Are universities ready for the AI era? Students are already 'replacing' teachers

According to researchers, generative AI has become a natural and integrated part of students’ everyday lives. Despite this, public discussions around AI in education often remain narrowly focused on issues such as cheating, assessment, and institutional control. However, research findings suggest that the ongoing transformation of higher education is far more profound and structural than these debates imply.

Today, students regularly use AI tools to search for information, explain connections between concepts, summarize academic literature, and test ideas. In many cases, these activities take place without the direct involvement of teachers. Alongside this shift, some students are beginning to question which skills are truly necessary in an academic environment where a significant portion of work can be completed with, or by, AI. At the same time, many students express concern that higher education does not provide sufficient training in how to use AI in ways that align with the expectations of working life and broader society.

What is the value of higher education institutions?

These changing patterns of behavior and evolving expectations pose a challenge to the traditional role of universities. Historically, higher education institutions have been built on specific assumptions about what it means to learn, to acquire knowledge, and to demonstrate academic competence. As knowledge production and problem-solving increasingly occur through interaction with AI, universities are faced with the need to redefine, clearly articulate, and justify their learning outcomes and pedagogical approaches so that they remain meaningful for today’s students.

“This leads to a fundamental question: what does higher education actually offer that cannot be replaced by AI, and how can this value be communicated and realized in practice?” says Tiina Leino Lindell, postdoctoral researcher and one of the study’s authors, who conducted the research together with Professor Christian Stöhr.

Based on interviews with both teachers and students, the studies show that students are increasingly using AI to manage their time more efficiently. Tasks that are perceived as boring, repetitive, or irrelevant to future careers are often delegated to AI tools, while activities considered important for personal growth and development are given higher priority. This shift has created tensions, as teachers express concern that students may overlook foundational elements of their education or practice essential skills too infrequently.

Academic integrity over technology

Another significant challenge identified in the research is the mismatch between the rapid pace of technological development and the relatively stable, long-term structures of university organizations. As a result, discussions about digitalization often become reactive: educational practices change first, while formal guidelines are developed afterward. The researchers argue that universities should move away from technology-specific rules and instead adopt principle-based approaches.

“Guidelines that are closely tied to individual technologies risk becoming outdated as tools and patterns of use continue to evolve,” says Christian Stöhr. “A recurring theme in the material is therefore the need for guidelines grounded in overarching pedagogical principles, such as academic integrity, transparency, clearly defined learning outcomes, and responsibility. This approach reduces the need to rewrite regulations every time new technologies emerge.”

Stöhr also emphasizes that questions surrounding AI and pedagogy should not be left solely to individual teachers. Instead, they should become an integral part of universities’ collective efforts to define educational goals and maintain academic quality.

“Our studies do not show how AI should be used in higher education,” Stöhr notes. “However, they clearly demonstrate that both students and teachers are already engaging with the technology in ways that challenge established norms. This makes questions related to goals, responsibility, and pedagogy increasingly difficult to postpone.”

The aim of the study was to examine how generative AI affects norms, roles, and teaching practices in engineering education from the students’ perspective. The research is based on interviews with 25 engineering students who actively use generative AI in their studies. The paper was published in the Journal of Computing in Higher Education.

Four key themes identified in the study

Students’ self-directiveness and efficiency:
Students use generative AI to address practical challenges such as language translation, understanding theoretical material, and debugging code. They tend to view AI as a fast and constantly available mentor, especially when compared to traditional digital tools, which are often perceived as slow or insufficient.

Learning objectives are being questioned:
Many students believe that mastering generative AI is essential for being prepared for the labor market. As a result, they adapt their learning strategies to what they perceive as future professional demands. However, they also point out that higher education does not provide enough structured support in this area.

The role of teachers is changing:
When students’ actual use of AI does not align with teachers’ formal rules, the teaching role itself begins to shift. Students increasingly rely on AI for simpler questions, which reduces direct interaction with teachers. Differences in perspectives between students and teachers regarding how AI use should be limited create additional challenges.

The ethics of cheating are being challenged:
When high academic workloads are combined with the efficiency of AI tools, new boundary-drawing practices can emerge. These practices may come into conflict with established principles of academic integrity.

A second study, published in Learning, Media and Technology, was conducted in two phases. The first phase involved individual and group interviews with engineering students from 13 different academic programs. The focus was on whether, how, and why students use generative AI, as well as their views on its advantages, disadvantages, and the possible need for rules or guidelines. The responses were grouped into five themes.

In the second phase, university teachers, postdoctoral researchers, and educational developers used these themes as a starting point to explore several possible future scenarios rather than predicting a single outcome. This scenario-planning approach was used to identify challenges and imagine future directions. A two-year timeframe was selected to ensure the discussion remained both realistic and forward-looking.