In recent years, and especially since the launch of ChatGPT, many tools have been developed that use AI to support scientific 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 that it will help to unlock the potential of research is growing. Many academics are aware of and generally interested in the development of AI, but rarely use AI in their day-to-day work (with the exception of increasingly common technologies such as automated translation or grammar correction).
This may be changing.
Large language models clearly have much to offer academic research. Their ability to process human language effectively, to understand complex questions, to find relevant information in large bodies of text, and to generate coherent text based on specific instructions make them suitable for automating some of the most repetitive and tedious tasks in research, and allowing academics to focus on understanding problems and developing ideas.
These advances are mostly focused on textual formats. If you search for “AI research assistant”, 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, 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 and radio are among the most influential media of the twentieth century.
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 search synchronised with the audio/video content, automatic translation, synchronisation of your annotations with the recording, automatic identification of people, places and organisations mentioned, visualisations and much more!
There are several ways to use AI to support your work with audio and video.
MAXQDA and Atlas.ti have recently introduced new AI features powered by OpenAI.
aureka automates qualitative analysis, enabling both inductive and deductive coding, while keeping you in full control.
Our system is designed with a human-in-the-loop approach. This means that we combine AI automation with human intervention at the right moment to refine the coding system.
aureka’s automated AI coding includes the following steps
In this way, aureka’s approach to automated coding saves a lot of time without compromising accuracy.