Codified Historical Method and Automated Life-Story Tools

The history profession has reacted to the rise of AI by expressing a mix of admiration for its research potential and a deep concern that AI interpretation will rewrite history, literally. Social historians, and oral historians in particular, sit closest to this impact, because commercial tools now collect the ordinary lives these disciplines illuminate. Living-memoir platforms such as StoryWorth, Remento, Autobiographer, and new applications for Ancestry use algorithmic question protocols or conversational AI to elicit an older adult's reminiscences, then reformulate them into an audience-sensitive narrative. I call this two-stage process Automated Biographical Elicitation and Re-tell (ABER) and describe its product as auto-autobiography. Commercially viable ABER substitutes for labor once performed by families themselves or, in disciplined form, by oral historians. The tools are cheap and tireless, lowering the cost of later-life review and potentially benefiting older adults and the families who inherit their stories. Why then do oral historians balk?

The broader question is how these tools differ from the parallel work of historians. Vendors and consumers alike call the product history. But historical inquiry, despite considerable internal debate about its actual methods, points toward specific ethics, values, and practices. My work seeks to codify each, develop the language in rubric form, to facilitate useful comparison of ABER and human-conducted practice.

That comparison is not straightforward. Prior to the development of commercial AI models, the history profession engaged in at least three relevant modern efforts to identify and define what makes its inquiry unique (NCHS 1996; AHA 2016; OHA 2018). These projects served differing purposes, from school standards to degree-program outcomes to practitioner guidance, and none was written to be compared against the new technology. The AHA's 2025 principles on AI address history education, not the elicitation of life stories. This project revisits the earlier efforts alongside the language used by computer scientists designing systems for elicitation and re-telling, situating the two vocabularies in a form that can be shared and facilitating comparative evaluation. I then use the OHA's guidance on interview follow-up questioning, warning against superficial responses and naming candor as an ethical and methodological outcome, to assess rubric language.

Selected references

American Historical Association. 2016. "AHA History Tuning Project: 2016 History Discipline Core." December 2016. https://www.historians.org/resource/history-discipline-core/

American Historical Association. 2025. "Guiding Principles for Artificial Intelligence in History Education." Approved by AHA Council, July 29, 2025. https://www.historians.org/resource/guiding-principles-for-artificial-intelligence-in-history-education/

Cheng, Myra, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, and Dan Jurafsky. 2026. "Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence." Science 391:eaec8352. https://doi.org/10.1126/science.aec8352

Larson, Mary. 2026. "Oral History Encounters AI: An Exploration of Core Principles and Best Practices, Context and Consent." Oral History Review 53 (1): 93–110. https://doi.org/10.1080/00940798.2026.2625663

Merrill, Natalie, and Robyn Fivush. 2016. "Intergenerational Narratives and Identity across Development." Developmental Review 40:72–92. https://doi.org/10.1016/j.dr.2016.03.001

National Center for History in the Schools. 1996. National Standards for History. Basic ed. Los Angeles: National Center for History in the Schools, University of California, Los Angeles. https://phi.history.ucla.edu/nchs/history-standards

Oral History Association. 2018. OHA Principles and Best Practices. Adopted October 2018. https://oralhistory.org/principles-and-best-practices-revised-2018/

Pinquart, Martin, and Simon Forstmeier. 2012. "Effects of Reminiscence Interventions on Psychosocial Outcomes: A Meta-Analysis." Aging & Mental Health 16 (5): 541–58. https://doi.org/10.1080/13607863.2011.651434

Reeves, Troy, and Sarah Milligan. 2018. "2018 Principles and Best Practices Overview." In OHA Principles and Best Practices. Oral History Association. https://oralhistory.org/principles-and-best-practices-revised-2018/

Sheftel, Anna, and Stacey Zembrzycki. 2026. "Oral History Is Not Just Data: Amplifying Humanistic Practices in the Age of Artificial Intelligence." Main essay in "A Roundtable on Oral History and Artificial Intelligence: Making a Case for Humans." Oral History Review 53 (1). https://doi.org/10.1080/00940798.2026.2618280

Talaei, Shayan, Meijin Li, Kanu Grover, James Kent Hippler, Diyi Yang, and Amin Saberi. 2025. "StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework." In Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25). New York: ACM. https://doi.org/10.1145/3746059.3747681

Thompson, Paul, and Joanna Bornat. 2017. The Voice of the Past: Oral History. 4th ed. Oxford University Press.

Wasik, Bill. 2025. "A.I. Is Poised to Rewrite History. Literally." New York Times Magazine, June 16, 2025. https://www.nytimes.com/2025/06/16/magazine/ai-history-historians-scholarship.html