The emergence of Generative AI (GenAI) has significantly transformed the learning and writing practices of students in higher education. With the support of advanced large language models (LLMs) like GPT-4, students can now complete course assignments with greater quality and efficiency. Previous studies have extensively examined the situational and linguistic differences between human-written and AI-generated texts across various registers, highlighting the fundamental linguistic distinctions between the two (Goulart et al., 2024; Barabara et al., 2024). This study analyzes a corpus of final-year research reports written by undergraduate engineering students from an L2 context, aiming to identify dimensional variations in texts with differing levels of AI intervention (measured by AI scores) following the release of ChatGPT. Employing both qualitative and quantitative methods, we applied Biber’s (1998) multidimensional framework to examine lexico-grammatical features and distinguish AI-mediated texts at varying degrees from original student-authored reports. The findings reveal that reports with high and low levels of AI intervention differ in 19 linguistic features across three textual dimensions. Compared to Biber’s established genre profiles, the low-AI intervention group aligned more closely with academic writing, whereas the high-AI intervention group exhibited features resembling non-academic genres.