Can you imagine the first job interview of a graduate from your university? If I picture a computing graduate, they should be able to write clean code, explain algorithms and debug under pressure. The interviewer would ask them to walk through their professional experience, including their online presence, a project they have taken up and scenarios on how they approach working alongside AI tools responsibly.
But if I’m being realistic, how many of your graduates will end up being shortlisted for interviews at companies for job roles their degree is supposed to lead them to?
The gap nobody wants to own
With youth unemployment in the UK at 16.2 per cent, and the number of young people not in education, employment or training (NEET) exceeding 1 million for the first time since 2013, fresh graduates are entering the tightest job market in years, competing for entry-level roles with experienced professionals made redundant by restructuring and AI-driven automation.
- Instead of producing finished graduates, aim to build adaptable people
- Equipping university graduates to think beyond the classroom
- Machine v human skills: what do we teach now?
While the post-16 skills white paper projects that 26 million workers will need upskilling within a decade, it says remarkably little about how university curricula should change to deliver this. Even though universities respond with careers fairs, industry talks and employability workshops, a common problem is that those who show up for these initiatives already have confidence and at least some connections, and one cannot build professional skills in students who hardly show up.
Beyond technical skills
If you ask any degree programme or course leader whether their graduates are employable, the answer will almost always be “yes”. However, if you ask their graduates six months into the job market or post-graduation, you often hear a different story.
The problem is not that most of these degrees lack rigour or applied knowledge and skill integration. For instance, in computing degrees (for which I can speak for from my domain), students do get to write code, build systems, face technical vivas and complete group projects, where they engage in technically challenging problem solving.
Nearly all of these aim to equip future graduates with technical competencies, and what falls through the gap are the non-technical skills graduates need to enter their professional life, to be able to present their work confidently, market themselves credibly and contextualise their technical decisions responsibly.
Particularly in the tech industry, this gap has widened. Graduates entering computing roles are now expected to demonstrate AI literacy, apply professional codes of conduct (such as those set out by the British Computer Society) and exercise judgement about the social implications of the systems they build. These are not optional extras – they are baseline professional expectations. The CBI’s AI Skills Report (2025) adds a harder edge, where AI is hollowing out the routine entry-level tasks that once instilled professional judgement on the job. The informal safety net that previously compensated for what the degrees did not formally teach is disappearing.
Rethinking what we assess
When we talk about the employability gap, we tend to talk about what events, schemes and services exist alongside the degree to address these. A more important question could be what the curriculum itself requires students to produce.
What you assess is what students would train themselves in. If a computing degree never formally asks students to pitch themselves for a role, publish professional content, build digital presence or reflect on their working style and professional direction, most students will not do these things. The degree has not signalled to them that these things matter.
Take a student I will call Jake – technically sharp, enthusiastic in seminars, but when asked to record an elevator pitch for a job posting in an assessment task, he froze. He had never been asked to articulate his own professional values before. He had spent three years learning to build things, not learning how to talk about them to a potential client or to play the role of a consultant. Jake is not unusual – he is the norm.
Employability provision at the margins, through curated workshops, career fairs, tech talks, tend to attract students who are already motivated and committed to build connections. Those who most need professional scaffolding rarely walk through these doors voluntarily.
Putting it into practice with a module redesign
At my university, I redesigned a first-year computing module, “Foundations of professional computing”, to embed employability directly into assessed, credit-bearing work across 12 structured weeks.
The module is built around two assessed halves. In the first half, students focus on analysing a live technology trend case scenario in the field. Rather than submitting a standard report and presentation, they produce a group video podcast and a collaborative LinkedIn article with their case analysis. Guest sessions from industry practitioners on emerging trends in areas such as AI, cloud computing and digital health feed directly into these assessed tasks, not as standalone inspiration talks but as contextualised professional exposure and real-world problem solving that can offer students insights. British Computer Society ethics and responsible computing are embedded throughout, not tagged on at the end as a compliance exercise.
In the second half of the module, we shift the focus to professional identity in the industry. Rather than a traditional CV, cover letter and report, students build an employability portfolio, which includes a live LinkedIn profile, GitHub presence and an elevator pitch video targeting a real job posting. They then write their reflections, covering personal development planning, professional responsibility and sector principles.
Weekly sessions introduce a “tool of the week”, ranging from productivity frameworks (eg Trello and Notion) to self-awareness tools (eg MBTI, Ikigai and Self-SWOT), video production tools (eg CapCut) and AI-driven interview practice through Shortlist.Me.
The goal is not to teach students about employability, but rather to require them to do what employed people do and to do it as part of the degree, not alongside it.
Early indicators, including student performance and engagement with learning activities, are encouraging, and I am developing richer instruments to assess engagement and graduate outcomes. But the design rationale is grounded in well-established learning design principles, where competency is built through doing and authentic assessment signals to students what the degree is genuinely preparing them for.
Going forward, questions worth asking
I am not suggesting that every degree programme replicate this module. But I would invite programme leaders to consider a few questions.
Could standard assessments be replaced with authentic professional outputs that mirror what graduates will actually do in industry? Perhaps a podcast rather than a report, a LinkedIn profile over a cover letter, an AI-practice pitch over a viva, or a time-boxed code sprint modelled on real industry workflows rather than an exam taken under controlled conditions? And would that shift how students understand what their degree is preparing them for?
Could industry guest sessions be tied directly to assessed tasks rather than running alongside the curriculum as standalone events? And would students engage with them differently if they fed into something that counted?
Could self-knowledge frameworks such as the ones mentioned above sit alongside professional digital tools such as GitHub and AI-feedback tools, giving students both the self-awareness to identify their professional direction and the fluency to pursue it?
And finally, does your curriculum, taken as a whole, produce graduates who are technically sharp, professionally articulate and ethically grounded? If not, where would you start?
Chathura Sooriya-Arachchi is a lecturer in computer science at the University of Westminster.
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