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Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback.