Pre-testing Bioethics Surveys with Generative AI: Exploring Synthetic Agents Perspectives on Assisted Reproductive and Genomic Health Prediction Technologies

Overview

Assisted reproductive technologies and AI-powered genomic prediction are developing faster than many legal, ethical and policy frameworks can adapt. Policymakers and health researchers must make decisions about issues such as donor anonymity, post-humous use of gametes, non-medical sex selection, reproductive provider licensing, genomic data privacy, algorithmic oversight and the use of genetic risk information by insurers. These issues are ethically sensitive, technically complex and often difficult to investigate through traditional survey research.

This interdisciplinary project analyses an existing dataset of 1,000 Australian-profiled generative agents who responded to questions based on assisted reproductive technology policy and governance principles for AI-driven genomic health prediction. The project combines bioethics, law, health policy, survey methodology, behavioural science, statistics and artificial intelligence. Students will investigate patterns of agreement and disagreement across key policy items involving privacy, regulation, reproductive autonomy, insurance access or perceived fairness; examine how synthetic agent responses vary by demographic and ideological profile; and analyse free-text justifications for moral and ethical reasoning or other recurring themes.

The research aims to contribute to the growing literature on synthetic populations and generative agents as tools for survey pre-testing. It will not claim that model responses are direct measures of Australian public opinion. Instead, it asks whether AI agents can help researchers identify ethically salient disagreements and likely analytical limitations before human research begins.

Research engagement

The student will:

• Conduct a review existing literature to identify gaps and contextualise the current study;
• Work on data using quantitative data analysis techniques; and
• Document the findings of the study, interpreting the data and drawing conclusions based on the analysis.

Research activities

The student will work with Dr Stephen Whyte and members of the behavioural economics/AI research team at the ARC BITA Centre. They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: Summary of key insights and
  • recommendations for future research.
  • Reference List/Bibliography
  • Appendices (optional)
• Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
• (Optional) The student will be eligible to present the findings of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested to. However, it should be noted that the primary deliverable is a final draft manuscript. No work beyond the VRES period is required. Any later contribution to a manuscript would be optional and by mutual agreement.

Research skills

They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

Outcomes

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: Summary of key insights and
  • recommendations for future research.
  • Reference List/Bibliography
  • Appendices (optional)
• Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
• (Optional) The student will be eligible to present the findings of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested to. However, it should be noted that the primary deliverable is a final draft manuscript. No work beyond the VRES period is required. Any later contribution to a manuscript would be optional and by mutual agreement.

Skills and experience

• An interest in health, law, policy, AI, statistics, psychology or economics.
• Some proficiency in data entry, data analysis, and statistical techniques; and
• Experience using Stata, R or Python would be beneficial.

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Gardens Point campus (Z Block, Level 7), with some work able to be completed online/remotely subject to mutual agreement between the student and their VRES supervisor.

Additional information

Existing synthetic agent data, code, variable documentation and regular supervision will be provided. Students will undertake a short, self-guided preparatory reading on ART policy, genomic data governance and responsible AI.

Keywords

• Generative AI
• Synthetic Data
• Bioethics
• Health Policy
• Assisted Reproductive Technology
• Genomic Prediction

Contact

Dr Stephen Whyte

+61 7 3138 1050

sg.whyte@qut.edu.au