AI as it Pertains to the Intellectual Growth of the Student Researcher
There is a legacy of science and technology working in tandem in driving innovation and discovery. Scholars, researchers, engineers, and artists have been critical to this growth as they harness new technology. There are many implications of AI and generative models in this construct; we only point out several of them with respect to the research process here as the impacts and implications will evolve with the technology. As it pertains to the intellectual growth of the student researcher, whether undergraduate or graduate, two aspects that are highlighted are the insecurity or vulnerability that are inherent in the apprenticeship process of becoming an independent researcher and the leadership growth inherent in becoming an independent researcher.
While the research products of an undergraduate research project or thesis, a master’s thesis, or a PhD dissertation are readily tangible, there is also, importantly, the intangible growth and scientific maturity that student researchers undergo as a part of earning their Masters or PhD. An example of this intellectual growth is the change from the initial insecurity of the student in their own knowledge that develops into confidence as the student researcher works through the process of the scientific method, deepens their foundational knowledge, and follows the data toward scientific understanding and becoming an independent contributor to the field. It is through the reliance on the fundamentals, the mathematics, the physics, and the observational constraints that the natural world is better understood, and the student matures to easily deciphering the boundaries of their knowledge and the knowledge that is yet to be discovered. As a part of the research process, the student researcher often becomes more self-aware, and in particular of their research strengths and weaknesses, often leveraging their strengths toward their unique contribution to the scientific domain.
In research pedagogy going forward, it will be important to ensure student researchers still have time to learn their own strengths and weaknesses. This is part of their intellectual growth wherein a researcher learns to leverage their strengths to realize their unique advancement of the field. It will be important that the student researcher does not rely too heavily on the use of AI and generative models in the early stages of their intellectual growth before their independent contribution to the discipline is recognized. . This may sound trivial, but it is a fundamental part of the research process. The scientific maturity gained as a part of this process also leads to leadership growth within a given scientific subdomain, thus springboarding the student to an advisory role for future projects in their career.
That said, the challenges inherent in personal and professional growth can be stymieing and overwhelming during the research process. There are many aspects of the process where AI and generative models can complement both student strengths and weaknesses as well as facilitate student growth and development. The idea generation component can aid students who overfocus on implementation, and the implementation aspects can help in streamlining and automating tasks for students who may have challenges moving from the idea/innovation stage to implementation.
