Code for Convenience: Python and R Solutions for Preparing Zoom Transcripts
Johnathan Chisam, Stephanie J. H. Frost, Jocelyn Elizabeth Nardo · Journal of Chemical Education · 2026
High Resolution Image Download MS PowerPoint Slide Preparing AI-generated interview transcripts for analysis often requires substantial postprocessing before they are suitable for qualitative research. Common tasks include reformatting poorly structured text, removing extraneous labels such as timestamps and speaker tags, and correcting transcription errors that can obscure meaning. These tasks are not only time-consuming but also introduce inconsistencies across transcripts if performed manually, creating a barrier for researchers who wish to move efficiently from data collection to analysis. In this technology report, we present and compare two automated solutions (one written in Python and one in R) that streamline this preparation process for Zoom-generated transcripts. Both codes are designed to detect and eliminate extraneous information, standardize transcript formatting, and ensure text readability, ultimately reducing the need for labor-intensive manual editing. By automating these steps, the codes improve efficiency, support consistency across datasets, and make it easier for researchers to focus on interpretive and analytic aspects of qualitative work. We believe that this contribution will have broad utility for qualitative researchers across disciplines, particularly in fields where large volumes of interview data are collected and timely analysis is critical. We argue that the automation of transcript preparation represents an important step toward lowering technical barriers in qualitative research, improving transparency and reproducibility, and expanding the accessibility of computational tools to scholars who may not have advanced programming expertise.