The use of artificial intelligence in universities has become nearly universal in just a few years. Educational digital tools are multiplying, adaptive learning platforms are gaining ground, and institutions are investing in digital infrastructures. Despite this acceleration, institutional governance struggles to keep pace: according to UNESCO, a small percentage of universities had a formal AI policy in 2025, revealing a gap between actual practices and the frameworks that govern them.
European Regulation on AI and Education: What Changes in 2026
The debate on digital innovation in education can no longer ignore the regulatory dimension. The European Union has reached a milestone with the implementation, starting August 2, 2026, of new transparency obligations under the European AI regulation. For educational institutions deploying AI tools (automated grading, learning path recommendations, plagiarism detection), this means enhanced requirements for traceability and user information.
Further reading : Trends and Tips for Successful Real Estate Investments in 2024
Specifically, a student interacting with an AI system as part of their training must now know that they are interacting with a machine. Institutions must document the operational logics of these tools. This constraint pushes universities to structure their digital governance, whereas many have operated without a formalized framework until now.
The resources available on mitxdesigntech.org illustrate how certain initiatives are attempting to bridge technological design and pedagogy, aligning digital skills with the concrete needs of teachers and learners.
Related reading : The best nightclubs in Rennes: discover the must-visit spots
However, the European regulation does not prescribe a pedagogical model. It sets safeguards without specifying how to integrate AI into a course. This gap leaves each institution facing its own decisions, with very uneven levels of preparedness from one country to another.
Digital Skills of Teachers: The Underestimated Link
Training the leaders of tomorrow first requires training those who support them. The governance of AI in education is evolving towards usage frameworks centered on teachers’ skills, rather than solely on general ethical principles. UNESCO launched a national AI competency framework for teachers in Egypt in July 2026, signaling a shift towards operational tools.
This shift is significant. For several years, the discourse on educational technology has focused on equipment (tablets, platforms, connectivity) and principles (ethics, inclusion, personal data). The issue of concrete skills development for teachers remained secondary in public policies.
Field feedback varies on this point. In some contexts, teachers quickly adopt generative AI tools to prepare their lessons or differentiate learning. In others, technological overload hinders adoption.
What an AI Competency Framework for Teachers Entails
Such a framework is not limited to listing software. It involves defining:
- The ability to critically analyze results produced by AI, to prevent teachers from becoming mere conduits for automatically generated content
- Mastery of issues related to student data, particularly in a context where the European regulation imposes new transparency requirements
- The ability to design educational activities that use AI as a learning lever, rather than as a substitute for teaching
A framework without associated training remains an administrative document. The challenge lies in scaling, meaning the ability to reach the entire teaching staff and not just volunteers already familiar with digital tools.
Institutional Preparation: Beyond Pedagogical Innovation
The debate on educational technology is gradually shifting from pedagogical innovation to “institutional preparation.” UNESCO is now emphasizing methodologies for assessing the digital maturity of institutions.
This shift in focus deserves attention. An institution can multiply innovative digital projects without being institutionally ready to sustain them. Institutional preparation encompasses data governance, ongoing staff training, cybersecurity infrastructures, and the ability to evaluate the real impact of deployed tools.
Three Dimensions of Educational Digital Maturity
- The technical dimension: network infrastructure, servers, software licenses, and their long-term maintenance, not just during the launch of a pilot project
- The organizational dimension: the existence of internal policies on the use of AI, the designation of digital referents, the ability to arbitrate between competing tools
- The pedagogical dimension: the integration of digital tools into training frameworks, measuring actual learning outcomes, tracking the skills acquired by students
The available data do not allow us to conclude that the majority of higher education institutions have achieved a satisfactory level across these three dimensions simultaneously. The gap between technological adoption and institutional maturity remains the main barrier to a digital education that truly prepares for leadership.
Artificial Intelligence and Leadership Training: Current Limitations
AI promises to personalize training paths, identify high-potential profiles, and automate certain administrative tasks. These promises fuel the discourse on training the leaders of tomorrow. The reality is more nuanced.
Current generative AI tools excel at producing textual content and analyzing structured data. However, leadership relies on relational and decision-making skills that these tools do not model. The ability to manage uncertainty, to unite a team, to make an unpopular decision—these skills are forged through human experience, not through interaction with a chatbot.
The risk identified by several observers is that of training that confuses mastery of digital tools with leadership skills. A student capable of using AI to analyze a dataset is not necessarily prepared to lead a complex project involving stakeholders with divergent interests.
Frameworks for evaluating the intersection of artificial intelligence and skills in education are still under construction. Training the leaders of tomorrow with digital tools first requires clarifying what “leadership” means in an environment where AI takes on an increasing share of analytical tasks. This question remains largely open.