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.

Ensuring equitable access to computing education for all students---including those with autism, dyslexia, or ADHD---is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.

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.

Grid displays are the most common form of augmentative and alternative communication device recommended by speech-language pathologists for children. Grid displays present a large variety of vocabulary which can be beneficial for a users’ language development. However, the extensive navigation and cognitive overhead required of users of grid displays can negatively impact users’ ability to actively participate in social interactions, which is an important factor of their language development. We present a novel interaction technique for grid displays, Predictive Anchoring, based on user interaction theory and language development theory. Our design is informed by existing literature in AAC research, presented in the form of a set of design goals and a preliminary design sketch. Future work in user studies and interaction design are also discussed.

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.