Standardized, validated questionnaires are vital tools in HCI research and
healthcare, offering dependable self-report data. However, their repeated use
in longitudinal or pre-post studies can induce respondent fatigue, impacting
data quality via response biases and decreased response rates. We propose
utilizing large language models (LLMs) to generate diverse questionnaire
versions while retaining good psychometric properties. In a longitudinal study,
participants engaged with our agent system and responded daily for two weeks to
either a standardized depression questionnaire or one of two LLM-generated
questionnaire variants, alongside a validated depression questionnaire.
Psychometric testing revealed consistent covariation between the external
criterion and the focal measure administered across the three conditions,
demonstrating the reliability and validity of the LLM-generated variants.
Participants found the repeated administration of the standardized
questionnaire significantly more repetitive compared to the variants. Our
findings highlight the potential of LLM-generated variants to invigorate
questionnaires, fostering engagement and interest without compromising
validity.

利用大型语言模型（LLMs）生成多样的问卷版本，保持良好的心理测量学特性，以解决长期或前后研究中回应者疲劳的问题，从而提高问卷的参与度和信效度。