The field of accessible technology and low-resource language support is rapidly evolving, with a focus on developing innovative solutions to address the challenges faced by deaf and hard of hearing individuals, as well as those with limited access to language resources. Researchers are exploring the application of AI language technologies to improve accessibility, including the development of more accurate and inclusive language models. Notably, the use of back-translation techniques has shown significant promise in enhancing neural machine translation models for low-resource languages, demonstrating improvements in translation performance and highlighting the potential for more effective language support. Additionally, there is a growing emphasis on creating more interactive and engaging learning experiences, with the integration of artificial intelligence and visualization animation technology being used to develop systems that can recognize and generate mathematical calculations, providing students with a more intuitive understanding of complex mathematical concepts. Furthermore, the development of comprehensive multilingual question answering datasets, such as IndicSQuAD, is helping to address the lack of representation of low-resource languages in natural language processing. Overall, these advancements have the potential to significantly improve accessibility and language support, enabling more individuals to participate fully in various aspects of life. Noteworthy papers include: Data Augmentation With Back translation for Low Resource languages, which demonstrates the efficacy of back-translation in enhancing neural machine translation models, and Generating Narrated Lecture Videos from Slides with Synchronized Highlights, which presents an end-to-end system for automating the process of creating engaging video lectures. Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages provides a systematic review of strategies to address data scarcity in generative language modeling for low-resource languages, highlighting the need for more inclusive and equitable language systems.