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Using ChatGPT for human-computer interaction research: a primer.

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Summary
This summary is machine-generated.

ChatGPT demonstrates strong validity for analyzing text data in Human-Computer Interaction research. It accurately provides sentiment scores and summaries, correlating well with human analysis and other methods.

Keywords:
application programming interface (API)human-subject researchprompt engineeringreproducibility

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Area of Science:

  • Human-Computer Interaction
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Text analysis is crucial in Human-Computer Interaction (HCI) for understanding user experiences.
  • Traditional methods can be time-consuming and resource-intensive.
  • The emergence of advanced language models like ChatGPT necessitates exploring their utility in HCI research.

Purpose of the Study:

  • To investigate the validity of ChatGPT as a tool for text analysis in Human-Computer Interaction.
  • To assess ChatGPT's performance in analyzing diverse text data types, including questionnaires, interviews, and think-aloud protocols.
  • To compare ChatGPT's analytical outputs against established human and computational analysis methods.

Main Methods:

  • Applied ChatGPT to analyze textbox questionnaire responses from augmented-reality (AR) interface studies.
  • Utilized ChatGPT for meta-summarization of transcribed interview data from AR interface simulations.
  • Employed ChatGPT to summarize transcribed think-aloud data comparing real and replica artworks.
  • Adopted a hierarchical approach for text batch processing and aggregation.
  • Evaluated ChatGPT's outputs using criterion and face validity measures, comparing against human ratings, rule-based analysis, and independent content analysis.

Main Results:

  • ChatGPT generated sentiment scores for AR interfaces with extremely strong correlation (r > 0.99) to human ratings and rule-based sentiment analysis.
  • Meta-summaries of interview data provided meaningful insights into interface qualities, showing substantial overlap with independent content analysis.
  • Summaries of think-aloud data effectively highlighted subtle differences between real and replica objects, aligning with keyword analysis.

Conclusions:

  • ChatGPT can be a valid and efficient tool for text analysis in Human-Computer Interaction research.
  • The study demonstrates ChatGPT's capability in sentiment scoring, meta-summarization, and identifying nuanced distinctions in qualitative data.
  • With careful implementation and validation, ChatGPT offers a promising approach to augment traditional HCI text analysis techniques.