Generative AI has not changed how students learn. But it has quietly transformed what it means to teach. Many educators on the front line are no longer just facilitators of learning. They are auditors of process, interpreters of policy and, increasingly, enforcers of integrity. That shift in the classroom is both understandable and unsustainable.
The policy-practice gap
Translating university GenAI policy into everyday teaching is far from straightforward. Policies, which are all but ubiquitous across the sector, tend to be broad, cautious and institution-facing. Classrooms, by contrast, are messy, diverse and fast-moving. Educators are expected to bridge that gap. In practice, this means deciding what counts as acceptable AI use in a specific task, how to design assessments that remain meaningful, and how to guide students who are often unsure where the boundaries lie.
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Students, meanwhile, are navigating mixed messages. They are told to use AI tools to support learning but warned against over-reliance or misconduct. Unsurprisingly, uncertainty is widespread. This is where pedagogy, not policy, does the real work.
In our own teaching across large, diverse postgraduate cohorts, we found that banning or ignoring GenAI is ineffective. Instead, structured integration with clear expectations produces better outcomes. This can include requiring students to declare their use of GenAI and scaffolding critique tasks to move learning beyond production to analysis and evaluation.
These approaches align with emerging research showing that GenAI can support learning when combined with structured reflection, critical evaluation and iterative feedback. But they come at a cost.
The unintended consequence: teaching becomes surveillance
Teaching increasingly includes elements of investigation. This creates what many academics recognise but rarely articulate: a shift from guiding learning to scrutinising it.
Educators are now expected to:
- interpret whether AI use is appropriate or excessive
- verify student declarations and supporting evidence
- distinguish between acceptable assistance and misconduct
- manage grey areas where policies offer little clarity.
The workload implications are significant. Reviewing AI use is time-consuming. Evaluating reflections can feel repetitive. Adjudicating edge cases is cognitively demanding and often emotionally draining. In our own teaching, students were required to submit a GenAI usage template alongside weekly tasks, including tool declarations, transcripts and reflective commentary. On paper, this creates transparency; in practice, it shifts a significant interpretive burden on to the educators. For example, a student might submit a technically correct answer with completed template and a brief reflection stating that GenAI was used for “idea generation and refinement”. The task for the educator is no longer simply to assess the quality of the answer; now they must interpret the process:
- Did the student critically engage with the AI output or largely reproduce it?
- Does the reflection demonstrate genuine evaluation or is it formulaic compliance?
- Is the level of AI assistance appropriate for this stage of learning?
What appears as a structured, integrity-focused task thus becomes an exercise in ongoing interpretation. Across large cohorts, this process is repeated hundreds of times, turning routine assessment into a cognitively demanding form of audit work.
More importantly, it risks changing the tone of education. When students feel constantly monitored, trust can erode. When educators feel responsible for policing, the joy of teaching diminishes.
As one emerging pattern shows, the burden of maintaining academic integrity has fallen largely on individual educators, rather than being supported systemically. That is not a sustainable model.
Reclaiming the role of the educator; what teachers can do now
There is a difference between being a guide and being a gatekeeper; but in a GenAI-enabled classroom, educators are often asked to be both. The challenge is not to eliminate that tension, but rather to manage it deliberately. Making AI use visible, focusing on how students think rather than just what they produce, and working through AI outputs together in class are small but practical ways to shift the balance back towards learning.
Beyond the classroom, universities can distribute responsibility for GenAI integration rather than leaving it to individual lecturers. This might involve learning designers, librarians and academic skills teams. Educators can work with learning designers to embed simple, consistent GenAI practices such as declarations or staged reflections directly into assessment design, rather than retrofitting checks during marking.
Librarians can support students’ skills in evaluating and citing AI-generated content, while academic skills teams can help address the gap between polished AI-assisted writing and deeper critical thinking. Some institutions have gone further: Erasmus University Library, for example, now offers a dedicated GenAI e-module for teaching staff, while Jisc provides practical staff demos tailored to teaching staff, professional services and learning resource centre teams.
Instead of constantly trying to detect misuse after the fact, educators want to help students question, refine and take ownership of their thinking, even when that thinking is supported by AI. Because at its best, teaching is not about verifying what students did. It is about helping them understand why it matters and how to do it better.
Meena Jha is an associate professor and head of technology and pedagogy cluster CML-NET in the School of Engineering and Technology at CQUniversity Sydney. Amara Atif is a senior lecturer in the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney.
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