Abstract
Machine Learning models have become more advanced than could have been supposed even a few years ago, often surpassing human performance on many tasks. Large language models (LLM) can produce text indistinguishable from human-produced text. This begs the question, how necessary are humans - even for tasks where humans appear indispensable? Qualitative Analysis (QA) is integral to human-computer interaction research, requiring both human-produced data and human analysis of that data to illuminate human opinions about and experiences with technology. We use GPT-3 and ChatGPT to replace human analysis and then to dispense with human-produced text altogether. We find GPT-3 is capable of automatically identifying themes and generating nuanced analyses of qualitative data arguably similar to those written by human researchers. We also briefly ponder philosophical implications of this research.
Degree
MS
College and Department
Physical and Mathematical Sciences; Computer Science
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Byun, Courtni L., "Dispensing With Humans in Human-Computer Interaction Research" (2023). Theses and Dissertations. 10158.
https://scholarsarchive.byu.edu/etd/10158
Date Submitted
2023-11-14
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd12996
Keywords
Natural language processing, human-computer interaction, qualitative research
Language
english