Following the launch of the generative AI Web application, Ask.SMILE, designed to evaluate the cognitive levels of questions asked, 2559 educators generated 25,973 question-feedback sets over a three-month period, with an average of over 10 questions per participant. Analyses revealed a significant improvement in question quality from initial submissions to later stages. Specifically, participants progressed from Level 1 questions (simple recall) to Level 5 questions (creative and evaluative), based on Bloom's Taxonomy. However, participants who began with lower-quality questions showed varied progression paths to higher levels. To analyze the data, a Linear Mixed Model (LMM) was used to account for both fixed and random effects, revealing a statistically significant improvement in question quality over time (p < 0.01). ANOVA results further confirmed the significance of question stage on quality, and a Least Squares Means (LSMEANS) analysis indicated a precise and significant improvement in later-stage questions. Additionally, survey responses highlighted participants' appreciation for the ongoing development of AI-driven educational tools like Ask.SMILE. Several implications and future research directions are explored, including the potential to expand the platform's features beyond question evaluation.