Graduates of the Artificial Intelligence Engineering program at Universidad de Las Américas are competent, enterprising professionals with an international-global vision. Graduates are equipped with the expertise to develop and apply intelligent systems, effectively solve problems, and make data-driven decisions in various fields and sectors. This capability empowers them to lead projects with a focus on excellence, innovation, and social commitment.
Furthermore, graduates analyze, design, develop, implement, and evaluate algorithms, models, and agents to address complex challenges across multiple industries requiring computational intelligence. While doing so, graduates apply engineering design principles, account for multiple constraints, and conduct appropriate experiments using artificial intelligence methods, techniques, and methodologies, always adhering to ethical standards and regulatory frameworks. Graduates possess strong communication skills, assume leadership roles in artificial intelligence solution development teams, and continuously acquire and apply new knowledge as needed using appropriate learning strategies.
Finally, graduates are expected to assume diverse roles within multidisciplinary teams in both national and international contexts. Graduates will drive transformations through Artificial Intelligence technologies, automating tasks, optimizing resources, and improving decision-making processes. In addition, graduates will contribute to the development of innovative products, services, and technologies that tackle real-world challenges. Throughout their professional practice, graduates will act with integrity and ethical responsibility, adhering to legal regulations for the benefit of society while remaining committed to national development.
Demographic Data - provides information about student enrollment disaggregated by gender and ethnicity, offering insight into the demographic diversity within the program.
Student Completion - provides information about retention and graduation rates calculated through the 2021–2022 academic year, based on cohorts of new, first-time students entering the program in the fall semester, regardless of enrollment in the daytime or evening instructional delivery (if applicable). These calculations exclude incoming transfer students. Specifically, retention rates are reported at one-year and two-year intervals.
In addition, graduation rates are calculated according to each program’s duration length (100%) and within 150% of the normal time for a bachelor’s degree. Gender-based graduation data reflects only the distribution of actual graduates within each cohort and does not consider the original gender composition of the entering group.
Scholarship Information - provides information about student financial support, with data disaggregated by the type of scholarship awarded.
Geographic Data - provides information about the geographic composition of students enrolled in the program, based on place of origin.
Additional Demographic Data - provides information about students’ marital status, disability status, and first-generation college attendance.
All programs utilize the Brightspace platform to collect and assess student work and to compile data and evidence of student achievement. The resulting outcomes and their analysis, which focus on identifying areas for improvement, are presented in the program’s assessment report. In the graphic below, the most recent assessment period for each Program Learning Outcome (PLO) is indicated, along with the percentage representing the level of achievement of the expected performance standard for that PLO, based on the rubric used to evaluate student work.