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Standardized, validated questionnaires are vital tools in HCI research andhealthcare, offering dependable self-report data. However, their repeated usein longitudinal or pre-post studies can induce respondent fatigue, impactingdata quality via response biases and decreased response rates. We proposeutilizing large language models (LLMs) to generate diverse questionnaireversions while retaining good psychometric properties. In a longitudinal study,participants engaged with our agent system and responded daily for two weeks toeither a standardized depression questionnaire or one of two LLM-generatedquestionnaire variants, alongside a validated depression questionnaire.Psychometric testing revealed consistent covariation between the externalcriterion 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 standardizedquestionnaire significantly more repetitive compared to the variants. Ourfindings highlight the potential of LLM-generated variants to invigoratequestionnaires, fostering engagement and interest without compromisingvalidity.

Keeping Users Engaged During Repeated Administration of the Same Questionnaire: Using Large Language Models to Reliably Diversify Questions