Agentic AI, where AI systems have their own forms of agency, will present some of the most critical challenges that computing education will face, including regulatory gaps and amplified cascading social and ethical impacts. This working group proposes a landscape analysis of the ethical and societal implications of using Agentic AI in higher computing education. By exploring emerging literature and use cases of Agentic AI, we aim to contribute a timely landscape study exploring 1) the emerging challenges and opportunities associated with Agentic AI, 2) how higher education is beginning to adopt and use Agentic AI and the resulting ethical and societal impacts, and 3) the implications (e.g. challenges, opportunities, limitations) of integrating Agentic AI in computing education. The expected outputs are: 1) a protocol and literature scoping review of Agentic AI in education, and 2) an analysis of current practices and use cases in computing education. Together, these outputs aim to identify emerging patterns, use cases, key challenges, and principles to support the ethical and responsible integration of Agentic AI in education.

Cybersecurity professionals must anticipate how attackers exploit system weaknesses -- a perspective known as ''adversarial thinking'' (AT). While cybersecurity education emphasizes the development of AT skills, there is little consensus on what these skills entail or how to assess them. We conducted a scoping review of computing education literature and semi-structured interviews (N=8) with cybersecurity professionals to better understand how adversarial thinking (AT) manifests in both academia and industry. Our analysis of 19 academic papers found that adversarial thinking is frequently described through system-centric approaches and/or hacker-centric definitions, with both presenting it as an open-ended creative exploration. However in practice, time constraints force cybersecurity professionals to develop a risk-based mindset and rely on institutionalized adversarial knowledge rather than a constant creative analysis. Our professionals strategically deploy adversarial thinking when standardized checklists feel inadequate or when potential risks warrant a deeper investigation. This paper contributes an empirically grounded account of how AT operates under professional constraints, and identify implications for how cybersecurity education can better prepare students.

Students learning programming exercise agency in deciding when and how to use GenAI tools like ChatGPT. However, this agency is often implicit and shaped by deadline pressure and peer behavior rather than explicit and conscious learning goals. We designed a GenAI Contract grounded in harm reduction and self-regulated learning theory to scaffold intentional decision-making: students articulated personal learning goals, created usage guidelines, and reflected on alignment at strategic points across an eleven-week semester. The contract was non-binding and graded only for completion, emphasizing self-awareness over enforcement. We implemented this with N=217 students in an intermediate Python course. For students still forming their relationship with GenAI, it worked, as 58% of students reported the intervention changing their thinking and created helpful accountability structures. However, awareness did not always translate to sustained behavior change. Some students who valued their guidelines still abandoned them under various pressures. Maintaining guidelines required constant self-control across hundreds of decisions, while using GenAI freely requires none. Many students could not sustain this burden despite this self-awareness. We discuss supporting student agency when GenAI tools and learning goals create tension.

Multiple software systems unintentionally exclude users, making the early integration of inclusive design training into software design education essential. In this study, we integrated critical pedagogy into an undergraduate software design course through (1) the CIDER assumption elicitation technique and (2) the exposure of values reflected in technology. Students' pre/post responses to the Critical Computing Index reveal that students' personal effectiveness increased after taking the software design course. Our initial analyses suggest that classwork encouraged critical reflection and agency, especially for improvements focused on accessibility, while interviews indicated that students grew from viewing marginalized perspectives as abstract ideals to actively integrating them into their design practices.

Computing education faces a unique challenge when teaching debugging skills to teachers who do not have a formal computer science background but who will need to learn programming to teach CS courses. While most debugging research about post-secondary learners focuses on preparing students for the technology industry, teacher professional development (PD) programs often serve a different population: teachers who are simultaneously learning computing knowledge and the pedagogical skills to teach it effectively. Through semi-structured interviews with seven facilitators of computing PD programs, this study explores how experienced PD facilitators approach the concept of debugging and instruct teachers in the process of debugging. We use reflexive thematic analysis to show how teacher PD differs from debugging recommendations in post-secondary CS: Rather than focusing on understanding the root causes of errors, facilitators scaffold the teachers' process of identifying and locating bugs to aid teachers in more quickly producing working programs, which they see as important for supporting teachers' confidence. This practical approach acknowledges the time constraints of PD workshops. Finding and understanding bugs is the most difficult part of the debugging process and is something post-secondary students struggle with even after completing one or two semesters of CS courses. These insights challenge assumptions about debugging pedagogy and highlight the need to carefully consider when new CS teachers should learn error analysis skills.

