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Resource Methods Toolkit wp6-tuni Published: 12 May 2022

Digital AI driven analysis

Artificial Intelligence (AI) driven analysis refers to a subset of machine learning techniques to discover patterns and relationships in the data. AI, in analytics, refers to the processes that uses machine learning techniques to build understanding and knowledge from a large amount of data. It also determines new patterns and relationships within the data set. AI can be done without the skills of an analyst, as the algorithm provides all the functionality needed. For example, text mining and  analysis are able to identify patterns and trends within unstructured data through the use of machine learning, statistics, and linguistics. The data can also be restructured, or simply structured, by text mining in order to make content logical and more comprehendible. AI contextually restructures the content to provide insight from a data set. AI can therefore be used during the whole interview; utilising chatbots to collect the data, algorithms to sort, mine and create an analysis.

Pros

  • AI is cost-effective as it optimises data sorting accuracy, while removing data management and handling tasks. 

  • There are a number of online services which offer these services, which can be tailored to your specific needs.

Cons

  • It may be tricky for the user to determine the best solution for the project at hand.

  1. https://aws.amazon.com/comprehend/ 

  2. https://atlasti.com/ 

  3. https://aylien.com/

  4. https://www.dedoose.com/

  5. https://cloud.google.com/natural-language/ 

  6. https://www.ibm.com/watson 

  7. https://www.lexalytics.com/ 

  8. https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home

  9. https://getthematic.com/ 

  10. https://monkeylearn.com/





  1. Balling, L. W., Townend, O., Mølgaard, L. L., Jespersen, C. B., & Switalski, W. (2021). AI-driven insights from AI-driven data. Hear Rev, 28(01), 27-29.

  2. D’Hotman, D., & Loh, E. (2020). AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health & Care Informatics, 27(3).

  3. Edwards-Jones, A. (2014). Qualitative data analysis with NVIVO.

  4. Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020). Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnology and Bioprocess Engineering, 25(6), 895-930.

  5. Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824-2838.

  6. Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019). Demystification of AI-driven medical image interpretation: past, present and future. European radiology, 29(3), 1616-1624.

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Authors

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Team co-Leader, CO:RE at TUNI

Jussi Okkonen

Jussi Okkonen, PhD, is an Associate Professor at the Faculty of Information Technology and Communication of Tampere University (FI). Okkonen’s research interests lie in socio-technical environments and digital literacy. He has recently done research on educational technology, children and youth in socio-technical context, and impact of AI.

Tampere University
Tampere University
CO:RE at TUNI
Methods

The team at the Faculty of Information Technology and Communication of TUNI identifies, develops and provides access to resources on qualitative, quantitative and mixed research methods together with evaluating their validity in research practice. These resources are collated in the CO:RE methods toolkit that cross-references resources from the evidence base, the compass for research ethics, and the theory toolkit, to give users tools to apply to their individual research contexts.

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