Biography
Biography pending.
Conference contributions 1
BLIND SPOTS. THE (CURRENT) LIMITATIONS OF ARTIFICIAL INTELLIGENCE IN ACADEMIC EDUCATION. THREE CASE STUDIES IN TEACHING LITERARY INTERPRETATION, PRAGMATICS, AND ROMANIAN AS A FOREIGN LANGUAGE
Situated at the intersection of contemporary theoretical debates on intelligent technologies and the humanistic imperative to rethink academic education, this study examines the blind spots of artificial intelligence - what AI does not see, or it sees only partially - in order to assess the conditions of its pedagogical legitimacy. We argue that new theoretical, critical, and pedagogical instruments are necessary, but that their development must be accompanied by a rigorous concern for what remains specific to human thought: hermeneutic subtlety, critical discernment, pragmatic sensitivity, and intercultural understanding. The first case study examines literary and fictional texts, showing that AI tends to neutralise ambiguity, reduce symbolic density, overlook aesthetic singularity, and transform interpretation into stereotypical paraphrases. The second case study focuses on pragmatics, where AI frequently fails to calculate implicature, grasp irony, humour, interpret silence, indirectness and culturally coded forms of interaction. The third case study addresses Romanian as a foreign language, where these limitations become particularly visible in contexts involving intercultural mediation, idiomatic expression, pragmatic politeness, aspectual nuance, register variation, and the symbolic value of culturally embedded texts. Reflecting together on these blind spots is crucial for the formation of younger generations and early-career researchers. Rather than being regarded merely as operational limitations of intelligent technologies, the blind spots of AI must be consciously identified, critically examined, and methodologically integrated into academic disciplines. Students need to distinguish algorithmic processing from human thought: how algorithms generate, classify, and predict information, and how human understanding interprets, contextualises, evaluates, questions, and assumes responsibility for meaning. In this sense, the blind spots of AI should become part of the very process through which students learn to read, interpret, assess, contextualise, and produce knowledge. Such an approach transforms AI literacy into a broader pedagogical competence: the capacity to understand its limits, to question its outputs, and to preserve the contextual, ethical, pragmatic, symbolic, aesthetic, and intercultural dimensions of human understanding.