Artificial Intelligence and Machine Learning in Fostering Inclusion of People with Hearing Impairments Using EMoS in Institution of Higher Education in Uganda

Although a literature review reveals a multitude of AItools for people with hearing impairment, there was little on the user experience during interactive sessions, audio and video facilitated sessions. This research paper examined the role of Artificial Intelligence and Machine Learning in aiding learning of People with hearing impairment through Embedded and Mobile Systems for inclusive digital education in Uganda. The study employed a descriptive research design, using qualitative research approaches for data collection and analysis. The sample included 15 students with hearing impairment in universities and graduates from universities. Data was collected from one-on-one written interviews and open-ended questionnaires guided by a sign language interpreter. The findings highlighted the importance of AI and ML developments in enhancing learning of persons with hearing impairments. However, findings also reveal that there still exists audio to text transcription errorsthat people with hearing impairments would browse through digital devices and find it hard to interact with videos and audio resources since some captions and subtitles had errors. There a scarcity of AI options for audio to automated sign language transcription which does not relying on human persons mimicked in the ethnic nature of people. The study recommended training persons with hearing impairments with key AI tools that supports their reading, interaction and giving feedback. Education ministries in collaboration with the ministry of ICT and NGOs should work to ensure live online tutorials adopt text to audio transcription, audio-to-text transcription, sign language to text transcription and introducing automated voiceover buttons in embedded and mobile systems. The study also recommended educators to work with IT experts to design and develop sign language transcription tools in digital devices to support learning of person with hearing impairments using Embedded and Mobile Systems for inclusive digital education within the ethnicity of students.