2026-06-27 –, Room 403 PC Desk (Seats 30)
As historians increasingly rely on digital tools to document lived experience at scale, artificial intelligence (AI) transcription has emerged as a practical response to the labor-intensive work of oral history. Yet the global turn toward automated transcription raises fundamental questions about how historical knowledge is shaped when spoken testimony is rendered into text by algorithmic systems. This paper examines these issues through a case study of the Korean Memories Project, a crowdsourced oral history initiative documenting the experiences of elderly South Koreans aged eighty and above who lived through Japanese colonial rule, the Korean War, and rapid postwar modernization—processes deeply embedded in global and transnational histories.
Using fifty interviews recorded in diverse community settings, this study compares two AI transcription systems: ClovaNote, developed specifically for Korean-language speech, and TurboScribe, based on OpenAI’s Whisper model. A mixed-methods analysis of representative transcripts evaluates transcription accuracy alongside qualitative patterns that affect historical interpretation. While both systems generate text suitable for indexing and large-scale archival use, they differ in omission patterns and error types. More significantly, both systems consistently normalize dialect, remove repetitions and hesitations, and restructure speech to enhance narrative coherence.
We argue that this process of textual standardization reflects a broader epistemological tension between AI systems optimized for readability and information extraction and oral history methodologies that treat pauses, affect, and speech patterns as historically meaningful. By centering Korean-language testimony from elderly speakers whose linguistic practices often fall outside dominant AI training data, this paper highlights how AI-mediated transcription reshapes what becomes legible in global historical archives. The paper concludes by proposing a hybrid, human-centered framework for AI-assisted transcription applicable to large-scale oral history projects worldwide.
Oral history; Digital history; AI transcription; Memory and testimony; Korean history
Alice Wrigglesworth is an Assistant Professor of English and English Program Coordinator at George Mason University Korea. A specialist writing instructor with over twenty years of international teaching experience, her research focuses on the Scholarship of Teaching and Learning, including peer feedback, culture in the classroom, and L2 writing pedagogy. She is the PI of the Korean Memories Project, an interdisciplinary oral history initiative, and conducts research on AI‑assisted transcription, translation, and the ethical use of generative AI in education and multilingual historical research.