Teaching assistants (TAs) play a critical role in computing and HCI education, yet little is known about how they perceive and use AI tools or imagine their future pedagogical uses. We report on a series of design workshops with 131 computing (CS) TAs across two U.S. universities. These workshops invited TAs to reflect on current AI use and envision future AI-enhanced tools and practices. Drawing on surveys and design artifacts, we (1) develop a cross-institutional typology of situated TA uses of AI, revealing opportunities and tensions; (2) show how TAs’ visions of AI are shaped by disciplinary norms, institutional structures, and their intermediary position as student-instructors; and (3) reveal ethical dilemmas. Our findings contribute to HCI by positioning TAs as AI-supported knowledge workers in the education domain; illustrating how design and speculation are shaped by people’s situated understandings of AI and their institutional contexts; and identifying a core tension in which TAs simultaneously preserve and erode the human dimensions of their work, with implications for future instructional tools and human–AI collaboration.

As enrollment in CS1 courses continues to increase, extensive research has focused on autonomous support to offer personalized assistance for struggling students at scale. However, it is crucial that these intervention techniques do not inadvertently hinder the development of higher-order, computational thinking skills for novice programmers. This poster extends upon the research on LLM-based support by assessing the short- and long-term student outcomes from two carefully prompt-engineered, LLM-generated hint styles: Targeted and Socratic hints. A randomized controlled trial with 178 students was conducted over two semesters in a CS1 course at a large university, allowing students to interact with a hint generation AI agent while attempting course coding assignments. In the short-term, students receiving Socratic hints spent more time, took more attempts, and used more keystrokes to solve coding questions, while committing more repeat errors. Furthermore, this short-term loss in debugging efficiency is not counteracted by any evidence of an improvement in long-term student outcomes. Further research is being conducted to quantify the tradeoff between short-term performance and long-term, higher-order coding skill improvement in the development of educational AI agents.

Enrollments in introductory computer science (CS1) courses continue to rise, making it difficult for instructors to deliver rapid, individualized feedback that addresses students' misconceptions at scale. We present an analysis framework and instructor tool that leverage large language models (LLMs) to classify, cluster, and present students' coding errors in real time. Our approach comprises two main contributions: (1) a prompt-engineered workflow for automatic error detection and a clustering pipeline using universal sentence encoders, KMeans, and t-SNE to group errors into thematic clusters; and (2) a dashboard that enables instructors to review class-wide, LLM-identified errors and dynamically tailor instruction toward current student misunderstandings. Our automated thematic clustering system is able to surface conceptual and strategic pitfalls that often persist beneath superficial debugging. A pilot study is being conducted to evaluate the effectiveness of the dashboard tool in large-scale CS1 instructional settings to enhance active learning at scale.

Computer Science enrollments grew rapidly for many years and have since remained at historically high levels. Due to the large enrollment, especially in introductory courses, the need for excellent teaching assistants (TAs), has remained extremely important. In response, numerous CS departments have turned to their expanding pool of undergraduate students to recruit Undergraduate Teaching Assistants (UTAs) to help scale instruction. These Undergraduate Teaching Assistants (UTAs) not only provide help with grading, but are also responsible for developing course resources, running lab sections, and mentoring both students and newly hired UTAs. Ultimately, UTAs have a significant role in affecting course climate, with a particular emphasis on building community, which has a positive impact on underrepresented student populations. Computer Science departments struggle with the challenges of finding, hiring, and training large numbers of teaching assistants. In both 2023 and 2024, this Birds of a Feather (BoF) session drew more than 75 participants, sparking lively discussions about the challenges of hiring and training effective teaching assistants. The first session centered on identifying shared challenges across institutions and exchanging ideas. In the second year, we shifted focus to gathering data from attendees' institutions and building a shared database on TA hiring and training practices. This year, we plan to take the next step by addressing a key challenge identified in previous sessions: developing a crowdsourced repository of best practices for TA training, with the long-term goal of using it as the foundation for a comprehensive TA training curriculum.

Teaching assistants (TAs) play an increasingly vital role in computer science (CS) education, particularly amid rising enrollments, expanding instructional modalities, and the emergence of generative AI tools. In this evolving landscape, CS TAs are taking on greater responsibilities and often serve as the primary point of personal interaction for students, particularly through recitations, lab sessions, and office hours. However, many CS TAs receive limited preparation in inclusive and responsive teaching practices, limiting their ability to effectively support students from diverse cultural and educational backgrounds. To address this gap, we developed and delivered a series of responsive pedagogy workshops at two diverse institutions. These workshops aimed to deepen CS TAs' understanding of inclusive and responsive teaching strategies, support their implementation in practice, and create space for co-design by positioning TAs not only as learners, but as partners in imagining how responsive pedagogy principles could be more effectively integrated into the courses and contexts in which they teach. In this experience report, we describe the design and implementation of these workshops with 117 TA participants, share workshop materials for broader adoption, and reflect on key findings related to integrating responsive pedagogy into CS education through TA training.

