Figure 1: The three screens that comprise the user interface of our prototype.(1) VSD programmers choose to upload or capture an image to use in a VSD.(2) The application automatically generates a set of potential hotspots for use in the VSD and the programmer can choose to edit, use, or delete these hotspots.They can also manually add their own hotspots.Once all of the hotspots are created, they can use the canvas to draw the hotspots on the image.(3) Once they have finished configuring the VSD, users can see a preview of what the VSD looks like and interact with the created hotspots.

While hackathons are often celebrated for their impact on participants, less attention has been given to the unique leadership development opportunities for the student organizers who create and run these events. Unlike traditional classroom settings, where leadership and collaboration skills are typically delayed until upper-level courses, hackathon organizers must tackle these challenges earlier on. In managing a large-scale, formative event, student organizers take on roles that require decision-making, teamwork, and project management. This poster explores the experiences of student organizers at Anonymous Hackathon, emphasizing how this informal learning opportunity complements gaps in the traditional computer science (CS) curriculum by fostering essential leadership and collaboration skills earlier in students' academic careers.

Millions of people worldwide rely on alternative and augmentative communication devices to communicate. Visual scene displays (VSDs) can enhance communication for these individuals by embedding communication options within contextualized images. However, existing VSDs often present default images that may lack relevance or require manual configuration, placing a significant burden on communication partners. In this study, we assess the feasibility of leveraging large multimodal models (LMM), such as GPT-4V, to automatically create communication options for VSDs. Communication options were sourced from a LMM and speech-language pathologists (SLPs) and AAC researchers (N=13) for evaluation through an expert assessment conducted by the SLPs and AAC researchers. We present the study’s findings, supplemented by insights from semi-structured interviews (N=5) about SLP’s and AAC researchers’ opinions on the use of generative AI in augmentative and alternative communication devices. Our results indicate that the communication options generated by the LMM were contextually relevant and often resembled those created by humans. However, vital questions remain that must be addressed before LMMs can be confidently implemented in AAC devices.

Generative models are now capable of producing natural language text that is, in some cases, comparable in quality to the text produced by people. In the computing education context, these models are being used to generate code, code explanations, and programming exercises. The rapid adoption of these models has prompted multiple position papers and workshops which discuss the implications of these models for computing education, both positive and negative. This paper presents results from a series of semi-structured interviews with 12 students and 6 instructors about their awareness, experiences, and preferences regarding the use of tools powered by generative AI in computing classrooms. The results suggest that Generative AI (GAI) tools will play an increasingly significant role in computing education. However, students and instructors also raised numerous concerns about how these models should be integrated to best support the needs and learning goals of students. We also identified interesting tensions and alignments that emerged between how instructors and students prefer to engage with these models. We discuss these results and provide recommendations related to curriculum development, assessment methods, and pedagogical practice. As GAI tools become increasingly prevalent, it's important to understand educational stakeholders' preferences and values to ensure that these tools can be used for good and that potential harms can be mitigated.