Fingerspelling, the manual representation of alphabetic characters, is a core element of sign languages. Learners report that reading back fingerspelling is among the most challenging aspects of learning sign languages, primarily due to limited practice opportunities. This paper presents AuslanSpell, a novel technology designed to enhance proficiency in Australian Sign Language (Auslan) fingerspelling. AuslanSpell allows learners to input any English word and produces 3D animated models that articulate the corresponding Auslan signs, featuring smooth hand transitions. In tests with 33 novice signers, after brief interaction with AuslanSpell, participants performed above chance on beginner multiple-choice tasks, correctly identified first and last letters more often than middle letters in free-text tasks, and reported high satisfaction – especially with features such as adjustable signing speeds and rotatable views. The results confirm AuslanSpell’s potential in enhancing the comprehension and engagement of fingerspelling learners. AuslanSpell will be available through Auslan Signbank and Apple App Store, across iOS, iPadOS, macOS, and the web.

3D printed models (3DPMs) are increasingly used to support the education of students who are blind or have low vision (BLV). As 3DPMs are more widely-adopted, educators are using more complex multi-part models. However, with this increased complexity comes additional challenges for their use, such as supporting audio labels of multiple parts as well as guiding the assembly and disassembly of the model. This work explores the co-design and evaluation of a system that supports the use of multi-part 3DPMs by BLV students. Working with BLV adults and children, as well as educators, an iPad application was developed to support interaction with an insect model, including speech interaction and support for assembly. Evaluation showed that the system was strongly enjoyed by students and educators were enthusiastic as they believed it would increase classroom engagement and inclusion, and its support for voice annotation could be used for assessment.