AI Faculty Summit: Advancing Discipline-Driven AI Integration in Higher Education

This best practice documents Tecnológico de Monterrey’s institution-wide implementation of its AI in Education strategy across seven Schools, embedding artificial intelligence within disciplinary contexts as part of a broader institutional transformation. The initiative was delivered through a flagship academic experience designed to translate institutional AI strategy into faculty-led educational innovation.

The approach centred on a three-phase process that combined collaborative design, guided implementation, and structured dissemination. Through this process, faculty and academic leaders worked together to design, test, and refine AI-enabled educational initiatives aligned with disciplinary learning objectives and professional contexts. The experience reflects Tecnológico de Monterrey’s faculty-centred and discipline-driven approach to integrating AI into higher education.

The initiative was implemented through a three-phase process.

The first phase was the AI Faculty Summit, a hands-on, sprint-based academic experience embedded within Tecnológico de Monterrey’s National Faculty Meeting. Nearly 400 faculty members from high school, undergraduate, and postgraduate programmes participated, having been selected by academic associate deans and faculty leadership to ensure alignment with institutional priorities. Over three intensive days, participants engaged in eight structured collaborative work sprints focused on designing AI-enabled educational projects grounded in specific disciplinary and professional contexts. This phase resulted in the development of 90 detailed proposals.

The second phase was the Test and Learn stage, conducted from July to December 2025. During this period, selected proposals entered implementation and were iteratively refined through qualitative feedback, reflection, and cross-school learning. This phase enabled Schools to assess progress, identify effective practices, and adjust designs based on early implementation insights.

The third phase focuses on dissemination. Outcomes, lessons learned, and measurable impacts from the implementation phase are shared at the AI in Education Summit, held as part of the Future of Education Conference in January 2026. This phase supports institutional learning and enables knowledge sharing beyond the university.

Impact was assessed at two complementary levels: institutional impact and process-level impact.

At the institutional level, impact was evaluated based on the successful translation of the AI in Education strategy into faculty-owned, discipline-specific initiatives across the seven Schools. Indicators included the breadth of faculty participation, the alignment of projects with disciplinary learning objectives, and progress toward embedding AI-enabled educational experiences within curricula.

At the process level, indicators were tracked across each phase of implementation. Quantitative indicators included the number of participants (nearly 400 faculty members), the number of AI-enabled educational proposals developed (90), and the number of proposals entering implementation during the Test and Learn phase. Qualitative indicators focused on faculty engagement, the quality and feasibility of project designs, the effectiveness of sprint-based collaboration, and evidence of alignment between pedagogical intent and AI integration.

Additional indicators included the successful execution of the Test and Learn phase, institutional readiness to support scale, and the longer-term integration of AI-enabled educational experiences across programmes. Together, these indicators provide evidence of how the initiative supported coordinated implementation of the institutional AI strategy.

• Strong institutional AI in Education strategy
• Discipline-driven, sprint-based design methodology
• Leadership-supported faculty selection and participation
• Test & Learn implementation model
• Dedicated dissemination through national and international academic forums