Applied Design Science Research for Coding Data Collecting Web Applications as Proof of Concept with AI

Limitations of off-the-shelf software data collection platforms (e.g., Qualtrics, Survey Monkey) 
can possibly affect the types of survey data collected by higher education (HE) institutional data 
professionals. Overcoming this limitation could be beneficial for increasing the quality of results 
obtained when striving to support HE stakeholders. Consideration of creative and novel 
approaches to collect data in a way that overcomes common platform limitations has historically 
been constrained by steep software learning curves. R is a language often used at HE institutions 
for working with data. Generative AI (GAI) conversational coding paired with the R Shiny 
framework allows for the rapid prototyping of proof-of-concept (POC) web applications by HE 
professionals with limited software development experience, going beyond the limitations of 
existing platforms and possibly better supporting the specific contextual needs of HE 
stakeholders. Demonstrated in this work is a conversational coding example using a design 
science research framework applied to a hypothetical HE recruitment scenario. A novel survey 
data collection web application artifact is produced as an illustrative applied example to 
understand in what ways a conversationally coded app can extend the capabilities of HE data 
professionals beyond features offered by existing data collection platforms. The result is a 
functional POC and perhaps offers a fresh perspective on what is possible by HE data 
professionals with limited programming experience.