As generative artificial intelligence (genAI) tools become embedded in computing education workflows, it is essential to understand how students use such systems to learn beyond introductory programming. This work investigates the relationship between the use of genAI by students and their conceptual understanding of mathematical and algorithmic principles in an undergraduate machine learning course with 134 students. We deploy a course-specific, custom-interfaced large language model (LLM), CubBot, to examine (1) how students interact with genAI in an upper-level CS course via an analysis of anonymized chat logs and (2) how genAI usage relates to students' conceptual understanding and learning outcomes via a randomized, controlled assessment comparing performance with and without CubBot access. This research contributes to the growing body of work on genAI-supported education by providing one of the first empirical investigations into genAI's relationship with conceptual learning in an upper-level CS course.

Undergraduate computer science (CS) students often struggle in theoretical, proof-based courses, which require formal proof and rigorous reasoning skills. Despite ongoing efforts to improve these courses, challenges remain due to the complex nature of the material and CS students' perception that it has limited practical relevance. In this work, we take a human-centered approach to investigate the challenges and needs of both instructors and students in such courses. Through semi-structured interviews with 16 instructors and 20 students followed by a thematic analysis of the resulting data, we develop the CNPE framework, which captures the Challenges and Needs in Proof Education. While rooted in our local experience, the CNPE framework can be adapted for broader application, underscoring the value of understanding stakeholder challenges and needs before designing educational interventions.

As undergraduate computer science classes grow in size, institutions increasingly rely on asynchronous computer-based assessments. To investigate whether exam timing reveals evidence of cheating, we analyze 21,403 submissions from 51 asynchronous exams across two undergraduate courses in this retroactive study. We extend prior research on proctored multiday exams by introducing a comparison in student performance trends between two distinct assessment modes: on-site proctored and off-site unproctored. We find that performance declines throughout the exam window in both modes. We observe a weak negative correlation between start time and performance, with standardized scores decreasing by 0.14 points per hour (on-site proctored) and 0.61 points per hour (off-site unproctored). In addition, start-time distributions and student surveys reveal behavioral differences. On-site proctored exams follow a centered start-time distribution, likely influenced by a reserved lecture hour. In contrast, off-site unproctored exams show a left-tailed distribution, with most students starting later than intended. This pattern suggests that greater scheduling flexibility leads to later exam starts, potentially exacerbating performance declines due to academic procrastination.

The rise of online assessments has motivated the development of randomized question banks, with randomization referring to the generation of different variants of a question. Although not all randomization efforts are equally effective in generating question isomorphs, the current classification of questions solely as randomized or not fails to address the varying degrees of randomization. To address this limitation in describing the diversity of randomization designs, we introduce a framework that outlines six distinct randomization levels. Additionally, we designed practical guides to assist educators in effectively using the framework, aligning with their pedagogical objectives. Through our application of this framework to classify around 200 questions from two courses, we further highlight the generalizability of the framework and reveal insights into the considerations and challenges associated with incorporating question randomization into computer science curricula.