Thematic analysis is an increasingly popular method in computing education research; however, widespread methodological confusion undermines its potential. For example, notions of objectivity do not make sense with reflexive approaches, and evidence of saturation is not required for thematic analysis. This position paper details how thematic analysis evolved from Braun and Clarke's influential 2006 work into an umbrella method encompassing three general approaches with corresponding epistemologies: coding reliability (positivist), reflexive (interpretivist), and codebook (hybrid) thematic analysis. Each has different goals and assumptions, but researchers often inadvertently mix incompatible elements. We then present our personal journeys of learning about thematic analysis and finally dissect common confusing claims in our field's publications and peer reviews. Our goal is for the field of computing education research to move towards a ''knowing'' practice. By clarifying thematic analysis approaches and providing guidance for authors and reviewers, we hope to help the field of computing education research better understand this popular method.

As computing's societal impact grows, so does the need for computing students to recognize and address the ethical and sociotechnical implications of their work. While there are efforts to integrate ethics into computing curricula, we lack a standardized tool to measure those efforts, specifically, students' attitudes towards ethical reflection and their ability to effect change. This paper introduces the novel framework of Critically Conscious Computing and reports on the development and content validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students' attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students' perspectives, inform the design of targeted ethics interventions, and measure the effectiveness of computing ethics education initiatives.

Computing ethics education aims to develop students' critical reflection and agency. We need validated ways to measure whether our efforts succeed. Through two survey administrations (N=474, N=464) with computing students and professionals, we provide evidence for the validity of the Critical Reflection and Agency in Computing Index. Our psychometric analyses demonstrate distinct dimensions of ethical development and show strong reliability and construct validity. Participants who completed computing ethics courses showed higher scores in some dimensions of ethical reflection and agency, but they also exhibited stronger techno-solutionist beliefs, highlighting a challenge in current pedagogy. This validated instrument enables systematic measurement of how computing students develop critical consciousness, allowing educators to better understand how to prepare computing professionals to tackle ethical challenges in their work.

Despite evidence of its effectiveness, Peer Instruction (PI) has not been widely adopted by undergraduate computing instructors. In PI, an instructor displays a hard multiple-choice question that students answer individually, then discuss their answer with peers, then answer again, and finally an instructor leads a discussion of the question. Even though the benefits of PI are well documented, it can be difficult to convince computing instructors to move away from passive lectures. Major reasons why instructors do not adopt PI include a lack of awareness, lack of time, and concerns over their ability to cover content. We hypothesized that we could encourage the adoption of PI by creating Peer+, a free tool in an ebook platform, a searchable question bank, and running summer instructor workshops. We offered a three-day in-person summer workshop to a total of 37 instructors in 2022 and 2023. Instructors completed a pre-survey, immediate post-survey, and a follow-up post survey after the fall semester. We also conducted semi-structured interviews with 17 instructors. On the immediate post-survey most (33/37, 89%) instructors reported that they were very likely or likely to use the tool in the fall. However, on the follow-up survey, less than a quarter (6/26, 23%) actually did. The number one reason for not using the tool was a lack of time (18/26, 69%). Notably, all of the instructors who used Peer+ planned to use it again. This work informs efforts to increase the adoption of evidence-based pedagogical approaches in computing.

As discussions of computing's impact on society increase in public discourse, so does recognition for computing students to address the ethical and sociotechnical implications of their work. While efforts to integrate issues of ethics and social justice into computing curricula are nascent, we lack a standardized measure to monitor our progress towards these goals. In this poster, we report on the development and validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students' attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool with evidence for reliability and validity to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students' perspectives, inform the design of targeted ethics interventions, and monitor the effectiveness of computing ethics education initiatives.

As the societal impacts of technology become more salient, it becomes increasingly important to assess future professionals’ attitudes towards considering and learning about the ethical and sociopolitical implications of computing. My research plan has three studies to investigate this issue: a synthesis of literature on ethical interventions, the development and validation of a scale measuring attitudes towards foundational principles of critically conscious computing, and a large-scale survey of students’ alignment with these principles. The contribution of this work is to assess the prevalence and distribution of students’ attitudes about ethics across a large population, potentially identifying trends. Grounded in critiques of computer science ethics education and its growing emphasis on critical consciousness, my work aims to contribute insights into students’ attitudes towards incorporating ethics into undergraduate computing curricula.

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM’s technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students’ social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students’ self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students’ use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students’ perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.

