Leveraging ChatGPT and Other AI Models in 3 Steps

An overview of how to use ChatGPT and other AI models in your projects in 3 steps

The new generation of generative AI models, such as ChatGPT by OpenAI, LLaMA, Bard, and Sparrow, have shown great potential to disrupt higher education and other fields. This is evident from the abundance of resources, articles, guidelines, commentary, and webinars about them.

ChatGPT, for example, has become the fastest-growing consumer application in history, with 100 million active users in just two months of availability. It was not designed to challenge academic freedom or change the standards of authentic assessment, but that is the reality. Despite the challenges, the tertiary education sector also offers opportunities to use generative AI models. The authors propose three key stages for generative artificial intelligence-assisted adaptive research practice, with a focus on academic research.

1. Make the Research Issue More Specific:

ChatGPT Chatbot was created with the capability to efficiently brainstorm concepts, revelations, and connections that are not immediately apparent to a human domain expert. In stage one, a dialogue between ChatGPT and the researcher can thus be added to the manual process of detecting a research gap by reading the literature, using search engines, and requesting human expert consultations.

2. Improve Research Efficiency:

It is possible to shorten the time it takes to get from research ideation to implementation and validation by using ChatGPT. For instance, ChatGPT can offer a summary of every research methodology that is pertinent to the study's questions and problem area. It can offer an overall ranking of techniques based on their prospective efficacy.

3. Verify Research:

The human researcher interprets data into conclusions and insights that might address the study hypothesis or topic or lead to new ones. By offering further integrations of validation in various ways, ChatGPT can be used to support these conclusions.