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.

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.

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.

Computer science (CS) enrollments are at an all-time high, and successful undergraduate CS graduations are indisputably important. With a student population of approximately 51,000, Georgia State University is a USA based state university which is diverse and forms a rich big data footprint as students navigate pathways to graduation. Quoted in a July 2017 article from HigherEd.com, "Georgia State's extensive predictive analytics efforts are leading to better grades and student retention -- and more minorities graduating from STEM programs.'' This doctoral project builds upon current data mining and modeling, machine learning applications, and learning analytics for predicting student success that is beyond retention. Gaining knowledge of CS student learning, developing better alerting models for success, and discovering behavioral indicators from learning analytics reporting is the goal of this research. Using this knowledge as evidence based data for improving the CS student experience will aid in performance improvements and increase pathways to graduation. My supporting research project is building CS student datasets to represent the student as directed graphical models, investigating their relationships using machine learning frameworks, and complex mathematical computations (tensors or gradient boosting) along with graph data mining techniques.

A recent 2017 study from HigherEd.com, shows that roughly 35% of students transfer colleges during their academic career. While much research has focused on the impact of undergraduate student success factors such as flipped classrooms, hybrid learning, and technology usage, we found no studies comparing transfer students versus non-transfer (native). In our research, we measure impact based on CS course load per semester related to pass/fail rates and contrast student status of transfer versus non-transfer (native). We show that transfer students tend to enroll in more CS courses per semester, beyond the department's recommended two. We also show that their performance is consistently different than that of native students, namely higher CS course fail rates and lower CS GPA scores. The detrimental effects and realization in this study is reason for further investigation. More features will be gathered to be used in our continued analysis and we see the need to examine the difficulty of CS courses taken to indicate why one cohort performs poorly and one does not. We conjecture that most transfer students tend to complete their core classes and are left with only CS courses to complete upon transfer. This results in the transfer students taking a heavier CS load and thus impacting their performance negatively, as compared to native students. Considering that many transfer students start their path way in 2-year institutions, it is imperative that better advising strategies are developed to enable the students to succeed upon their transition.