Developing an AI-Generated Virtual Respondent Platform to Provide Applied Field Survey Experience and Foster Independent Learning in an Online Learning Environment

Abstract

This presentation discusses an AI-generated virtual respondent platform developed for a survey research assessment in an online business research unit (QM367). The assessment provides an authentic, applied field survey experience despite the constraints of collecting data from real participants in an online learning environment. Students receive scaffolded instructions and a project report template; however, they independently select a research question and design their own questionnaire.

Students conduct structured interviews with 30 AI-generated virtual respondents. Each respondent has an internally consistent profile representing an Armidale resident, while the set reflects diversity in age, gender, education, employment, household type, and geographic location. Students administer their questionnaires, record and clean responses, prepare datasets, apply descriptive statistical techniques, interpret findings, and evaluate limitations.

AI is used purposefully to simulate field survey participation, while students critically evaluate the limitations of AI-generated respondents and simulated data. By engaging in the complete survey research process, students demonstrate their understanding of questionnaire design, field data collection, data management, statistical analysis, interpretation, and professional judgement. This assessment design aims to increase engagement, strengthen practical research skills, improve confidence in conducting surveys, and motivate students to take greater responsibility for their learning and participation in an AI-allowed online learning environment.