For the first time in human history, we can delve into intricate dialogues with AI on any scientific topic, harvesting insights and inspiration from our mechanical counterparts. Yet, alongside the excitement, there's a hint of intimidation. The way we conduct research is transforming, and we must evolve alongside it, embracing this unprecedented human-machine partnership.
"If you can't beat 'em, join 'em" rings true, but the question remains, how can we wield the power of AI models like ChatGPT to fuel our research, boost productivity, and avoid pitfalls? This book strives to answer that critical question.
Using nearly a hundred tangible, real-world research examples, this guide presents a detailed pathway on how researchers, irrespective of their area of study, can employ ChatGPT as a competent research assistant. This guide encapsulates ten easy-to-follow principles to accomplish a vast array of research tasks:
- Identifying research topics and framing questions through an in-depth discussion with ChatGPT
- Formulating and refining hypotheses based on the chosen research question
- Undertaking literature reviews, covering all steps of a systematic review protocol
- Selecting adequate research design and corresponding methodology
- Developing valid, reliable, and efficient research tools
- Handling every aspect of data collection, management, and ethics
- Interpreting and analyzing both quantitative and qualitative data
- Writing and refining research papers and reports
- Addressing peer review comments
- Disseminating study findings through mass and social media platforms
Each of these tasks can be seamlessly accomplished by merely typing prompts into the ChatGPT interface. Ruopeng An will guide you through this transformative process.
Available at the Amazon Kindle store.
About the author
Ruopeng An is associate professor at Washington University in St. Louis. He obtained his doctoral degree in policy analysis from Pardee RAND Graduate School.
Prior to joining Washington University, An was assistant professor at the University of Illinois at Urbana-Champaign. His research primarily targets obesity, aiming to develop policy recommendations based on assessments of various interventions' impacts on weight outcomes.
With over 200 peer-reviewed journal publications, An is recognized as one of Elsevier's top 2% most cited scientists. His work has been highlighted in TIME, New York Times, Los Angeles Times, Washington Post, Reuters, USA Today, Bloomberg, Forbes, Atlantic, Guardian, FOX, NPR, and CNN. He serves on research grants and expert panels for NIH, CDC, NSF, HHS, and the French National Research Agency.
In 2018, An was elected as a Fellow of the American College of Epidemiology. He founded the Artificial Intelligence and Big Data Analytics for Public Health Certificate program and teaches applied machine and deep learning courses at Washington University.
