Strategic Integration of Artificial Intelligence in Educational Quality: Diagnosis and Proposal for Universities in Amazonas, Peru

Resumen

Artificial intelligence (AI) in higher education faces the challenge of translating technological adoption into effective improvements in educational quality, especially in contexts with infrastructural limitations such as Amazonas region, Peru. This research aligned with SDG 4 (quality education) and aimed to propose a model for integrating artificial intelligence into public policies to increase educational quality in public universities of Amazonas region. A basic study was developed with a quantitative approach, non-experimental cross-sectional design, and propositional scope. The sample included 365 participants (students and faculty) from UNTRM and UNIFSLB, selected through simple random probability sampling. A validated Likert-type questionnaire processed with SPSS 27 was applied. Results revealed a paradox: 98.4% reported medium-high levels of AI integration, but only 29.9% perceived high educational quality. The most deficient dimensions were equity (24.1% low), relevance (20.3% low), and effectiveness (20.8% low). It was concluded that high spontaneous AI adoption does not generate proportional improvements without clear institutional policies, systematic training, and ethical governance frameworks, justifying the proposed model of strategic integration into educational public policies, which was validated by experts.