Amid increasing calls for critical and anti-oppressive approaches to computer science (CS) education, educators are exploring how to create justice-centered teaching material. Additionally, broadening participation in justice-centered computing requires an understanding of students' relationship with social justice and their CS education. In this study, we created and distributed a programming project with a social justice context and critical thinking reflection questions as a probe for an intermediate programming class. We conducted a thematic analysis of 11 semi-structured interviews and distributed a short survey (N=86) with students of this class at a large public research university in the American Midwest. Our findings showed that these students support social justice contexts and content within their computer science education. Students requested deeper dives and discussions into social justice programming that would challenge their preconceived notions, incorporate calls to action, and direct action. However, we also found an interesting tension forming: many students described how their homework problem-solving mindset clashed with the critical thinking reflection questions.

Decades of research have provided evidence of the effectiveness of Peer Instruction (PI) in many fields, including computing. PI involves an instructor displaying a hard multiple-choice question that students answer individually, then discuss with peers and answer again. The instructor then displays the results from the two votes and leads a discussion.

Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.

The field of computer science education has seen an abundance of experience reports exploring various implementations of pedagogical approaches and tools. While these reports have provided valuable insights, there remains a need to understand how these interventions work and why they are successful in specific contexts. The realist synthesis literature review method, commonly used in fields with experience reports and implementation research, offers significant potential for computer science education research by identifying the causal mechanisms and theories by which an intervention works (or not). This poster presents the process of conducting a realist synthesis review and explores its strengths and challenges in the context of computer science education research. The poster aims to provide insights into how realist reviews can help researchers synthesize experience reports to develop more effective evidence-based practices and theories.

Computing culture and its artifacts tend to oust marginalized students. Therefore alongside our push to broaden participation in computing (BPC), we must create new methods of critiquing and changing computing culture and its artifacts. Many marginalized students have experienced what Dr. Ruha Benjamin calls "glitches": a breakdown of computing artifacts regarding the intersection of identities and computing that expose systemic biases. As many egregious "glitches" border on parody or satire, we investigated the potential of satirical programming in BPC programs to critique computing artifacts. We designed and conducted a one-hour session for three high school BPC programs in the American Midwest. Sessions used culturally responsive pedagogy to discuss how programming encodes bias. We taught elements of Python to scaffold the creation of a short Python script. We showed an example of a satirical Python script and encouraged students to satirize a "glitch" they experienced in a computing artifact or concept. Our findings show that many marginalized students were well aware of "glitches" in their own experiences with software. They enjoyed translating the "glitches" into satirical Python scripts that demonstrated their understanding of how systemic biases manifest in software development. In this poster, we share the results of a post-session survey, student examples of satirical scripts, and recommendations for instructors looking to include creative methods of discussing systemic biases in software.

Fostering public AI literacy (i.e. a high-level understanding of artificial intelligence (AI) that allows individuals to critically and effectively use AI technologies) is increasingly important as AI is integrated into individuals’ everyday lives and as concerns about AI grow. This paper investigates how to design collaborative, creative, and embodied interactions that foster AI learning and interest development. We designed three prototypes of collaborative, creative, and/or embodied learning experiences that aim to communicate AI literacy competencies. We present the design of these prototypes as well as the results from a user study that we conducted with 14 family groups (38 participants). Our data analysis explores how collaboration, creativity, and embodiment contributed to AI learning and interest development across the three prototypes. The main contributions of this paper are: 1) three designs of AI literacy learning activities and 2) insights into the role creativity, collaboration, and embodiment play in AI learning experiences.

desAIner is a creativity support tool able to assist a fashion designer in the ideation process of creating clothing through the use of a ”bad” Generative Adversarial Network (GAN). After training the GAN on a relatively small number of diverse high fashion images from popular designers’ clothing collections, the tool generates a latent space of surreal visual representations and combinations of these fashion images for designers to explore and generate fresh ideas. Using this tool, we conducted interviews with two fashion designers who used the tool and discussed how these ”bad” GANs can help them turn the creative task of designing novel clothing into a more effortless and efficient task by presenting surreal visual stimuli to incite inspiration.

As enrollment numbers in online courses increase, students, instructors, and teaching assistants have difficulty finding needed information in online forums because of the number of posts, resulting in duplicate posts that exacerbate the problem. We introduce PARQR, a recommendation tool that suggests relevant contributions as participants compose their posts. We investigate the use of PARQR in five online degree-seeking courses. We survey 74 students and interview five teaching assistants to understand their experience with online forums and PARQR. We compare the differences between using and not using PARQR for an online course assignment. PARQR users found the tool to be useful for navigating online forums, and PARQR was effective in reducing the number of posts (0.291 vs. 0.506 posts per active student) and duplicate posts (17.8% vs. 25.6%) in an online course. These results suggest that PARQR makes on-line forums more efficient for users to find needed information.