This working group is conducting a scoping review exploring how interpretivist research paradigms are articulated, applied, and represented in qualitative research in computer science education. Despite the prevalence of qualitative research in our field slowly growing, interpretivist assumptions remain inconsistently described and understood; the direction of influence between this inconsistency and enduring debates over which paradigms are endorsed in our field remains unclear.

Hidden curriculum plays a significant, yet often overlooked, role in shaping students' experiences, expectations, identities, and professional development (PD) in computing education. As computing programs continue to evolve in response to emerging technologies--i.e., artificial intelligence (AI), and changing industry expectations, understanding the implicit structures that influence student success becomes increasingly important. Students are increasingly expected to navigate collaboration, communication, co-curricular experiences (e.g., internships, research, etc.), and the use of technological tools. These expectations are rarely uncovered and made explicit within formal curricula.

As artificial intelligence (AI) progresses and becomes more ubiquitously used in education, the need to provide instruction around AI skills will increase. This work presents an opportunity for students to develop image manipulation skills through segmentation. Guided Undergraduate Training for Shark Segmentation (GUTSS) is a mobile application that enables students to use these skills while simultaneously learning more about marine anatomy. To make the connection between AI and science education, in-service science teachers can use the software inside and outside of the classroom to help students learn about different aspects of shark anatomy through the GUTSS. Through the application, teachers can enrich their classroom curriculum with technology, share materials, grade assignments, and view their students' work. GUTSS uses open-set object detection, image segmentation, and image manipulation to assist users with organ identification. Gamification within the application will make learning shark anatomy more engaging to students. Prior work from the NSF ITEST Award indicates teachers' willingness to integrate AI concepts into the classroom aligned with state standards. Further wireframing of the application from instructor perspectives is ongoing, to incorporate teacher viewpoints from mixed-methods survey responses on AI usage and anatomy. Future work aims to supplement shark dissection images from AI generation of anatomical image data sets, by collecting these data from students and teachers. Ultimately, the application will be able to identify non-aquatic organisms' anatomical features as a tool for students to learn.

Amidst the emergence of new engineering education doctoral programs nationwide, students are becoming increasingly curious about venturing into this field. In this panel session, we invite you to explore the personal journeys of four Ph.D. students from the University of Florida. These individuals made the bold decision to switch degree programs during their graduate school journey and are now part of the newly formed engineering education Ph.D. program at the university. Through candid guided discussion, this panel will delve into the reasons behind these transformative transitions, their impact on the participants' academic journeys, and the significance of engineering education research in shaping the future of engineering.

Engineers are frequently confronted with complex, unique, and challenging problems. Many of our most pressing engineering problems contain ambiguous elements, and a core activity of engineering is being able to solve these complex problems effectively. While engineering problems are often described as ambiguous, ambiguity has not been clearly defined in the literature in the context of engineering problem solving. This work-in-progress paper describes our initial results to understand how ambiguity is experienced during engineering problem solving. We interviewed both engineering students and engineering professionals about ambiguous problems they have encountered. We found that both groups identified technical ambiguity as the core element of engineering problem solving. They also described differences between classroom and workplace problems, with students describing classroom problems as "purposefully" ambiguous. Students had strong negative emotional reactions to ambiguity, in contrast to professionals who seemed to accept ambiguity as a common element in engineering problems. Our initial findings suggest that changes to engineering education practice that allow students to become comfortable with ambiguity would better prepare them for the ambiguous problems they will face in the workplace.