AI for Higher Education Management

The programme

Course contents

Each module contributes one component of the institutional project. The contents rest on documented cases with verifiable results, covering implementations that met their objectives and projects that did not.

1. 01

   Global and regional context

   Establish the state of artificial intelligence adoption in higher education, globally and in Latin America, and its distance from institutional governance.

   • State of adoption among students and academic staff, and the share of institutions with formal policies
   • The transition from open experimentation towards governed and measurable implementations
   • Latin America: institutional guidelines, home-grown capability and the priorities of the regional agenda
   • Distribution of value across institutional functions: academic, student and administrative
2. 02

   Cases of success and failure

   Analyse documented implementations with verifiable results and identify the design factors behind the results obtained and the objectives not met.

   • Georgia State University: predictive analytics applied to student advising, and its operating model
   • Ivy Tech: early detection of academic risk at scale in the first weeks of the term
   • Tecnológico de Monterrey: TECgpt and the AIGEN network as home-grown capability in the region
   • Arizona State University: capturing efficiencies in administrative and support functions
   • Los Angeles school district: problem definition, vendor selection and data protection
3. 03

   What artificial intelligence is and what it implies for higher education

   Establish a shared understanding of the real capabilities and limits of the technology, and of the ethical, regulatory and governance obligations its institutional use imposes.

   • What a machine learning system does and does not do, and what sets generative artificial intelligence apart
   • Institutional ethics: transparency towards the community, algorithmic bias and equity of access
   • Student data protection and the regulatory frameworks in force across the countries of the region
   • Governance: principles, the accountable body, the use-case review cycle and accountability mechanisms
   • Implications for teaching, assessment and academic integrity
4. 04

   Cases applied to universities and technical education institutions

   Examine applications differentiated by institution type, scale and level of data maturity.

   • Student retention and advising: early alerts and the human intervention that resolves them
   • Administrative processes: admissions, academic records, student finance and technical support
   • Technical and vocational education: links with industry, placements and employability
   • Differences in scale and data maturity between complex universities and technical institutions
5. 05

   General application model

   Hold a replicable method to prioritise, size and assess an artificial intelligence implementation in any area of the institution.

   • Prioritisation by volume, repetitiveness, data structure and the current cost of the process
   • Audit of available data: existence, quality, access and accountable owners
   • The decision to procure, adapt or develop, and the minimum data each alternative requires
   • Vendor assessment criteria: track record in higher education, data ownership, termination terms and metrics agreed in the contract
   • Construction of the financial case and definition of the ninety-day plan
6. 06

   Institutional project

   Integrate the components developed into an implementation project ready to present to the institution's leadership.

   • Integration of the six components into a one-page document
   • Definition of milestones, owners and explicit criteria for continuation or closure
   • Preparation of the presentation to peers and mentors
   • Final adjustment of the project from the observations received