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West, Harry 
ORCID: https://orcid.org/0000-0002-2704-5474 and Marvell, Alan D ORCID: https://orcid.org/0000-0001-8363-0793
(2026)
Exploring the use of generative AI podcasts to support students learning.
Journal of Geography in Higher Education.
doi:10.1080/03098265.2026.2682208
(In Press)
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16319 Marvell, A and West H (2026) Exploring the use of generative AI podcasts to support students learning.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (652kB) | Preview |
Abstract
Generative Artificial Intelligence (GenAI) is transforming higher education pedagogy, through increased use, curriculum design and engagement. While it can enhance geographical thinking and spatial analysis, concerns exist about superficial learning and effectiveness. This paper examines the pedagogical value of GenAI-generated audio resources, presenting a case study from a second-year undergraduate geography module in a UK university, “Climate Change: Challenges for the 21st Century,” where JellyPod was used to turn lecture slides into short podcasts. A survey with 45 students found that 90% believed the podcasts improved learning, citing clarity, brevity, and the ability to distil complex topics. The podcasts accessibility and flexibility were praised, with 92% integrating them into routines for independent learning. However, 47% noted the AI voice was “robotic” or “monotone,” and that some content was superficial and lacked interactivity. Overall, GenAI podcasts are effective, scalable tools for reinforcing content, but future improvements should focus on voice realism, interactivity, and personalisation to promote inclusive, student-centred geography education.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Uncontrolled Keywords: | Podcasts; GenAI; Digital pedagogy; Independent learning; Student-centred |
| Related URLs: | |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) H Social Sciences > H Social Sciences (General) L Education > L Education (General) |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Depositing User: | Alan Marvell |
| Date Deposited: | 08 Jun 2026 13:53 |
| Last Modified: | 08 Jun 2026 14:00 |
| URI: | https://eprints.glos.ac.uk/id/eprint/16319 |
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