Automatic speech recognition lecture transcriptions supporting AI created active and accessible e-learning in higher education
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.
Active e-learning, Artificial intelligence, Automatic speech recognition, Disability, Higher education, Lecture transcriptions
1-10
Draffan, E.A.
021d4f4e-d269-4379-ba5a-7e2ffb73d2bf
Wald, Mike
90577cfd-35ae-4e4a-9422-5acffecd89d5
Ding, Chaohai
d07f64ec-a5c8-4167-9d34-9a3913e4dd1a
Li, Yunjia
dae5a6ac-b79d-4528-944e-53a2fd46378a
18 March 2026
Draffan, E.A.
021d4f4e-d269-4379-ba5a-7e2ffb73d2bf
Wald, Mike
90577cfd-35ae-4e4a-9422-5acffecd89d5
Ding, Chaohai
d07f64ec-a5c8-4167-9d34-9a3913e4dd1a
Li, Yunjia
dae5a6ac-b79d-4528-944e-53a2fd46378a
Draffan, E.A., Wald, Mike, Ding, Chaohai and Li, Yunjia
(2026)
Automatic speech recognition lecture transcriptions supporting AI created active and accessible e-learning in higher education.
Journal of Enabling Technologies, 20 (1), .
(doi:10.1108/JET-11-2024-0084).
Abstract
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.
This record has no associated files available for download.
More information
Accepted/In Press date: 8 October 2025
Published date: 18 March 2026
Additional Information:
Publisher Copyright:
© 2025 Emerald Publishing Limited
Keywords:
Active e-learning, Artificial intelligence, Automatic speech recognition, Disability, Higher education, Lecture transcriptions
Identifiers
Local EPrints ID: 510778
URI: http://eprints.soton.ac.uk/id/eprint/510778
ISSN: 2398-6263
PURE UUID: 53e82a58-7da3-4380-8bc6-e6b85bcbea2e
Catalogue record
Date deposited: 21 Apr 2026 16:57
Last modified: 22 Apr 2026 01:41
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Contributors
Author:
E.A. Draffan
Author:
Mike Wald
Author:
Chaohai Ding
Author:
Yunjia Li
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