Document Type

Conference Proceeding

Publication Date

8-7-2026

Department

Department of Computer Science; Department of Psychology and Human Factors; Department of Engineering Fundamentals

Abstract

Background: Generative AI (genAI) is transforming educational research, offering new possi-bilities for conducting interviews while local sandboxing minimizes data privacy risks and hallucinations. Purpose: This work-in-progress presents the AI Autoethnography Assistant, a project investigating how large language models (LLMs) can support autoethnographic interview design and execution. Approach: Using prompt engineering grounded in Interpretative Phenomenological Anal-ysis, paraphrasing techniques, and structured follow-up questions, we developed protocols that incorporate po-sitionality and prompt reflection on origin stories and pivotal life moments. Outcomes: Conversations conclude at the user’s discretion, with the AI generating a thematic summary. We tested refined prompts across four platforms (Gemini, Claude, ChatGPT, Copilot), evaluating outputs for contextual richness and conceptual thickness using Perplexity AI for comparison. Conclusions: Results show genAI can serve as a flexible, reflexive interviewer, though consistency and depth vary by platform—Claude produced the richest descriptions. Future work includes piloting the tool with academic and community-based users.

Publisher's Statement

Copyright (c) 2026 Jyoti Suhag, Jennifer Drewyor, Kathryn Bugbee, Michelle Jarvie-Eggart, Lynn Albers, Leo Ureel. Publisher’s version of record: https://doi.org/10.24908/pceea.2026.21634

Publication Title

Proceedings of the Canadian Engineering Education Association Conference

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Version

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