Factors influencing artificial intelligence (AI) literacy in the age of generative AI chatbots for health information seeking
Yang Liu, Ying-Yueh Su, Anas Alhur, Salman Bin Naeem
Abstract
Artificial intelligence (AI) literacy plays a significant role in effectively communicating with AI generative chatbots for seeking health information or advice, as well as communicating with healthcare professionals and public health officials. The study aims to identify the factors influencing AI literacy and its subsequent influence on consumers’ behavioral intentions toward seeking health information using generative AI chatbots. A nine-factor measurement model (INS, ISE, IFO, AIH, ANX, AVD, AIL, BI, AIC) was developed with the help of the Theory of Planned Behavior (TPB) and tested using structural equation modeling. The results of SEM analysis demonstrate that IFO (β = 0.138, CR = 2.731, p < .05), ANX (β = 0.175, CR = 3.103, p < .05), and AVD (β = 0.158, CR = 3.855, p < .05) had a statistically positive significant influence on AI literacy. Similarly, BI (β = 0.128, CR = 2.423, p < .05) had a statistically positive significant influence on AIC. A positive but non-significant effect of INS on AIL was also observed (β=0.058, CR = 1.006, p > .05). AIL also had a positive but non-significant effect on BI (β=0.050, CR = 1.598, p > .05). Additionally, the results of model fit indices show adequate model fit values: χ2 = 1.679 DF = 1867; p = .000; IFI = 0.954; TLI = 0.951; CFI = 0.953; RMSEA = 0.031. The findings conclude that information overload, anxiety, and avoidance significantly influence AI literacy. On the other hand, information source exposure, AI hallucination, and information seeking do not significantly influence consumers’ AI literacy level. The study suggests that health professionals and staff could be equipped with better knowledge and skills related to AI applications in healthcare settings.