Although teaching ethical computing practices is essential for developing responsible technologists, ethics is still too frequently overlooked in computing education. As a consequence, students may graduate not only without the skills to identify and address ethical dilemmas, but also without an understanding of why ethical decision-making is essential, in the first place. To overcome this gap, we have created a set of easily adoptable, experiential ethics-focused [hidden] designed to systematically introduce students to core ethical concepts in computing and to emphasize the real-world consequences and importance of ethical computing practices. In the following paper, we report on instructor observations regarding the inclusion of these ethics-focused computing labs at a diverse set of categorically distinct partner institutions. The complete project materials are openly available on our website: [hidden]

In this study, we analyze the effectiveness of generative AI in diagnosing student misconceptions in an undergraduate operating systems class. Notably, we ask students to assess whether an LLM's feedback correctly diagnosed what mistaken belief was the proximate cause of their errors. We tested 3 models: ChatGPT, Claude, and Gemini. Gemini was the most consistent in terms of perceived student correctness. ChatGPT received the highest student ratings. In a qualitative assessment, the LLMs were correct 42 percent of the time in diagnosing the students' misconceptions. In a quantitative assessment, 63.5 percent of LLM responses were perceived as exactly or almost exactly correct. We conclude that LLMs can provide valuable formative feedback but are not yet ready to be the sole source of insights.

Our [hidden] initiative helps participants learn how to design ethical software while introducing them to essential concepts in Artificial Intelligence and Machine Learning (AI/ML). Several of these developed labs focus on foundational ethics-focused topics. These experiential interactive modules highlight why ethics in AI/ML matters and provide practical experiences that reveal the diverse ways ethics-focused topics can influence modern systems. The tutorial is suitable for a broad audience within the software engineering community—from students to seasoned professionals—who wish to better understand ethical implications across domains and ensure that the software they develop is ethical and fair. Complete project materials are openly available on our website: [hidden]

Students frequently choose correct answers for incorrect reasons, leaving traditional formative assessments unable to surface the misconceptions that matter most for learning. We introduce Structured Post-Evaluation Interviews and Remediation (SPEIR), a workflow that extends two-tier Justified Multiple-Choice Questions (JMCQs) with guided discussions and targeted recovery opportunities to surface and address those hidden misunderstandings. Implemented across ten course sections and compared with a traditional MCQ control, SPEIR showed that correctness alone substantially overestimates understanding, while per-question analyses revealed higher rates of fully correct reasoning in SPEIR sections. Students who completed recovery quizzes demonstrated notable gains, and instructors reported that SPEIR enabled efficient, focused feedback. These results suggest that SPEIR is a scalable approach for integrating diagnostic assessment with timely remediation.

This paper is a case-study on utilizing learning analytics to evaluate the success of integrating Smart Learning Content (SLC) into a project-based undergraduate Python programming course delivered through an online learning platform. We showcase how integration of the logging capabilities of SLC with the platform enabled us to study students' engagement with the SLC activities, and examine connection between the SLC usage and student learning outcomes. There are large publicly available repositories of SLC materials, yet their integration into a specific course and evaluation of its success are often not straightforward. By providing access to several SLC types, such as program construction examples, code animations, and parsons puzzles, we aimed to bridge the gap between static conceptual reading and programming projects already present in the course. To evaluate the integration of SLC, we released the augmented course in the Spring 2025 semester to 252 students at 20 community colleges across the US and analyzed collected interaction logs.