Society is deciding how to integrate generative AI, with visions ranging from replacing people to augmenting individuals to enhancing collaboration. Individual-focused AI tools are already disrupting computing education and students' careers. We see this disruption as a generational opportunity: computing education should prepare students not only for industry careers, but to co-design technologies supporting humane futures, shaping the organizations and society they want to live in. In this position paper, we share a vision for computing educators and students to co-design generative AI integration into our education systems. We argue that current integration approaches over-emphasize individual learning at the expense of human interaction, threatening to weaken learning communities. We then argue for student-faculty co-design and co-building of collaborative learning tools with AI, used in their courses, departments, and education community. We share concrete examples of community-centered learning activities to start building trust and basic knowledge about AI use and AI design. We then offer a core design principle for community-centered AI tools in computing education: they should embody the values we want students to build into AI tools in society. We share examples in the form of problem(s)/limitation(s) of current tools, design ideas to address them, and collaborative learning supported by the tool. Next, we recognize implementation challenges, balancing hope while affirming that building new tools is not the solution to many problems. We conclude with a call to action and ways to start dialogue among faculty and students.

Generative AI is disrupting computing education. Most interventions focus on teaching GenAI use rather than helping students understand how AI changes their programming process. We designed and deployed a novel comparative video reflection assignment adapting the Describe, Examine, then Articulate Learning (DEAL) framework. In an introductory software engineering course, students recorded themselves programming during their team project two times: first without, then with using generative AI. Students then analyzed their own videos using a scaffolded set of reflection questions, including questions on their programming process and their help-seeking from humans, the internet, and AI. We conducted a qualitative thematic analysis of the reflections, finding students developed insights about planning, debugging, and help-seeking behaviors that transcended AI use. Students reported learning to slow down and understand before writing or generating code, recognized patterns in their problem-solving approaches, and articulated specific process improvements. Students also learned and reflected on AI limits and downsides, and strategies to use AI more critically, including better prompting but also to benefit their learning instead of just completing tasks. Unexpectedly, the comparative reflection also scaffolded reflection on programming not involving AI use, and even led to students spontaneously setting future goals to adopt video and other regular reflection. This work demonstrates structured reflection on programming session videos can develop metacognitive skills essential for programming with and without generative AI and also lifelong learning in our evolving field.

In this Birds of a Feather, we will discuss the roles of computing education in preparing students to understand and address the disparate impacts of climate change in local and global contexts. We intend to have open discussions on the challenges and opportunities related to connecting computing education with climate change and the injustices that climate change exacerbates. We will focus discussions around three questions: (1) How can we center justice-based perspectives on understanding and addressing climate change in computing education? (2) What are the relationships between computing, climate change, and overall environmental impacts? and (3) How should we reimagine traditional notions of "development" and "progress" in computing in ways that challenge how current framings within computing misunderstand or misteach computing's environmental impacts? We invite all computing educators, researchers, administrators, practitioners and anyone else with any level of curiosity about climate change to join this discussion (because it affects all of us!). Expected outcomes for this Birds of a Feather include sharing resources and experiences to build a community for knowledge sharing and collaborations.

Students often struggle with advanced computing courses, and comparatively few studies have looked into the reasons for this. It seems that learners do not master the most basic concepts, or forget them between courses. If so, remedial practice could improve learning, but instructors rightly will not use scarce time for this without strong evidence. Based on personal observation, program tracing seems to be an important pre-requisite skill, but there is yet little research that provides evidence for this observation. To investigate this, our group will create theory-based assessments on how tracing knowledge affects learning of advanced topics, such as data structures, algorithms, and concurrency. This working group will identify relevant concepts in advanced courses, then conceptually analyze their pre-requisites and where an imagined student with some tracing difficulties would encounter barriers. The group will use this theory to create instructor-usable assessments for advanced topics that also identify issues caused by poor pre-requisite knowledge. These assessments may then be used at the start and end of advanced courses to evaluate to what extent students' difficulties with the advanced course originate from poor pre-requisite knowledge.

