Creating a Podcast Using Generative Artificial Intelligence to Support Student Learning of Organic Reaction Mechanisms

A podcast was created by Google NotebookLM artificial intelligence and applied to support learning in a Year 1 university organic chemistry course. The Generative AI (GenAI) was trained using an open access article presenting priority and selectivity rules for organic reaction mechanisms. The software performed very well in terms of background material, showing creativity. Coherent explanations of organic chemistry were also given, although some technical errors were made in chemical detail and there was some pedagogic confusion. The source material was edited and two further podcast versions created. All three podcast outputs were combined with academic curation to create a final output with minimal errors. The podcast was released to students, being advertised as AI-generated support material. Students who listened to the podcast reported it helped reinforce learning and was worth listening to. However, a number of students were concerned about ethical, societal, and sustainability concerns of using AI, and some actively chose not to engage with it. In summary, with academic curation, Google NotebookLM can make engaging scientific podcasts with limited workload. However, student attitudes to the use of AI in education must be carefully considered.

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Publication Details

Journal
Journal of Chemical Education
Published
2026-03-27
DOI
https://doi.org/10.1021/acs.jchemed.5c01652
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Creating a Podcast Using Generative Artificial Intelligence to Support Student Learning of Organic Reaction Mechanisms

David K. Smith
Journal of Chemical Education
Machine Learning in Materials Science
article

Creating a Podcast Using Generative Artificial Intelligence to Support Student Learning of Organic Reaction Mechanisms

David K. Smith
article en

Abstract

A podcast was created by Google NotebookLM artificial intelligence and applied to support learning in a Year 1 university organic chemistry course. The Generative AI (GenAI) was trained using an open access article presenting priority and selectivity rules for organic reaction mechanisms. The software performed very well in terms of background material, showing creativity. Coherent explanations of organic chemistry were also given, although some technical errors were made in chemical detail and there was some pedagogic confusion. The source material was edited and two further podcast versions created. All three podcast outputs were combined with academic curation to create a final output with minimal errors. The podcast was released to students, being advertised as AI-generated support material. Students who listened to the podcast reported it helped reinforce learning and was worth listening to. However, a number of students were concerned about ethical, societal, and sustainability concerns of using AI, and some actively chose not to engage with it. In summary, with academic curation, Google NotebookLM can make engaging scientific podcasts with limited workload. However, student attitudes to the use of AI in education must be carefully considered.

Journal of Chemical Education
University of York (GB)
Openalex Percentile: Top 6%
Machine Learning in Materials Science
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