In higher education, we are increasingly being asked to embed employability and tackle GenAI into the curriculum, often as if they are separate challenges. Our experience suggests that bringing them together can make both more workable in practice. Used carefully, GenAI tools can support students to engage with employability in more interactive and inclusive ways, bringing more students in, while also developing the practical and critical awareness they need to use those tools well.
Why employability can be hard to engage with
Meaningful employability learning asks students to do something quite exposing, to talk about themselves: their strengths, their gaps, their choices, and their futures. That can feel more vulnerable than, say, discussing a case study or a theoretical framework.
To add to the discomfort, students do not always find it easy to recognise their own skills. They may realise that communication, teamwork or leadership matter, while still struggling to see how they may have already developed those skills or what routes they could take to do so in the future.
A challenge we need to work through, then, is that students may feel awkward about presenting their professional selves to others, especially if they are still working that out for themselves.
Why inclusion matters here
This is also an inclusion issue. We cannot assume that all students arrive knowing how to articulate their skills, reflect on their development, or use AI tools critically and purposefully. To make the development of employability (and GenAI) skills equitable, opportunities need to be embedded in the curriculum with enough flexibility and choice for students to engage in ways that work for them.
This is where guided activities with AI tools can help. In line with Universal Design for Learning, they can support students with multiple means to engage, respond, and work through uncertainty, including through text or voice, working at their own pace, asking for help, working multimodally, and more. This can reduce barriers to entry, give students more choice in how they participate, and offer them a safe space to experiment before they need to present themselves more publicly.
An example: a GenAI-augmented skills audit
An example of activity that worked particularly well for us was a GenAI-augmented skills audit.
On paper, a skills audit is a familiar task: Students evaluate themselves against a list of skills, perhaps with space to add their own, and identify evidence that supports each evaluation. It can be useful, but often involves extensive support as the lecturer tries to unstick people and coax them through to the next box.
One of us instead developed a guided prompt that students paste into Copilot or a similar tool. The prompt first asks students for information to help them evaluate skills relevant to them, only offering a more generic list if they struggle, which starts from a place of greater personalisation. It then guides them through the task, explaining in simple language, asking one question at a time, checking understanding, and offering short follow-up questions where evidence they give is unsupported or unclear, all while offering support and guidance where needed.
Rather than getting AI to do the task, the activity scaffolds interaction and help students do the thinking themselves, using tools that work for them, with guidance and support.
What changed in the room
In using this with students, the difference in the room was noticeable. Compared with the paper version, students were much more able to get started and keep going. Students found it useful and engaging, and some said it helped them think about their skills in new ways, especially as it challenged them to strengthen vague examples used to evidence the skills.
It similarly changed what the lecturer was able to do in the room. With the guided prompt, immediate support was built into the activity, freeing up space for richer conversations with students about what they were noticing, where they were struggling, and what seemed to be working for them.
Importantly, the activity also created a useful opening for discussing AI tools. For instance, one student reflected that they had only used prompts to ask for information, not as guides. A few offered critiques of using AI for the task. Another offered possible edits. Those reflections mattered, showing the activity was supporting a dual purpose, both helping students articulate their skills while also engaging in critical reflection on the use of those AI tools.
For us, that is where the two agendas meet: Employability learning becomes a context for developing AI literacy, and AI literacy development becomes embedded in that process rather than something left outside the room.
Beyond one task
The skills audit is only one example. We have used a “career tree” activity where students can use GenAI tools to help them visualise their interests, skills, experience, and qualifications as a tree that then becomes the starting point for discussion. We have also introduced GenAI tools to boost student confidence in professional conversations, a practice supported by recent research on AI-assisted interview simulation.
Across these activities, the common thread is to guide interaction that helps students externalise their thinking. This can then act as a springboard for further discussion and reflection, both about themselves and the AI tools they’ve used to help them get there.
Designing with care
Bringing GenAI tools into the employability toolkit is not a magic wand. GenAI can be overconfident (understatement of the year!), overly enthusiastic, and short on context, and neither employability skills nor GenAI literacy develop simply by having those tools in the room. Our experience suggests, however, that when GenAI activities are paired with critical reflection, they can help students engage with employability learning in ways that are more manageable, interactive, and inclusive, and it is that same reflective practice that can concurrently develop their AI literacy.
As we are being asked to embed both employability and GenAI, guided interactive activities may be one practical way to do both, while widening access for students who might otherwise find these areas hard to enter. What kinds of GenAI-supported employability activities are you using, or now considering, to make this work more inclusive and accessible for your students?
Dr Miri Firth PFHEA is Senior Lecturer and Academic Lead for Assessment at the University of Manchester, where she leads sector-wide innovation in employability education and flexible assessment. Her teaching practice is grounded in inclusive pedagogy, experiential learning, and research-led curriculum design, enabling students to develop confidence, creativity, and career-readiness.
Amy Aisha Brown is a Senior Lecturer in Academic Skills at the University of Portsmouth London, with interests in the development of academic and professional skills and the use of technologies, such as AI tools, in that work. Her work focuses on reducing barriers to student success through accessible, reflective, and critically informed approaches to teaching and learning.