Choices learners make when navigating a self-directed online learning tool can impact the effectiveness of the experience. But these tools often do not afford learners the agency or the information to make decisions beneficial to their learning. We evaluated the effect of varying levels of information and agency in a self-directed environment designed to teach programming. We investigated three design alternatives: informed high-agency, informed low-agency, and less informed high-agency. To investigate the effect of these alternatives on learning, we conducted a study with 79 novice programmers. Our results indicated that increased agency and information may have translated to more motivation, but not improved learning. Qualitative results suggest this was due to the burden that agency and information placed on decision-making. We interpret our results in relation to informing the design of self-directed online tools for learner agency.

As we celebrate the 50th SIGCSE Symposium, this panel explores how computing education researchers chart a course individually and as a community to build our research practices and collective knowledge of computing education. This navigation involves developing our research goals, which tools we use to work towards those goals, and which academic communities outside of computing education we seek to learn from and contribute to. However, these processes of navigation are rarely discussed as a community. Paper and grant submissions and reviews provide an imperfect way for our community to communicate our varied values and priorities. This panel brings together experts in computing education research who differ in their research goals, tools, and external communities. We can expect a lively discussion amongst the panelist and we hope to spark important discussions within the computing education research community!

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

A primary goal of computing education research is to discover designs that produce better learning of computing. In this pursuit, we have increasingly drawn upon theories from learning science and education research, recognizing the potential benefits of optimizing our search for better designs by leveraging the predictions of general theories of learning. In this paper, we contribute an argument that theory can also inhibit our community's search for better designs. We present three inhibitions: 1) our desire to both advance explanatory theory and advance design splits our attention, which prevents us from excelling at both; 2) our emphasis on applying and refining general theories of learning is done at the expense of domain-specific theories of computer science knowledge, and 3) our use of theory as a critical lens in peer review prevents the publication of designs that may accelerate design progress. We present several recommendations for how to improve our use of theory, viewing it as just one of many sources of design insight in pursuit of improving learning of computing.

We propose and evaluate a lightweight strategy for tracing code that can be efficiently taught to novice programmers, building off of recent findings on "sketching" when tracing. This strategy helps novices apply the syntactic and semantic knowledge they are learning by encouraging line-by-line tracing and providing an external representation of memory for them to update. To evaluate the effect of teaching this strategy, we conducted a block-randomized experiment with 24 novices enrolled in a university-level CS1 course. We spent only 5-10 minutes introducing the strategy to the experimental condition. We then asked both conditions to think-aloud as they predicted the output of short programs. Students using this strategy scored on average 15% higher than students in the control group for the tracing problems used the study (p<0.05). Qualitative analysis of think-aloud and interview data showed that tracing systematically (line-by-line and "sketching" intermediate values) led to better performance and that the strategy scaffolded and encouraged systematic tracing. Students who learned the strategy also scored on average 7% higher on the course midterm. These findings suggest that in <1 hour and without computer-based tools, we can improve CS1 students' tracing abilities by explicitly teaching a strategy.

Two large multinational studies show more than 60% of students incorrectly answer questions about the execution of basic programs. How can we improve program comprehension learning outcomes, and does that improve program writing learning outcomes? Nearly all prior tools and approaches have been evaluated in a writing-focused pedagogical context. People receive instruction on a programming construct's syntax and semantics, practice by writing code, then advance to the next construct (roughly a spiral syntax approach). In contrast, little work has explored a comprehension-first pedagogy, teaching and assessing program semantics - how static code causes dynamic computer behavior - before teaching learners to write code. I hypothesize this pedagogy improves program comprehension and writing learning outcomes, and that an adaptive curriculum of programs that aligns with the learner's interests and assessed knowledge further improves outcomes. Towards that goal, I built and evaluated a comprehension-first tutorial (PLTutor) with a fixed, non-adaptive curriculum, showing 60% higher learning gains (3.9 vs 2.4 on the SCS1) than the writing-focused tutorial Codecademy. I'm looking for new ideas (such as more social (theories, design, etc)), prior work, or methods to inform my thesis proposal and committee selection.

What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures.