Abstract
BACKGROUND: Over 96% of adult women face health issues, with 70% experiencing conditions like infections. Mobile health education is increasingly popular but faces challenges in personalization and readability. Artificial intelligence (AI) chatbots provide tailored support, and a discrete choice experiment can help in understanding user preferences to improve chatbot design. OBJECTIVE: This study aims at exploring the preferences of women toward AI chatbots to improve health education communication and user experience. METHODS: A discrete choice experiment was conducted, identifying 6 main attributes of AI chatbots: response accuracy, legibility, service cost, background information collection, information utility, and content provision. A total of 957 female participants from a hospital in Hebei Province participated, choosing between 2 hypothetical chatbots or opting for neither (a no-choice option). The conditional logit model was used to estimate user preferences. RESULTS: A total of 957 participants were included in the analysis. The results showed that participants preferred a chatbot with 100% response accuracy (β=0.940, P<.001; 95% CI 0.624 to 1.255), very easy to understand information (β=0.907, P<.001; 95% CI 0.634 to 1.180), a service fee of CN ¥0/month (β=-0.095, P<.001; 95% CI -0.108 to -0.082; a currency exchange rate of US $1=CN ¥7.09 was applicable), practical information utility (β=1.085, P<.001; 95% CI 0.832 to 1.338), and provision of disease-related knowledge (β=0.752, P<.001; 95% CI 0.485 to 1.018). Whether or not to allow the collection of background information (only question and answer information) has no significant impact on women's choice preferences. Additionally, participants were willing to pay an additional CN ¥9.916 (95% CI 6.843 to 12.292) for 100% response accuracy, CN ¥9.567 (95% CI 6.843 to 12.292) for "very easy to understand" information, and CN ¥11.451 (95% CI 8.704 to 14.198) for the "very practical" information utility. Additionally, they were willing to pay CN ¥7.931 (95% CI 4.975 to 10.886) for "knowledge of diseases" compared to "gender knowledge" (CN ¥2.602, 95% CI -0.551 to 5.756). The relative importance of the chatbot attributes indicated that information utility (1.085/3.858, 28.12%) and response accuracy (0.940/3.858, 24.37%) were the most influential factors in participants' preferences. CONCLUSIONS: AI chatbots designed for female users should focus on high response accuracy, clear content, free access, privacy protection, practical information, and disease knowledge to attract users and enhance health education.