AI Tools & Resource Repository Behavioural Research UK
Section 1 · Stage 4

Data Analysis & Interpretation

This section introduces essential AI-powered analytical tools designed to help behavioural researchers extract meaningful insights from diverse datasets across research methodologies.

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What AI can help with

For qualitative analyses

  • AI can extract key themes and trends from large textual datasets.
  • NLP models can analyse sentiment in qualitative responses.

For quantitative analyses

  • AI can generate visualisations for complex data trends.
  • AI can provide quick solutions to coding errors.
  • AI-powered tools like Julius AI and GenAI platforms may help analyse statistical data.

Tools

Purpose-built research tools

Julius AI Free & paid

A comprehensive data analysis platform that processes multiple file formats, provides visualisations, and executes code in Python and R. Offers automatic data correction, PDF information extraction, and structured "Workflows" for common analytical procedures, while employing "Advanced Reasoning" to break complex requests into manageable steps with web search capabilities.

Watch a walkthrough video

Evidano Paid

Previously Ailyze. Supports assisted coding, document analysis and thematic or content analysis, with an AI interviewer option for collecting qualitative data. The free tier offers only limited qualitative analysis features.

Hugging Face Free & paid tiers

Hosts thousands of AI models and datasets, many open-source and useful for research. Access pre-trained models for sentiment analysis, emotion detection, text classification, and named entity recognition to analyse interview transcripts, survey responses, and social media data. Models can be tested via web interface, downloaded for local use, or accessed through the Inference API. Particularly valuable for analysing qualitative data at scale.

QInsights Free & paid

Built for thematic analysis of interviews, focus groups and open-ended survey responses. Every AI-surfaced theme links back to the underlying quotes, so findings stay traceable for supervision, peer review or stakeholder scrutiny. You ask the questions and interpret the results; the AI organises, retrieves and surfaces candidate patterns rather than generating themes on its own.

Delve Paid

AI-assisted qualitative coding built around conversing with your data — query your whole corpus or selected codes and transcripts, and get answers with snippet citations back to the source. Works like a virtual peer debriefer for talking through emerging findings. The AI features can be switched off entirely where a protocol prohibits AI use.

GenAI platforms (ChatGPT, Claude, Gemini, Deepseek)

GenAI platforms can be helpful in qualitative analyses, as well as generating R or Python code for quantitative analyses:

  • Quantitative analyses in R, Stata, or Python: by creating artefacts, these platforms can help generate and debug efficient code for complex statistical procedures, create customised data visualisations, and translate technical outputs into clear interpretations. You can prompt them to recommend appropriate statistical methods based on research questions, generate code for data preprocessing, and write comprehensive documentation (e.g. ReadMe files) for reproducibility. They can also convert analysis scripts between languages while maintaining analytical integrity.
  • More sophisticated AI-powered code editors include GitHub's Copilot, Cursor, Windsurf, Claude Code, and Codex. They are also helpful in building web and smartphone applications for your study design.

AI for quantitative analysis

Research shows that it can be risky for researchers with little expertise in quantitative analyses to use GenAI for such quantitative methods: Prandner and colleagues (2025) found that analysis procedures were often general and not clearly illustrated, making it difficult for a naïve user to make informed decisions. It also shows difficulties with proprietary software (SPSS) but more promising results with open-source software (R). The AI can act as a starting point or provide partial solutions, but without a foundational understanding of statistics and software, the naïve user may struggle to evaluate the AI's output, make informed decisions, and correct errors.

For sentiment analysis, Lossio-Ventura and colleagues (2024) found that LLMs, particularly ChatGPT and fine-tuned OPT, represent a significant advancement, especially for health-related survey data, offering superior performance and efficiency gains. They caution that users should be aware of the limitations — particularly concerning protected health information, potential errors with linguistic nuances and specialised terminology, and the necessity for human oversight. Open-source LLMs like OPT provide a promising alternative.

AI for qualitative analysis

A contested area — read both sides first

The use of AI for qualitative analysis, especially reflexive approaches like thematic analysis, phenomenology and ethnography, is highly contested. In late 2025, 419 experienced qualitative researchers signed an open letter rejecting AI for reflexive research (Jowsey et al., 2025), arguing that AI cannot "make meaning", that reflexive analysis is inherently human, and that AI infrastructure carries environmental and social costs.

That position has been challenged. Responses argue that when the researcher stays in control the core of reflexive work remains intact, and that binary thinking in the current polarising debate is unhelpful (Greenhalgh, 2026; Friese, 2025).

Before using AI in qualitative analysis, review both sides and consider whether the critiques apply to your method, your use case and your level of AI involvement. This isn't settled, and your view may reasonably differ depending on your analytic tradition and how you use the tools.

Check out this more comprehensive repository of AI tools for qualitative data analysis maintained by the CAQDAS Networking Project at the University of Surrey.

Case studies

Wheeler (2025) — how-to guide

Wheeler (2025) provides an excellent introductory how-to guide on using Generative AI to assist in qualitative data analysis.

Smirnov (2025) — LLM content analyses

Smirnov (2025) used simulations with ChatGPT and Claude to perform content analyses and narrative analyses and compared them with real human output. Their findings suggest that LLMs hold great promise with the right prompts in coding textual data and generating insightful themes with increased credibility and reduced time.

Hayes (2025) — "conversing" with data

Hayes (2025) highlights the potential of LLMs as collaborative analytical partners, enabling researchers to "converse" with textual data through targeted questioning, revealing patterns and connections that might otherwise remain hidden. AI tools such as Claude can dramatically accelerate traditional tasks like transcription, coding, and theme identification while maintaining the researcher's control over interpretation and conceptual framing.