Generative artificial intelligence is increasingly embedded in students’ academic work, but student adoption does not by itself indicate that universities have established the literacy, policy, assessment, and support conditions required for responsible use. This study examines student AI engagement at the University of the Philippines Diliman through a quantitative, descriptive-correlational, cross-sectional survey of 87 student respondents. It analyzes five interrelated domains: reported AI normalization, perceived AI readiness, perceived institutional stabilization, perceived legitimacy of AI-enabled academic support services, and educational concern. Findings show that students reported regular access to and use of AI tools for central academic tasks, particularly grammar checking, information search, summarization, paraphrasing, and drafting. However, this behavioral normalization coexisted with uneven perceived readiness, strong expectations for student and faculty training, only partial confidence in policy clarity and feedback mechanisms, selective support for AI-enabled academic services, and persistent concern about overreliance, fairness, and educational value. Exploratory correlation results further showed that AI use intensity was moderately associated with perceived readiness and support expectations, but was nearly unrelated to perceived institutional stabilization and educational concern. These patterns support a bounded interpretation of student-perceived normalization without stabilization: AI has become routine in student academic practice before students perceive the university’s literacy, policy, and support conditions for responsible use as fully stabilized. The study contributes a Global South public university case that links adoption, readiness, institutional support, legitimacy, and concern within a single analytical model, while avoiding unsupported claims about actual institutional governance or objective AI competence.

Palestra sobre a temática de Inteligência Artificial e a Automação na Indústria e no Comércio

NO2

Heatmap Made with reactablefmtr

Statistical Diagnostic

Taller académico desarrollado en lenguaje R utilizando R Markdown. Incluye la resolución de ejercicios relacionados con tipos de datos, estructuras condicionales, ciclos y operaciones matemáticas.