This study utilizes a qualitative methodology informed by phenomenology to examine student–machine interactions in higher education, investigating whether students interpret AI responses as impersonal outputs or attribute human-like qualities to them. Drawing on Edmund Husserl’s concepts of active and passive consciousness, alongside recent phenomenological and anthropological work on AI, we analyze a substantial body of data tracking student–AI tutor interactions across various disciplines using a sociolinguistic approach. Our findings reveal a near-universal pattern of low-level instrumental anthropomorphization. Students frequently employ conversational norms, like politeness and personal pronouns, while remaining fully cognizant of the AI’s nonhuman nature. However, clear disciplinary differences emerged based on distinct epistemological perspectives. Computer science students primarily treated the tutor as a technical tool, whereas social science students engaged in more human-like dialogues, occasionally framing the AI as a “machine oracle”. These dynamics further interact with speech act types. Notably, friendlier, anthropomorphic language elicited more socially responsive answers from the tutor, whereas technical prompts produced mechanical replies. We also identified playful, meta-aware anthropomorphism, where students tested system limits through role-play. Ultimately, these findings demonstrate that anthropomorphization operates at both passive and active levels of consciousness, heavily influenced by linguistic conventions and disciplinary norms. While this humanization may support learning, it raises critical ethical concerns regarding overtrust, contributing to theoretical discussions of anthropomorphism and AI-supported educational design.
Németh et al. (Fri,) studied this question.