Presenting pilot research on AI governance in higher education at LitCon 2026

At the 6th International Conference “Literacies and Contemporary Society: From Skills to Practices” in Nicosia, I had the opportunity to present the results of pilot research on digital communication and artificial intelligence in higher education.

The study offers a Bulgarian perspective based on data collected in February 2026, with contributions from students and faculty members at the Faculty of Journalism and Mass Communication at Sofia University “St. Kliment Ohridski.”

I am particularly grateful to my academic colleagues and students who participated in the research and contributed their experiences and perspectives. I also appreciate the support and academic exchange with Prof. Teodora Petrova.

AI adoption is advancing faster than institutional governance

The pilot findings identify a structural tension within contemporary higher education.

Artificial intelligence tools are already deeply embedded in academic practice. Students and faculty use them for research, writing, content development, translation, analysis, planning, and other learning-related activities. Institutional governance, however, remains fragmented and uneven.

Universities are still developing policies that define acceptable AI use, disclosure requirements, academic responsibility, data protection, and the relationship between AI-supported work and academic integrity. This creates an environment in which adoption may be widespread, while expectations and responsibilities remain unclear. Because this was a pilot study, the findings should be treated as exploratory rather than representative of the entire higher-education sector. Nevertheless, they point to governance challenges that universities can no longer address solely through individual course rules or informal guidance.

AI literacy must extend beyond technical competence

One of the central implications is that AI literacy cannot be reduced to knowing how to operate a particular tool.

Technical competence is only one part of responsible AI use. Students and educators must also be able to:

• Assess the reliability and limitations of AI-generated content

• Recognize possible bias and fabricated information

• Protect confidential, personal, and institutional data

• Explain when and how AI has contributed to academic work

• Distinguish legitimate assistance from academic misconduct

• Retain responsibility for final decisions and submitted content

AI literacy therefore combines technical ability with critical judgement, ethical awareness, and communication competence.

Governance requires transparency and accountability

Universities need governance frameworks that establish clear expectations for students, faculty members, researchers, and administrators.

These frameworks should define where AI use is permitted, where it is restricted, and when disclosure is required. They should also clarify who remains accountable when AI-supported outputs contain inaccuracies, bias, copyright concerns, or other problematic material.

A governance approach based only on prohibition is unlikely to reflect actual academic practice. At the same time, unrestricted use without transparency may undermine assessment validity, authorship, and trust.

Institutions therefore need policies that are understandable, applicable across disciplines, and flexible enough to respond to technological change.

Academic integrity policies must reflect current practice

Existing academic integrity frameworks were largely developed before generative AI became widely accessible.

Universities must now examine how established principles—such as authorship, originality, attribution, independent work, and responsible source use—apply when students and educators work alongside intelligent systems.

This does not require abandoning traditional academic standards. It requires translating those standards into a new technological context.

Clear guidance can help students understand not only what is prohibited, but also how AI can be used responsibly as part of research, learning, and academic communication.

The future depends on governance capacity

The central conclusion of the study is clear: the future of AI in higher education will depend not only on technological capability, but on institutional governance capacity.

Universities that develop coherent policies, invest in AI literacy, and connect technology use with academic integrity will be better positioned to benefit from AI while managing its risks.

The conference in Nicosia provided a valuable setting in which to discuss these findings with international researchers, educators, and policy representatives. It also reinforced the importance of connecting research evidence with institutional decision-making as higher education adapts to rapidly changing digital practices.

The conference presentation is available through my ResearchGate profile.