AI & Verification in Research
I recognize that the modern world increasingly relies on modern solutions, including the use of AI tools. At the same time, I acknowledge their limitations and imperfections. Therefore, in my research process, I use generative AI as a supporting tool rather than as a source of original thought or analysis. To ensure accuracy and maintain integrity in my work, I independently verify information generated by AI by consulting original and reliable sources before incorporating it into my research. In this way, AI primarily helps streamline the search and information-gathering process, allowing me to dedicate more time to higher-level tasks such as critical evaluation, synthesis of existing literature, and developing my own insights and conclusions.
A concrete example
When researching memory systems and context in second language learning, I used generative AI to help surface relevant literature and suggest keywords I might not have considered initially. However, I then independently verified each source to ensured that I wasn’t missing critical papers, while maintaining the rigor my research demands.
After receiving this output, I began by checking the suggested sources and confirming that they were peer-reviewed. I then reviewed the abstracts and selected articles for further reading based on their relevance to my research question. This screening process left me with only one directly relevant study. To broaden the search, I used the keywords suggested by the AI in academic literature databases, which helped me identify a couple of additional articles that met my criteria.
I plan to use the same approach into user research and product work. It is important to treat tools critically, grounding decisions in evidence to produce the best quality findings. Rigor and innovation aren’t opposed; they should rather work hand in hand.