Register alignment of ChatGPT-generated academic texts

The rise of Artificial Intelligence (AI) tools such as ChatGPT has transformed language pedagogy and assessment. Despite their growing use in academic contexts—from classroom materials to standardized testing—questions remain about the register appropriateness of the texts they produce. The humanlikeness of AI language must be defined not only by fluency or coherence, but by register appropriateness—functional language use that aligns with the situational characteristics of registers. This study investigates whether ChatGPT-generated academic texts mimic human-authored writing in two academic genres (journal articles and textbooks) across two disciplines (biology and history). Using multi-dimensional analysis, we analyzed 200 texts (100 AI-generated and 100 human-authored) along three linguistic dimensions: (1) specialized information density vs. non-technical synthesis, (2) definition/evaluation of new concepts, and (3) author-centered stance. Our results reveal a mixed picture: while ChatGPT exhibits moderate success in mimicking register distinctions found in journal article registers, its performance is notably less aligned with textbooks. ChatGPT-generated textbook excerpts in biology, for instance, often resemble the dense, technical style of journal articles, as a result failing to match the simplified, pedagogically oriented discourse found in human-authored textbooks. Our findings indicate that while ChatGPT can largely reproduce human-like register patterns in journal article writing, it struggles to achieve the same in textbook contexts, particularly within biology. Overall, the results suggest that ChatGPT-generated texts often lack sufficient functional appropriateness. We therefore recommend further quantitative linguistic analyses of AI-generated language and urge caution when using ChatGPT for content creation.