This article is part of weekly updates on new developments in the use of AI methods and tools of surveys (households, individuals, farms…) and administrative data for official statistics

Coverage Period: 02–08 February 2026

Key words: AI, survey research, machine learning, data quality, household surveys, data analysis

Introduction

This week’s update on the use of Artificial Intelligence (AI) in survey research highlights significant advancements in data processing, analysis, and reporting. The key trend is a move towards a human-led, AI-enhanced research model, where AI accelerates workflows without replacing human interpretation [7]. National statistical offices, such as the UK’s Office for National Statistics (ONS) and the US Department of Labor (DOL), are actively integrating AI to improve the quality and efficiency of their data operations [3, 4]. This report details new developments in AI-powered tools and methods for data editing, cleaning, processing, analysis, and dissemination, offering valuable insights for researchers and statistical offices.

AI in Data Collection, Editing, and Processing

Recent developments show a strong focus on using AI to improve data quality and efficiency from the initial stages of data collection and processing. Key applications include data imputation, automated coding, and data quality control.

2.1. Survey-to-Survey Imputation

A study published in The World Bank Economic Review demonstrates the effectiveness of survey-to-survey imputation for filling data gaps in household consumption surveys, particularly in low- and middle-income countries [2]. The research found that basic imputation models using a modest set of predictors (demographics, employment) can produce accurate poverty estimates. The accuracy of these models is robust to variations in questionnaire length, poverty lines, and other factors. This method is now being applied to impute poverty data for hard-to-reach populations using administrative records and phone call data [2].

2.2. Automated Data Editing and Cleaning

The US Department of Labor (DOL) has deployed several AI systems to automate data editing and cleaning processes in its major surveys [3]. These systems use machine learning to impute missing data, refine survey samples, and automatically classify data, significantly improving statistical accuracy and efficiency. The table below summarizes some of the key AI applications at the DOL.

2.3. Low-Code AI Data Quality Tools

The emergence of low-code AI data quality tools is democratizing data quality management [5]. These tools provide visual interfaces and pre-built AI models that allow business analysts and researchers to automate data cleansing and validation without extensive coding knowledge. Key features include intelligent deduplication, automated format standardization, and AI-powered anomaly detection [5].

AI in Data Analysis and Reporting

3.1. AI-Powered Survey Analysis

Generative AI tools are being integrated into spreadsheet software, such as Google Sheets and Microsoft Excel, enabling researchers to classify survey responses, analyze sentiment, and identify trends with simple formulas [6]. A recent article highlights a framework for AI-powered survey analysis that emphasizes a hybrid approach, combining AI for speed and scalability with human expertise for interpretation and context [6].

3.2. Machine Learning in Demographic and Health Surveys

A study in Digital Health demonstrates the application of multiple machine learning algorithms to analyze data from the Somalia Demographic and Health Survey (DHS) [8]. The research found that a Gradient Boosting model outperformed other models in predicting inadequate meal frequency among children, with birth order being the most significant predictor. This study showcases the power of machine learning to identify key factors in large-scale household surveys and inform targeted interventions [8].

3.3. AI-Powered Reporting and Visualization

Several new platforms are using AI to automate the generation of reports and visualizations from survey data. Displayr, for example, is an AI-powered survey analysis and reporting platform that uses AI for text analytics, data cleaning, and narrative drafting [7]. Other tools, such as Quillit and Condens, focus on turning qualitative data from interviews and focus groups into structured, citation-ready insights [7].

AI Tools and Platforms for Researchers

A variety of AI-powered tools are now available to support researchers throughout the research lifecycle. A recent review of AI research tools identified the following key categories and top-rated tools [1]:

Conclusion

The developments from this week underscore the rapid integration of AI into survey research. From improving data quality at the source to accelerating analysis and reporting, AI is offering powerful new capabilities to researchers and statistical offices. The most successful applications will be those that combine the power of AI with the critical thinking and subject matter expertise of human researchers. As the UK’s Office for National Statistics notes, the goal is to “harness that potential responsibly” to improve the quality and resilience of our core statistics [4].

References

[1] I tested the 6 best AI tools for research in 2026, and here’s my honest take The Jotform Blog
[2] Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment The World Bank Economic Review
[3] Artificial Intelligence Use Case Inventory U.S. Department of Labor
[4] The road ahead for the ONS: a conversation with Darren Tierney National Statistical
[5] Low-Code AI Data Quality Tools Improve Data Accuracy Easily

[6] How to Analyze Survey Data with Generative AI

[7] Top 10 AI-Powered Market Research Companies in 2026

[8] Machine learning-based algorithms to identify factors associated with inadequate meal frequency among children aged 6–23 months in Somalia: Evidence from the Somalia Demographic and Health Survey 2020 - PMC

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