JMIR Publications

ABSTRACT

Background:

Large language models (LLMs) can aid students in mastering a new topic fast but for the educational institutions responsible for assessing and grading the academic level of students it can be difficult to discern whether a text originates from a student’s own cognition or if it is synthesized by an LLM. Universities have traditionally relied on a submitted written thesis as proof of higher-level learning, on which to grant grades and diplomas. Ubiquitous availability of LLMs challenges this practice.

Objective:

In this study we assumed the role of hypothetical health science master’s student actors looking to leverage the full power of an LLM in completing scientific research paper manuscripts that could be submitted for master’s thesis graduation.

Methods:

In an exploratory case-study we used ChatGPT to generate two research papers as conceivable student submissions for master’s thesis graduation from a health science master’s program. One paper simulated a qualitative and another simulated a quantitative research project.

Results:

Using a stepwise approach we prompted ChatGPT to 1) synthesize two credible datasets, and 2) generate two manuscripts, in less than a day, that—in our judgment—would have been able to pass as credible graduation research papers at the health science master’s program the authors are currently affiliated with.

Conclusions:

Our demonstration highlights the ease with which an LLM can synthesize research data, conduct scientific analyses, and produce credible research papers for a master’s graduation. To uphold the integrity of academic standards, we recommend master’s programs to prioritize oral examinations and school exams. This shift is crucial to ensure a fair and rigorous assessment of higher-order learning and abilities at the master’s level.