Universities must adapt as AI becomes uninvited but permanent academic participant

The findings redefine how responsibility and agency are distributed in education. Teachers still design and supervise, but AI manages a growing share of information exchange and cognitive scaffolding. The authors note that universities must now account for AI-mediated communication as a standard feature of academic life, an element that affects motivation, assessment validity, and curriculum design.

Artificial intelligence has quietly moved from the sidelines of academia to the core of the university environment, according to a new study published in Societies.

Titled “(Un)invited Assistant: AI as a Structural Element of the University Environment,” the study states that AI is no longer a peripheral tool but a structural participant in higher education. Through a multi-country analysis and empirical testing, the researchers argue that universities have entered a new “three-actor” era, teachers, students, and AI, fundamentally altering how learning, evaluation, and academic management operate.

From supporting tool to structural participant

The authors track how AI’s integration into academia has evolved beyond automation or plagiarism detection. Their quantitative research, spanning 1,197 students across Bulgaria, Kazakhstan, Poland, Slovakia, and Ukraine, demonstrates that regular AI use by students is statistically significant and widespread. This consistent engagement, they write, transforms AI from a passive support instrument into a functional member of the educational process.

The paper introduces a “3D model” of higher education to capture this structural change. The X-axis represents students, the Y-axis academic staff, and the Z-axis the intensity of AI use. Once the Z-axis becomes active, higher education ceases to be a two-dimensional interaction between teachers and learners, it becomes a three-party ecosystem where AI influences communication, learning design, and assessment feedback. The authors call AI an “uninvited assistant” because its arrival was spontaneous and student-driven rather than institutionally planned, yet it has embedded itself in every layer of academic practice.

Evidence from the classroom: Measuring the shift

The researchers combine bibliometric review with statistical testing to map AI’s trajectory in education research and practice. Their t- and z-tests confirm that AI usage among students is not incidental but structural, forming measurable behavior patterns that shape study habits and information processing.

This empirical grounding gives weight to the idea that AI has already passed the threshold of experimental adoption. Students rely on large-language-model chatbots, adaptive writing tools, and semantic search systems for tasks ranging from note synthesis to research interpretation. In many institutions, AI systems now mediate learning outcomes, providing predictive analytics that flag at-risk students and streamline feedback cycles for instructors.

The findings redefine how responsibility and agency are distributed in education. Teachers still design and supervise, but AI manages a growing share of information exchange and cognitive scaffolding. The authors note that universities must now account for AI-mediated communication as a standard feature of academic life, an element that affects motivation, assessment validity, and curriculum design.

Governance, ethics, and the need for new academic norms

The study warns that acknowledging AI as a structural actor brings serious governance challenges. Academic integrity policies, quality assurance systems, and pedagogical frameworks remain anchored in a two-actor model that no longer reflects reality. The authors call for systemic adaptation, urging institutions to revise regulations and teaching standards to include AI-assisted learning as a legitimate, auditable process rather than an anomaly.

They outline three key areas demanding reform:

• Ethical Governance: Establishing transparent norms for acceptable and prohibited AI use, supported by university-level codes of conduct.
• Quality Management: Integrating AI analytics into institutional monitoring so that algorithms help identify learning difficulties without eroding human oversight.
• Policy Synchronization: Developing national and regional guidelines to ensure consistency across universities, especially as students’ cross-border use of AI platforms grows.

The paper views ethics not as a set of restrictions but as a driving force in operational design. Since AI influences grading, authorship, and originality, responsibility must be shared among all three actors. The stability of the academic system depends on recalibrating accountability: teachers must verify AI-generated outputs, students must disclose AI assistance, and universities must monitor algorithmic fairness.

A new educational geometry

The proposed 3D model represents a conceptual turning point. Rather than treating AI as an external input, the model embeds it within the educational geometry of teaching and learning. The authors suggest that this framework can extend to other public systems, healthcare, administration, labor markets, where AI similarly assumes operational roles alongside humans.

For universities, the model implies strategic reorganization. Curricula must evolve to teach AI literacy, enabling students to critically evaluate algorithmic results and manage co-creation with machine systems. Teacher-training programs should incorporate AI interpretation skills so instructors can adapt pedagogical methods to environments where digital systems share cognitive space with learners.

The study also anticipates social consequences. By mediating access to information, AI alters the power dynamics of knowledge creation. The academic hierarchy, professor, student, institution, now competes with machine intelligence capable of generating, summarizing, and validating information at scale. The authors describe this as a “restructuring of educational communication,” where authority is negotiated among humans and intelligent systems.