Artificial intelligence literacy is usually assessed through self-report scales. Such instruments show where a group stands, but not how its members reason about the technology in their own terms. Research on this topic has also concentrated on science and technology disciplines and on high-resource languages. It remains unclear how students working in low-resource languages, such as the Slavonic languages, articulate their artificial intelligence literacy, and what knowledge underlies their evaluative judgments. This study analyses written reflections and a background questionnaire from students of Slavonic studies, translation and language teaching who completed a one-semester course in artificial intelligence literacy. The reflections were produced after students used a generative artificial intelligence tool as both the commissioner and the collaborator of an individual academic project. They were examined through qualitative content analysis combining deductive and inductive categories. The findings show that the reflections centre overwhelmingly on evaluating outputs, while declarative knowledge of how such systems function remains a knowledge of symptoms rather than of mechanisms. Evaluative judgments depend heavily on disciplinary expertise in areas such as phonetics, lexicology, translation and language pedagogy, rather than on general critical thinking, and target precisely the errors a domain-uninformed reader would not detect. The questionnaire further shows that students already held a critical disposition toward artificial intelligence before the course began, which constrains how the reflective practice documented here can be interpreted. Taken together, the results reposition disciplinary expertise, more than technical understanding of artificial intelligence, as the primary resource for critical evaluation, with direct implications for teaching artificial intelligence literacy and language technology in higher education.