Use the five stages of grief to guide academic staff through AI adoption

Academics must now operate, teach and conduct research in an AI-enabled world, whether they choose to adopt and use generative artificial intelligence (GenAI) or to resist it and discourage its use. For many colleagues, the rise of GenAI represents the loss of something familiar: the “good old days” of traditional assessment, the craft of academic writing and the slow development of independent thinking. There are also more existential fears regarding academic identity, professional value and whether aspects of an academic’s role might be automated. As a result, responses to GenAI have not been limited to technical or pedagogical aspects, but also encompass emotional ones.

To help academics navigate this transition, those who manage them can use Elisabeth Kübler-Ross’ five stages of grief: denial, anger, bargaining, depression and acceptance.

Stage 1: Denial

Denial is a defence mechanism that helps individuals soften the immediate impact of change, according to Kübler-Ross. In this stage, academics refuse to accept the reality of the situation, questioning the necessity and relevance of GenAI in higher education despite its growing popularity among students and society at large. 

What could help: start with low-stakes conversations within teaching teams or disciplinary/academic groups that focus on how students are using GenAI for tasks such as idea generation and article summarisation, rather than on enforcement or compliance. This might include sharing examples from comparable subjects where teachers have adjusted assessment briefs, added reflective components or incorporated guided GenAI exercises into formative assessments. The aim here is to spark curiosity. 

Stage 2: Anger

Frustration and anger come when denial becomes difficult to sustain. People often direct these emotions toward others (eg, in this case, AI companies), themselves or the situation, argues Kübler-Ross. Staff may feel threatened by GenAI, expressing fears about job security and a loss of academic authority and identity. Colleagues may feel frustrated by assessment vulnerability and the slow pace of policy change.

What could help: acknowledge emotions as legitimate rather than defensive. Create spaces where concerns can be aired openly and taken seriously, especially around assessment integrity and workload. For example, organising assessment redesign challenges or competitions allows colleagues to voice frustrations openly and channel them into concrete changes.

• Spotlight guide: Bringing GenAI into the university classroom
• Spotlight guide: AI and assessment in higher education
• AI and assessment redesign: a four-step process

Stage 3: Bargaining

At this stage, we seek to mitigate the loss and reach a compromise. Efforts may include integrating GenAI tools into current teaching practices rather than rethinking curricula. This can lead to progress, but perhaps we need bolder actions.

What could help: support experimentation while discussing and acknowledging the limitations of GenAI. This could take the form of facilitated staff meetings or informal education coffees and chats where colleagues can voice specific concerns about assessment integrity, workload or student use of GenAI, with these issues explicitly acknowledged and documented. Encourage shared learning across academic teams and departments. This is a good moment to co-design principles.

Stage 4: Depression

This stage is characterised by a sense of loss brought about by the erosion of what we may view as a traditional education system and the evolving role of an educator. Support initiatives may have emerged in some universities, but they are often ad hoc and driven by individuals rather than institutional strategy. 

What could help: peer support matters here. Small communities of practice, scheduled time for training and upskilling and visible leadership support can make a big difference. Aim for progress, not perfection.

Stage 5: Acceptance

In this stage, we come to terms with the situation and find ways to move forward. In many cases, this may be a quieter recognition rather than a confident embrace. For many colleagues, this acceptance may be more a realisation about how difficult genuine transformation is.

What could help: shift the focus from short-term compliance to long-term adaptation. This includes investing in staff development and upskilling, redesigning curricula with AI considerations and developing shared policies for responsible use. Acceptance is not the end of the journey, but the point at which more thoughtful, ethical and strategic engagement becomes possible.

Our journeys of adapting to GenAI may not be linear; we may revisit previous stages or skip a stage altogether. But this framework highlights the need for proactive engagement, collaboration and supportive leadership. Ultimately, acceptance must shift toward long-term adaptation, innovation and the preparation of students for an AI-enabled future.

Michael Mehmet is an associate professor in marketing at the University of Wollongong School of Business, Australia. Rushana Khusainova is a senior lecturer in marketing at the University of Bristol Business School, United Kingdom.