Detecting Artificial Intelligence-Generated Personal Statements in Professional Physical Therapist Education Program...

The objective of this study was to compare the lexical sophistication of personal statements submitted by professional physical therapist education program applicants with those generated by OpenAI's Chat Generative Pretrained Transformer (ChatGPT).

Personal statements from 152 applicants and 20 generated by ChatGPT were collected, all in response to a standardized prompt. These statements were coded numerically, then analyzed with recurrence quantification analyses (RQAs). RQA indices including recurrence, determinism, max line, mean line, and entropy were compared with t-tests. A receiver operating characteristic curve analysis was used to examine discriminative validity of RQA indices to distinguish between ChatGPT and human-generated personal statements.

ChatGPT-generated personal statements exhibited higher recurrence, determinism, mean line, and entropy values than did human-generated personal statements. The strongest discriminator was a 13.04% determinism rate, which differentiated ChatGPT from human-generated writing samples with 70% sensitivity and 91.4% specificity (positive likelihood ratio = 8.14). Personal statements with determinism rates exceeding 13% were 8 times more likely to have been ChatGPT than human generated.

Although RQA can distinguish artificial intelligence (AI)-generated text from human-generated text, it is not absolute. Thus, AI introduces additional challenges to the authenticity and utility of personal statements. Admissions committees along with organizations providing guidelines in professional physical therapist education program admissions should reevaluate the role of personal statements in applications.

As AI-driven chatbots like ChatGPT complicate the evaluation of personal statements, RQA emerges as a potential tool for admissions committees to detect AI-generated statements.