What are the uses of AI for research?

AI is everywhere in our digital environments, and professional research is no exception.

Some applications of AI for research include:

• scientific discoveries

• diagnosis and disease detection

• developing research questions

• analyzing vast datasets

By identifying patterns in massive datasets with high accuracy, research is cheaper, more energy efficient, and widely used.

What does the use of AI mean for research? It means that labor-intensive, time-consuming, and repetitive tasks can be done by AI, so that researchers can focus on the big questions.

AI uptake is high and growing in the research world. Generally speaking, three broad uses of AI for research are:

• Pattern detection

• Literature review

• Data analysis

Let's dive into how this can look in quantitative and qualitative research, along with the risks and benefits of AI tools.

When it comes to quantitative research, AI is often used for organizing large volumes of data and using this data to predict what comes next.

At Caltech, AI processing of climate data is "45,000 times faster than current weather models." This "Big Data" would be impossible for human teams to do alone!

Benefits:

• Increased speed of computer processing

• Accurate predictions

• Computer capacity to solve previously "unsolvable" complex equations

Risks:

• AI dependence can minimize researchers' creativity

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AI is used in qualitative research to group common themes emerging from text and then describe or make decisions based on them.

For instance, in narrative-based medicine (NBM), AI can be used to review patient stories and clinical information for improved decision-making. However, large gaps remain in AI when it comes to recognizing subtle medical symptoms that need human judgment for safety.

Benefits:

• Efficient data organization and analysis of large sets of data

• Identifying themes and patterns in narratives and lived experiences

Risks:

• The risk of "AI lies" with incorrect or fake conclusions

• May overlook non-verbal cues, context, emotion, and cultural nuances

If you’re not a climate data scientist or clinical specialist, AI can still be useful for your research.

AI tools built for research are like assistants with added features. Certain tools can be used for formatting, brainstorming, clarifying ideas, and formulating research questions.

They act like a discovery engine library for your research using retrieval-augmented generation (RAG). Unlike common AI tools, RAG pulls answers from an established research database. Here's how RAG works:

Additional features depend on the software you use, like how current the database research is, the measurement of its popularity, and how your topic relates to other research.

Benefits:

• Great for formatting and finding reference material

• Great for university and school-level research

• More grounded than standard AI

Risks:

• Plagiarism (copying work without crediting the source)

• Still requires fact-checking and source verification

• Can still generate incorrect or fabricated information

For the best use of AI for research, humans need to do the thinking, while AI carries out the automated tasks.

Be aware of the best way to use AI for your research with these steps:

If you want to learn more about AI in research, check out Bytes like What are the pros and cons of using AI for research?