journey through different educational and life outcomes based on various forms of capital - Talent, Wealth, Goodwill, Legacy - the game offers a hands-on approach to understanding the complexities of opportunity and achievement in computing and engineering education. This interactive tool not only aids in grasping complex educational theories but also highlights the importance of personal effort, policy, and practice in overcoming barriers for educational participation. Ideal for educational settings, “Game of Life: Educational Pathways” prepares future computing and engineering professionals to navigate and contribute to a more equitable field.

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.

Broadening participation in computing (BPC) has been a key focus of the National Science Foundation (NSF) for over two decades. Its aim is to support students and faculty from historically underrepresented groups, including women, people with disabilities, and certain racial and ethnic groups. Within these communities, the diverse range of gender and sexual identities remains overlooked in computing education research (CER). To address this invisibility, this panel will discuss the benefits of integrating LGBTQIA+ perspectives. The moderator will provide context, define relevant terms, and set ground rules for discussion. The panelists will offer insights from a variety of perspectives, including: a discussion of the policy landscape impacting LGBTQIA+ students and the importance of incorporating their perspectives as researchers and participants; the erasure of queer history in computer science and advocate for LGBTQIA+ inclusion, considering the humanitarian calling for the field and CS educators in our tech-driven world; the current resistance to supporting LGBTQIA+ scholarship in computing, advocating for an inclusive approach; and finally, the experiences of marginalized individuals in CS education and ways to support them, emphasizing inclusivity through storytelling and personal narratives. The panel aims to increase visibility, understanding, and collaboration between the computing education research community and LGBTQIA+ individuals. By acknowledging and integrating diverse perspectives, we can begin to create a more inclusive, equitable computing landscape.

This Work-in-Progress, Research paper presents the experiences of three engineering students who navigate STEM education with multiple marginalized identities. Using the Social Identity Wheel as a framework, the study aims to fill gaps in intersectionality research by exploring the positionality and experiences of these marginalized students in STEM. This paper showcases the practical application of the Social Identity Wheel in computing and engineering education research, particularly for studies with small sample sizes, to guide in participant selection and data interpretation techniques. The findings underscore the significance of incorporating intersectional perspectives in all forms of educational research and the importance in understanding the complexities of intersectionality. The research questions guiding this study are: (1) How can the use of the Social Identity Wheel be used to further contextualize and explore the positionality and experiences of marginalized students in STEM? and (2) In what ways does the interplay of one's non-visible identities and visible identities intersect to shape their STEM experience?

This work-in-progress paper presents a study that sheds light on the concerns that students may not develop sufficient programming skills and as a result, be less competent with the use of ChatGPT. The potential benefits for students are significant: Access to ChatGPT increases the ability for students to work constructively on their own schedule. The ease of use of ChatGPT may engage students who might otherwise hesitate in asking for support. Before these tools can be meaningfully introduced into a course, work must be done to study the impact of these AI tools on a student's ability to learn. In this study, participants are recruited from introductory Java programming courses at a large public university in the United States. This paper presents preliminary findings from a mixed method study design that consists of a pre-task assessment quiz; and a programming task in one of three conditions: (1) with no external help, (2) with the help of an AI chatbot, or (3) with the help of a generative AI tool like GitHub Copilot; followed by a post-task assessment and an interview on their experience and perceptions of the tools. Our preliminary findings describe our data collection, thematic analysis of the students' prompts and chatGPT responses, and a summary of the experience for 3 students. Our findings demonstrate a range of students' attitudes and behaviors towards chatGPT that provides insight for future research and plans for incorporating such AI tools in a course.

This full paper presents the Collaborative Active Learning and Inclusiveness (CALI) inventory, and an analytical model using the CALI inventory, demographic data, mindset surveys, and knowledge mastery assessment, to explore relationships between classroom climate and student experiences. The CALI inventory enables the investigation of the impact of the student experience in an active learning classroom by distinguishing the factors that characterize the structure, social learning, and inclusive practices. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. A CS Mindset Instrument was developed based on research that measured students' self-efficacy by evaluating the extent of variation in their self-perceived ability to accomplish a task, sense of belonging in computing, and professional identity development. Demographic data is collected that allows for an analysis using an intersectional lens to acknowledge the complexity of social and cultural contexts. The knowledge and mastery assessments capture changes in competency through pre-post mastery quizzes. The combination of CALI with other instruments, including those that characterize student mindset, identity, and levels of mastery, enables investigation of how various practices of inclusive and collaborative active learning have differential effects on students with different identities in computer science.

This full paper presents CALI 1.0: an inventory to capture the various components of collaborative active learning and inclusive practices in introductory CS courses, as a basis for studying their impact on a student’s ability to engage with course material and be successful in the course. Collaborative active learning is defined as a pedagogy where learners work with one another on activities that involve actively engaging with the course material through discussions, problem-solving, case studies, role plays, and other methods. CALI consists of three separate indices: Structure, Sociality, and Inclusiveness. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. In this paper, we present the inventory and results from a focus group of faculty to gauge responses to using this inventory to capture the teaching components of their courses. We present CALI as a tool that can be used to study how teaching practices, e.g. inclusive and collaborative active learning pedagogies, impact course experiences for students in different demographic groups as well as a reflective tool for use by faculty while designing their courses.