The Invisible Mentor: Generation Z’s Hidden Use of Gen Ai in Learning Processes
DOI:
https://doi.org/10.15170/MM.2026.60.03.06Keywords:
generative AI, higher education, Generation Z, shadow learning, digital skills developmentAbstract
THE AIM OF THE PAPER
The emergence of generative artificial intelligence (GenAI) has triggered substantial changes in higher education. Although the integration of this technology is increasingly widespread, there remains considerable uncertainty about how transparently students disclose or cite GenAI use, which contributes to the phenomenon commonly referred to as “shadow learning”. This study investigates the contradiction between Generation Z students’ GenAI usage patterns and their disclosure (citation) practices, and it examines factors associated with concealment.
METHODOLOGY
We conducted an online questionnaire study combining quantitative and qualitative items (N = 363; 99.7% Generation Z). Data were analyzed using descriptive statistics, chi-square tests, and thematic content analysis. The sample was collected via snowball sampling; it is non representative, Budapest-based, and male-skewed, therefore results should be interpreted as sample-specific rather than population-level estimates.
MOST IMPORTANT RESULTS
Overall, 87.4% of respondents reported using AI tools regularly (daily or weekly), primarily for learning and research (227 mentions). In parallel, disclosure practices indicate a substantial lack of transparency: 41.0% reported that they rarely or never indicate GenAI use, and an additional 31.1% do so only when required by external rules. Conceptual uncertainty was significantly associated with concealment (chi-square test: p=.011). In contrast, usage intensity was not associated with transparency; daily users omitted disclosure at a similar rate to less frequent users.
RECOMMENDATIONS
Practical recommendations are derived directly from the study’s hypotheses. (H1) Because usage intensity was not related to transparency, disclosure expectations should not be tied to frequency of use; instead, institutions may adopt a consistent cross-course disclosure protocol (e.g., a brief AIuse statement attached to submissions). (H2) Given the significant association between definitional uncertainty and concealment, institutions should provide an operational definition of disclosable GenAI use (e.g., text generation, paraphrasing, ideation, translation), supported by brief examples and boundary cases. (H3) As GenAI is used primarily for process support (learning, explanation, research), transparency can be strengthened by assessment and documentation elements that make the learning process visible (e.g., optional prompt/interaction logs, short reflective add-ons, or brief oral defenses), enabling legitimate learning support without compromising academic integrity.
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