As computing cohorts grow in scale, designing assessments that foster communication, collaboration, and meaningful learning becomes increasingly challenging. This lightning talk shares a pedagogical strategy implemented in a large-scale (over 300 students) software design and engineering course at a Southeastern university in the United States. In this iteration, teams of computer science (CS) students created two video-recorded podcast chapters, each 30 to 60 minutes long, as part of a conceptual assessment. Each chapter focused on a key topic in software design and engineering, one on three design patterns (e.g., the Factory Method Pattern) and another on code smells, refactoring, and test-driven development. Podcasts, now widely popular across streaming platforms, offered students an accessible and creative medium to communicate technical concepts while practicing professional collaboration. The team-based format aimed to promote interaction, reinforce conceptual understanding, and develop communication skills as all members planned, scripted, and discussed each episode together. Rather than focusing only on outcomes, this activity emphasized process and reflection as students explained, questioned, and built on one another's ideas. This lightning talk is presented to gather feedback from the computing education community on this instructional strategy and possibly collaborate on further extensive research on student-created artifacts in large-scale CS courses.

As artificial intelligence (AI) tools like ChatGPT become more common, their role in computer science (CS) education continues to evolve, especially in large courses with time-limited assessments. This lightning talk presents an observation from a large-scale (over 300 students) introductory software design and engineering course at a university in the Southeastern United States. During a 30-minute, open-notes assessment where students were allowed to use generative AI, they were asked to extend one user story in an existing codebase they had previously built for the course. The task followed an all-or-nothing grading approach that required a fully functional user story implementation for credit. Although students often support using AI for learning, their reactions revealed a gap between what they expected AI to do and the actual thinking required to solve real problems quickly: Some students even expressed frustration, noting that AI tools offered little help under time pressure. To close the experience, we held a reflection lecture where students analyzed the role of AI as a tool and discussed its purpose in supporting augmented intelligence rather than replacing human reasoning. This case illustrates how assessment design can expose the limits of generative AI as a learning aid and highlights the importance of helping students build awareness of time, effort, and reflection when using these tools. The goal of this talk is to share these insights, invite discussion on AI use under time constraints, and explore how students adapt or resist adapting when AI cannot ''think fast enough'' for them.

Active learning has been found to increase student performance, particularly in science, technology, engineering, and mathematics (STEM) disciplines [2].However, adopting active learning in large courses is challenging, especially in scenarios involving hundreds of students in the same classroom.The larger the class, the more challenging it is to navigate and support students' critical thinking skills and study habits.This limits effective follow-ups on students' learning processes and forces instruction to rely heavily on automated methods based on standardized summative assessments and strategies that focus arguably more on grading than learning [1,3].Due to the current scale of large courses in computer science and the ease of access required, instructors are asked to rely on methods that are easy to design and implement, one being the use of study sheets to support student preparation for tests.This poster reports preliminary findings from a strategy that required students to create study sheets for an in-class test in an introductory software engineering course.A total of 372 students from four separate large sections (70+ students) voluntarily reported on their work for this strategy.Two sections were asked to submit two study sheets in two separate assignments that counted as half of their evaluation grade, both submitted within a week of each other.The remaining sections worked on study cheats (encouraged but ungraded) without required submission.We report preliminary findings that suggest significant differences between both modalities when gender is considered concerning students' perceived performance and dedication to the study sheet creation.We report on students' perceived performance and self-reported dedication, considering 368 responses: our analysis considered a gendered classification between male and female students.Four students were not considered due to the small subgroup sample.We found no significant differences between the two groups when analyzing them as a whole.Nonetheless, we observed significant differences in students' perceived performance and selfreported dedication regarding the first and second cheat sheets when comparing students' genders.Particularly, female students who were not asked to submit their study sheets reported the highest rates of perceived performance