Artificial intelligence is transforming how we ask questions, collect data, and share knowledge. For scholars curious about experimenting with AI, the challenge is not whether the tools are useful, but how to integrate them responsibly into academic practice. Used wisely, AI can amplify creativity, expand access to information, and free up more time for deep, original thought. Used carelessly, it risks undermining rigor, accuracy, and trust.
Here are some best practices for scholars who want to use AI in research with integrity:
1. Use AI to Amplify, Not Substitute
The goal of scholarship is interpretation, judgment, and meaning-making. AI can brainstorm alternative framings, suggest comparative angles, or identify patterns across large bodies of text—but it should not replace your own reasoning. AI should serve as your brainstorming partner, not your pen.
Example: Use ChatGPT or Claude to test how your argument holds up under different perspectives, but rely on your own expertise to draw conclusions.
2. Treat Outputs as Provisional
AI systems can be remarkably fluent, but they are also prone to error and made-up answers called “hallucinations.” Never assume that a generated source, citation, or fact is accurate until you’ve verified it yourself. Treat AI’s outputs as leads to investigate, not conclusions to accept.
Example: If AI surfaces a list of secondary sources on Adam Smith, double-check each one in your library databases before including it in your literature review.
3. Stay Transparent
Transparency builds trust. If AI tools contributed to your research process, acknowledge how you used them. This is not only ethical but also expected more and more by academic journals and institutions.
Example: In your methodology section, you might note that you used Otter.ai for interview transcription or Elicit to assist in literature discovery.
4. Leverage AI for the “Unseen Work”
Some of the most valuable uses of AI are in the background—helping with transcription, summarization, data cleaning, and formatting—so that more energy can be devoted to interpretation and argument.
Example: Use Descript to transcribe oral history interviews or Scholarcy to create quick summaries of long research papers before diving into close reading.
5. Guard Against Overreliance
It’s tempting to let AI take over too much of the research process. Overreliance can flatten your scholarly voice and narrow the originality of your work. Be intentional about when you use AI and when you set it aside.
Pro Tip: Schedule “AI-off” time in your workflow to ensure your arguments remain rooted in your own judgment and disciplinary standards.
6. Prioritize Security and Ethics
Data protection isn’t consistent across all AI tools. Be careful about uploading sensitive or unpublished materials to public platforms. Where possible, use institutional or research-compliant AI tools, and always respect intellectual property and participant privacy.
Example: Avoid pasting raw interview transcripts into public models; instead, anonymize or summarize before analysis.
7. Cultivate a Community of Practice
AI is evolving quickly, and no scholar should navigate it alone. Sharing experiences, best practices, and cautionary tales helps the academic community adapt responsibly. Seek out colleagues experimenting with similar tools, or join structured groups.
In Conclusion
AI is not a replacement for scholarship. It is a new library, a new set of instruments, and a new conversation partner. By using it thoughtfully—to amplify ideas, support tedious tasks, and expand the reach of research—scholars can protect the rigor of their disciplines while also exploring new intellectual frontiers.
Interested in applying AI tools to your research? Apply today for our Cosmos x IHS AI Accelerated Scholarship program.
