AI for ethical and better research – recap
In today’s live session, we explored how to use AI tools to enhance your research without outsourcing your brain. The focus was on doing this ethically, transparently, and in a way that actually strengthens your critical thinking rather than weakening it. Why use AI in research? - To speed up literature discovery, initial synthesis, and data extraction so you can spend more time on real thinking and analysis. - To navigate growing publication volumes and quickly map out trends, contradictions, and limitations in a field. Key principles for ethical use - Always double- or triple-check AI-generated statements, numbers, and citations; you are responsible for the accuracy of your work. - Use AI to support your reasoning, not to replace it: reading and comparing papers manually is still essential for developing research-gap and critical assessment skills. What “Answer This” can help you with - Literature reviews: generating structured drafts with line-by-line citations, plus exportable reference lists for your reference manager. - Research gaps and field mapping: visualizing topics, trends, and underexplored areas, and running bibliometric-style overviews. - Data work: extracting numerical and tabular data from papers, building paper/data tables, and saving outputs into notebooks you can refine and edit. How to integrate it into your workflow - Start with a focused prompt (e.g. “comprehensive review on integrated CCU, focusing on materials, process integration, and business models since 2020”) and refine via filters (sections, number of points, Q1/Q2 journals, preprints only, date ranges, etc.). - Use the output as a starting point: export references, inspect DOIs, open and read the underlying papers, and then rewrite and restructure in your own words and style. If you missed the session, the recording is now available here - you can pause, follow along, and test the workflows on your own topic.