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Generative AI challenges the traditional view of humanity centred on language/rationality and impacts language-related majors. This study investigates the relationship between taking a ‘Computational Linguistics’ course and the evolution of explicit and implicit human-machine cognition among Chinese Linguistics majors. Through a longitudinal controlled experiment combining questionnaires and the Implicit Association Test (IAT), it explores how the acquisition of technological knowledge interacts with and reshapes perceptions of human uniqueness. The results indicate that course learning did not alter university students’ factual understanding of human–machine attributes, nor did it change their value judgments regarding human distinctive attributes and shared attributes. At the level of implicit cognition, students generally exhibited a strong association between humans and distinctive attributes, and the strength of this association was not affected by course learning. However, course performance correlated with variations in the salience of this association. Students with higher academic achievement showed a more pronounced implicit human distinctive attribute association than those with lower academic performance.
Zhang et al. (Fri,) studied this question.