Conference Agenda

The pedagogical integration of artificial intelligence (AI) in engineering education represents a strategic challenge for higher education institutions, particularly in developing contexts where technological, curricular, and faculty-related factors converge. This study analyses the determinants influencing the pedagogical integration of AI in engineering programs at an Ecuadorian university. A quantitative, non-experimental, cross-sectional design was employed with a sample of 87 engineering faculty members. A structured instrument was developed and validated to assess three analytical dimensions: faculty readiness, curricular alignment, and technological conditions. From these, three normalized sub-indices and a Global AI Maturity Index were constructed. Results indicate an advanced level of faculty readiness, a high but still developing level of curricular integration potential, and an emerging level of technological maturity. Multiple linear regression analysis revealed that prior digital experience and basic AI competencies are significant predictors of pedagogical AI integration, while years of teaching experience and institutional support showed no significant effect. These findings suggest that early-stage AI adoption in engineering education is primarily driven by individual faculty capacities rather than institutional technological maturity. The study highlights the need for applied pedagogical training, curriculum redesign, and strengthened technological infrastructure to achieve a more systemic and sustainable integration of AI in higher education

Keywords-- Artificial intelligence in education, engineering education, pedagogical integration, digital competence, higher education innovation