Background and Context. Problem decomposition is a core concern of computing education. It has also become increasingly relevant: in response to GenAI, many CS1 educators are advocating for shifting instructional emphasis away from code writing and towards decomposition and higher-level planning. Currently, there is a lack of knowledge in how novices do decomposition in large, multifunction tasks.

Oral exams have been shown to have many benefits for both students and staff; however, there have been prior concerns about their scalability and fairness. In this experience report, we discuss the implementation of oral exams in a large-enrollment (N=628) introductory computing course designed for non-computer science majors. We report on the limited correlation between oral exam and computer-based exam scores, week-to-week variability in student performance, and the overwhelmingly positive feedback we received from students about their perceptions about the fairness of oral exams. Finally, we highlight the time required from instructional staff and provide recommendations and lessons learned for others considering implementing oral exams surrounding logistical hurdles, staff training, oral exam design, and grading.

When engaged in the initial design of a program, novice programmers and seasoned developers alike often sketch out---or, perhaps more famously, whiteboard---their ideas. However, with the introduction of natively multimodal Generative AI models, such diagrams may now function as a means of code generation in their own right. In this work, we perform an initial evaluation to understand how student-created decomposition diagrams can serve as prompts for code generation, with implications for teaching and assessing problem decomposition skills.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.