Erroneous examples are structured problem examples which incorporate deliberate errors that students can identify and correct to reinforce their understanding. Erroneous examples have been shown to be beneficial for student learning in various domains. However, designing effective erroneous examples requires careful planning and a deep understanding of common student misconceptions, posing a time burden on educators. In this work, we introduce a framework that leverages Large Language Models (LLMs) to automatically generate erroneous programming examples for use in introductory computer science education. We systematically evaluate the performance of various LLMs on the generation task, and find that LLMs are generally capable of generating meaningful erroneous examples along with accurate explanations. We present our preliminary findings and outline next steps for this study and future research.

Manually grading free response questions remains a persistent challenge in education. While such questions offer valuable opportunities for student learning and critical thinking, their evaluation often requires substantial time and effort from instructors or teaching assistants. In addition to the grading workload, open-ended responses are susceptible to inconsistencies in scoring and may reflect unclear expectations, both of which can undermine the effectiveness and fairness of the assessment process. To address these challenges, we employed an AI-based grading system integrated in PrairieLearn to automatically evaluate student submissions to free response questions using a predefined set of rubric items. This approach not only streamlines the grading process but also enables direct comparison between AI-generated rubric applications and human judgments, providing insight into alignment and potential discrepancies. These discrepancies provided valuable insight, allowing us to iteratively revise and clarify the rubric items. Our experiences with using the AI grading system across several computing courses suggest that even experienced educators face difficulties articulating rubrics that are both specific and interpretable. We furthermore argue that more attention should be given to the iterative development and evaluation of rubrics.

We report on our experiences fielding MeetingMayhem, an interactive game that we developed, which introduces students to fundamental concepts in network security, cybersecurity, and adversarial thinking. The game is intended for students who do not necessarily have any prior background in computer science. Assuming the role of agents, two players exchange messages over a network to try to agree on a meeting time and location, while an adversary interferes with their plan. Following the Dolev-Yao model, the adversary has full control of the network: they can see all messages and modify, block, or forward them. We designed the game as a web application, where groups of three students play the game, taking turns being the adversary. The adversary is a legitimate communicant on the network, and the agents do not know who is the other agent and who is the adversary. Through gameplay, we expect students to be able to (1) identify the dangers of communicating through a computer network, (2) describe the capabilities of a Dolev-Yao adversary, and (3) apply three cryptographic primitives: symmetric encryption, asymmetric encryption, and digital signatures. We conducted surveys, focus groups, and interviews to evaluate the effectiveness of the game in achieving the learning objectives. The game helped students achieve the first two learning objectives, as well as using symmetric encryption. We found that students enjoyed playing Meeting Mayhem. We are revising MeetingMayhem to improve its user interface and to better support students to learn about asymmetric encryption and digital signatures.