Automatic speech recognition lecture transcriptions supporting AI created active and accessible e-learning in higher education

Purpose This paper addresses the research gap regarding ways automatic speech recognition can support AI in learning beyond just becoming a useful tool for the transcription of lectures for students with accuracy rates in English improving for many academic subjects. This support has eased some of the difficulties experienced by students who find concentrating on the content of a lecture while writing at the same time a barrier to learning. Research has shown that actively reviewing notes that are not necessarily made by students, can still result in successful outcomes. The proviso being that the interactions with lecture content and knowledge that needs to be learnt can happen in a way that enhances long term memory. Design/methodology/approach Lecturers in higher education often use a variety of e-learning systems to offer varying forms of gamification to support remembering, understanding and application of content taught during lectures. However, these systems may not necessarily enable the practice of higher order learning skills as will be demonstrated with a review of the features offered by 37 online platforms used in colleges and universities. Findings It is proposed that with the support of generative AI, it is possible to introduce more accessible interactive activities to enable students, including those with disabilities, the ability to analyze, evaluate and create personalized content linked to transcribed lecture notes. Originality/value The suggestions offered include the use of online multiformat activities that can be automatically checked for accuracy against the originally accessed transcriptions and their summaries.