Best AI Research Assistant to Analyze Interviews 2025

How can AI enhance academic research?

In recent years, and especially since the launch of ChatGPT, many tools have been developed that use AI to support academic work and are becoming more widely known in the academic community.

Although the academic community is divided on the importance of AI for research, the number of those who believe it will help unlock the potential of research is growing. Increasingly, academics are starting to use AI in their day-to-day work, gradually moving beyond the initially established technologies such as automated translation or grammar correction.

But how can AI enhance academic research?

Large language models clearly have much to offer in this field. Their ability to process human language effectively, understand complex questions, find relevant information in large bodies of text, and generate coherent text based on specific instructions makes them suitable for automating some of the most repetitive and tedious tasks in research, allowing academics to focus on understanding problems and developing ideas.

Why we built aureka as a research assistant for audio and video

These advances are mostly focused on textual formats. If you search for “AI research assistant” online, you will find that most of the tools reviewed refer to tools that help with tasks such as searching for textual references, obtaining literature summaries, "chatting" with a document or brainstorming through conversation with an AI.

However, audio and video represent a very rich source of information for research. We produce and interact with more audiovisual content every day. Most qualitative research projects require interviews to gain in-depth insights, and TV, radio and recently also video streaming and podcasting are among the most influential media.

Either if it is for academic research drawing on interview data, consultancy, market research or focus groups, working with interview data can be very time consuming, especially if we don’t count with adequate tools.

We developed aureka to fill this gap.

Improving the quality of automatic transcription opened the door to a new way of working with audio (visual) material. But transcription is only the first step.

aureka is the AI research assistant for audio and video. It transcribes automatically and uses the transcript as the basis for various forms of automation useful for research work: full-text keyword search synchronised with the audio/video content, AI powered search that allows to ask research questions and get results from your interview data even without keyword match, automatic translation, synchronisation of your annotations with the recording, and much more. 

AI assistants for audio and video

There are several ways to use AI to support your work with audio and video.

• AI-powered transcription software: the many AI-powered transcription software out there (such as Sonix, Otter, Amberscript or Trint, to name a few) will help automate the first – and probably most time-consuming – step of analysing interviews. You no longer need to manually transcribe the audio. This is great! However, most transcription software doesn’t seem to have the researcher’s use case in mind, and doesn’t offer useful features such as AI powered searching based on semantic similarity, or user friendly ways of keeping an overview of one's own annotations for further use in publications. 
• Qualitative research software can also be used to analyse interviews or other audio/video recordings, provided that we upload the transcript (Atlas.ti), or use an integrated automatic transcription service for an extra cost, which is often more expensive that most translation tools (NVivo or MAXQDA). All in all, there is a high cost in terms of time and/or money.

• AI chatbots like ChatGPT enable you to upload interview transcripts and inquire about them, but they do not ensure data privacy, and the results are often non-reproducible, failing to meet a crucial scientific standard.

Some of these tools have introduced AI features powered by OpenAI.

• Some transcription tools, such as Sonix for example, offer AI analysis tools for extra cost. They provide summarization and extraction of topics and sentiments, which is very usefull to get a quick overview of the transcript's content. However, it might not be suited to work with predefined (e.g theory-oriented) categories with a deductive approach. 
• In the case of MAXQDA, automatic summaries of texts that share a code are generated, which is very useful for keeping track of coded content and having notes ready for the writing process. However, the task of coding is still done manually or with a simple automation (without AI) that codes text fragments that contain a keyword.
• Atlas.ti uses OpenAI to provide automated coding. AI processes the selected document and applies inductive coding to the data. The user then has to review each code and can edit or delete it. Although this can be very useful if inductive coding is what we want, it may not be suitable if we have a coding system in place beforehand. Its "intentional AI Coding" can help if there is a coding system given beforehand, as it formulates questions based on the coding intent, and these then guide the coding process. The assigned codes should then be reviewed at the end, which might very time-consuming in the case of a large corpus. 

aureka’s approach to AI-based interview analysis

aureka automates qualitative interview analysis in several ways. 

• Our AI research assistant, which will launch in early 2025, allows to transcribe, manage, and ask research questions to your interview data. You can read more about the features and sign up for a free trial here. 
• Our AI metadata generation system enables the processing of interview transcripts and the application of qualitative coding. If you possess a custom vocabulary, such as one derived from your theoretical framework, our system can assign these terms to approximately one-minute segments of the recording transcript and compile them to highlight the most significant elements. You can read more about the system here.