Interest in learning American Sign Language (ASL) is growing across higher education institutions in North America, as reflected in rising enrollments. Yet this growth is constrained by limited program availability and few opportunities to practice outside the classroom. AI-based technologies show promise for supporting ASL learning, but educators – who bring essential pedagogical, linguistic, and cultural expertise – have been largely absent from conversations on the design of these tools, with prior work focusing primarily on learners. To address this, we conducted formative interviews with eleven Deaf and one hearing ASL instructor, followed by two focus groups with six Deaf educators, to examine how AI tools could support ASL education. Findings revealed priorities for technology design and considerations for integration into existing pedagogical practices, with attention to curricular, linguistic, and access factors. We offer insights for designing and researching technologies aimed at (1) providing adaptive, structured feedback on signing performance and (2) supporting immersive conversational practice with virtual signing partners.

The latent feature relational model (LFRM) for graphs represents each node as having binary memberships in one or more communities. The community memberships can be represented in form of a binary vector and LFRM defines the link probability between any pair of nodes as a bilinear function of their community membership vectors. Moreover, using nonparametric Bayesian prior - Indian Buffet Process - on the community membership matrix enables learning the number of communities automatically from the data. However, despite its modeling flexibility, strong link predictive performance, and nice interpretability of binary embeddings, inference in LFRM remains a challenge and is typically done via MCMC or variational methods. These methods can be slow and may take a long time to converge. In this work, we apply the small variance asymptotics idea to the non-parametric Bayesian LFRM, utilizing the connection between exponential families and Bregman divergence. This leads to an overlapping k-means like objective function for the nonparametric Bayesian LFRM, which can be optimized using generic or specialized solvers. We also propose an iterative greedy algorithm to optimize the objective function and compare our approach with other inference methods on several benchmark datasets. Our results demonstrate that our inference algorithm is competitive to methods such as MCMC while being much faster.