Despite the rapid adoption of GenAI assistants in computing education, we still lack insight into the AI designs that instructors consider essential for effective teaching in large computing courses beyond CS1.Prior work has analyzed instructors attitudes towards

The rapid digitalization of the global economy, driven by big data and artificial intelligence, has significantly increased energy consumption, reshaped labor markets, and impacted politics and communities to an extraordinary extent. Addressing these challenges involves educating future computer scientists about the carbon emissions associated with their code and the broader societal consequences of the technologies they design. Traditionally, computing education has focused on optimizing runtime and memory efficiency, frequently overlooking the links to energy efficiency and carbon footprint considerations. Additionally, the integration of ethics into the curriculum has not been comprehensive. This paper proposes a framework for integrating sustainability into the computing curriculum, prioritizing it as a critical consideration for students. It outlines the competencies required for sustainability education and identifies topics directly related to the UN SDGs as a natural entry point for sustainability concepts. Additionally, it reports on a pilot framework implementation at a major US public university, where over 3,200 students from over 30 disciplines were exposed to sustainable coding practices and the ethics of AI and machine learning. The curriculum incorporated transformative teaching and learning methodologies with lectures, supplemental materials, and interactive projects highlighting these themes. Challenges to implementation, which may be encountered by other institutions, are also discussed. Survey results demonstrate that sustainability can be seamlessly integrated into early coding education, encouraging ongoing effort to accentuate energy-efficient coding in computer science courses.

Understanding the debugging process of novice programmers as they iteratively solve coding challenges is essential for developing intelligent tutoring systems that address gaps in comprehension and procedural coding skills. This poster presents a framework for systematically analyzing student coding attempts using large language models (LLMs) to identify syntactical, conceptual, and strategic errors. This study investigates 346 coding attempts for three live-coding challenges in a CS1 course, tracking the progression of errors over successive submissions. Preliminary results indicate that among students who attempted the challenges at least ten times, syntactical errors decrease more rapidly within the first ten attempts compared to conceptual or strategic errors. Although students effectively resolve syntax issues early in the debugging process, higher-level conceptual and strategic errors persist, suggesting the need for targeted instructional support at this stage.

Despite the need to build a quantum workforce, current courses that introduce quantum programming are rooted in quantum notation that students may find intimidating. We propose Q-CS1, a quantum equivalent of CS1 that begins with hands-on quantum programming. Q-CS1 is enabled by the Qwerty quantum programming language, which allows for reasoning about qubit behavior without physics notation or quantum circuits. An outline of Q-CS1 is provided along with plans for assessing its effectiveness.

Recently, there has been a growing interest in the role of teaching assistants (TAs) in computer science (CS). This interest is due to the vital role CS TAs play in supporting student learning and their expanding responsibilities driven by growing enrollments in CS programs worldwide. While much of this research focuses on the technical and pedagogical aspects of CS TAs' duties, researchers recognize the need to further explore the unique value human CS TAs provide, particularly with the rise of AI tools and assistants. In this paper, we use qualitative methods to analyze 109 survey responses collected across two different institutions in the United States as part of a larger design-based research project to make two contributions. First, we illustrate how CS TAs adopt humanistic stances and demonstrate care in their roles, thereby expanding prevailing understandings of CS TAs. Second, we detail similarities and differences across CS TAs' experiences at each institution that underscore the importance of understanding CS TAs as they are situated in different institutional contexts. We conclude by discussing implications of this work for computing instruction and TA training, emphasizing the importance of foregrounding the roles and values brought by TAs.

To address the challenges of generative AI in CS1 education, especially its misuse by students to bypass coding exercises, which undermines their engagement with foundational learning, CS1 curricula are evolving to emphasize higher-level problem-solving and systems thinking. In response, a novel experiential learning initiative grounded in High-Impact Practices was introduced to a CS1 course over the course of 2 semesters, involving 132 students. This initiative utilized robotics lab assignments to enhance computational thinking across various levels of granularity, from individual functional components to overall system behaviors, bridging conceptual understanding with real-world applications. The approach emphasized project-based learning, extended engagement time, and reflective practices to deepen students' understanding of core computing concepts and scaffold knowledge integration. The curriculum featured both individual and team-based lab assignments to build foundational skills followed by collaborative problem-solving. The initiative's impact was assessed against a control group of 427 students who completed traditional web development lab assignments. Evaluation methods included thematic analyses of student reflections, instructor opinion surveys, and statistical analysis of exam performances across the semester. Results revealed a substantial positive effect on self-efficacy and learning outcomes. Students in the experiential learning group reported increased confidence in applying their computing skills to real-world scenarios, heightened engagement, and greater improvements in technical proficiency. Notably, their exam scores demonstrated a statistically significant improvement compared to the control group. These findings highlight the effectiveness of integrating practical, interactive elements into computer science education to meet the demands of a rapidly evolving technological landscape.

Manually grading coding assignments in large computer science (CS) classes is a challenging logistical task. The evaluation of code correctness is subjective, leading to grading bias and inconsistencies. This problem is exacerbated when multiple teaching assistants (TAs) grade different submissions of the same problem.