Reflexivity on the role of AI in research: am I staying in form AND raising the bar?
Reflexivity on the role of AI in research: am I staying in form AND raising the bar?
Inspired by discussions in conferences and online (including in this forum), I decided to put together the linked article exploring the use of AI in qualitative research.
I am a great believer in the ability of professionals to tap to their judgement and tools to produce great outcomes, and felt that the AI debate in qual too often get’s reduced as “AI good / AI bad” overall, or then into trying to be normative on which specific steps it can be applied and where “there be dragons”. That’s why in this position piece I anchor AI to the question of reflexivity - how as a researcher am I using this tool.
Reflecting on usage allows self-discovery and improvement. Following dogma curtails learning. This tension is common in all evolving fields.
There are two sides to the question. First one I call “staying in form” and that is abot safeguarding against the familiar risks of sloppy AI tools. The second one is “raising the bar”, and asks if I am actually improving quality and depth now that new tools are available. For example, am I elevating the role of junior researchers and research assistants (“just hire a research assistant to code your material” is a common quip in this forum…) in the thinking process.
Again in full transparency I am the co-founder at Skimle, and the article is also hosted on the Skimle blog. We’re building professional tools for professional researchers, and a part of that is engaging in how knowledge work is changing.