Revolutionizing Survey Data Collection with AI PoweredAutomation in Sample Selection and Response Quality

Authors

  • Ayobami AKINTOLA

    Department of Mathematics and Statistics, Redeemer’s University, Ede, Osun State, Nigeria

  • Ayodele AKANJI

    Department of Mathematical Sciences, Bingham university, Karu, Nasarawa State

Received date: April 4, 2025

Accepted date: May 1, 2025

Published date: May 18, 2025

DOI:

https://doi.org/10.14419/8m5jht86

Keywords:

Artificial Intelligence, Sample Selection, Response Quality, Machine Learning, Survey Automation, Data Analysis

Abstract

As the demand for high-quality, scalable, and cost-efficient data collection grows across research domains, traditional survey methodologies continue to face significant challenges, including declining response rates, sampling biases, and deteriorating response quality. This study investigates the potential of Artificial Intelligence (AI) powered automation to revolutionize survey data collection, specifically through predictive sample selection and real time response quality enhancement. We designed and deployed a modular AI-enhanced survey system integrating three core components: a predictive sampling engine based on supervised machine learning, a reinforcement learning powered adaptive questioning module, and a natural language processing (NLP) based response quality validator. A randomized controlled experiment involving 1,000 participants compared the AI-enhanced system to a traditional survey model across four key performance domains: sampling accuracy, response quality, participant engagement, and user satisfaction. Results demonstrated statistically significant improvements in the AI condition across all domains. The AI group exhibited closer alignment with national demographic benchmarks, higher internal consistency (Cronbach’s α = 0.89), increased semantic coherence and lexical richness, and a 94.2% completion rate compared to 78.6% in the control group. User satisfaction ratings and sentiment analysis also favored the AI enhanced experience, with 73% of feedback classified as positive. These outcomes validate the capacity of AI systems to improve both the technical performance and user experience of survey research. This study highlights the transformative potential of AI in digital data collection and provides a scalable, participant centered framework for future applications in market research, public opinion studies, and academic inquiry. Ethical considerations related to algorithmic transparency and data fairness are also discussed, emphasizing the need for responsible implementation. The findings offer a critical step toward the development of intelligent, adaptive, and high-integrity survey systems for the data driven future.

References

  1. Groves, R. M., et al. (2009). Survey Methodology. Wiley.
  2. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method. Wiley.
  3. Keeter, S. (2017). The growing challenge of survey nonresponse. Pew Research Center.
  4. Brick, J. M., & Williams, D. (2011). Explaining rising nonresponse rates. Public Opinion Quarterly, 75(4), 679–712.
  5. Kalton, G. (1983). Introduction to Survey Sampling. Sage.
  6. Tourangeau, R. (2013). The hard-to-survey population. Annual Review of Statistics and Its Application, 1(1), 1–21.
  7. Revilla, M., et al. (2021). Improving data quality in online panels. Social Science Computer Review, 39(4), 650–667.
  8. Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50, 537–567.
  9. Couper, M. P. (2017). New developments in survey data collection. Annual Review of Sociology, 43, 121–145.
  10. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
  11. Shrestha, Y. R., et al. (2021). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 63(3), 84–109.
  12. Rao, D., Jain, A., & Patel, M. (2020). Predictive analytics in respondent selection. Journal of Survey Statistics and Methodology, 8(3), 223–239.
  13. Boase, J., & Milakovich, M. E. (2020). Adaptive AI systems in online public opinion research. Journal of Information Technology & Politics, 17(1), 1–15.
  14. Kumar, R., & Sharma, P. (2022). NLP-enhanced questionnaire design. AI and Society, 37(1), 89–104.
  15. Albagli, E., et al. (2022). AI-powered validation systems for online surveys. Data Science Journal, 21(1).
  16. Zhang, C., & Dafoe, A. (2020). Artificial intelligence in social science research. Big Data & Society, 7(2).
  17. Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd. W. W. Norton & Company.
  18. Tang, Z., et al. (2019). Artificial intelligence for survey design and analysis. IEEE Transactions on Emerging Topics in Computing, 8(4), 792–801.
  19. Mittelstadt, B. D., et al. (2016). The ethics of algorithms. Big Data & Society, 3(2).
  20. Chen, Y., & Sandhu, G. (2021). Machine learning for intelligent respondent targeting in online surveys. Journal of Survey Methodology, 18(2), 134–150.
  21. Patel, R., Singh, A., & Oberoi, K. (2022). Algorithmic quota sampling for improved demographic balance. International Journal of Digital Research, 5(1), 45–59.
  22. Wang, Q., & Huang, L. (2020). Reinforcement learning in adaptive survey design: A real-time approach. Social Data Science Review, 12(3), 210–225.
  23. Gomez, L., & Rowley, J. (2019). Personalizing surveys for enhanced respondent engagement. Online Research Methods Quarterly, 11(1), 43–56.
  24. Morales, S., Gupta, R., & Zhou, Y. (2021). Enhancing open-text response validity using BERT-based NLP models. AI and Society, 36(4), 899–913.
  25. Keane, J., & Lin, M. (2022). Real-time response prompting in online surveys. Journal of Artificial Intelligence in the Social Sciences, 9(2), 112–127.
  26. Jarvis, H., & Albar, D. (2020). Ethics of adaptive surveys: Transparency and autonomy in algorithmic decision-making. Digital Ethics Quarterly, 4(2), 59–70.
  27. Doshi, P., & Kumar, N. (2023). Auditing adaptive AI in survey systems: A framework for accountability. Journal of Computational Social Science, 7(1), 101–120.

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Received date: April 4, 2025

Accepted date: May 1, 2025

Published date: May 18, 2025