Professor Xue Zhou and Dr Qianqian Chai discuss the importance of developing students’ sense of urgency in emerging AI capabilities
Across global higher education, artificial intelligence is accelerating workplace transformation. Employers and policy bodies highlight a widening gap between the skills graduates develop and those needed in an AI-driven economy. Universities are responding through curriculum redesign, guidance, staff development, and discipline-specific initiatives.
Yet one critical question remains:
Do students genuinely recognise the urgency?
Many educators report a paradox: institutions are accelerating AI literacy strategies, yet student engagement often does not match the pace or ambition of institutional change. Here, “urgency” means that students recognise that AI literacy matters now and prioritise practice rather than postponing it.
Where opportunities are optional, participation can be very limited. In one evaluated GenAI workshop series, just 171 students attended, representing under 1% of the cohort. Similar patterns have been observed across institutions offering drop‑in support or elective AI skills sessions. Engagement is also uneven across disciplines: STEM students are more likely to view AI as directly relevant, while students in arts, humanities, and social sciences are often less engaged.
Even in taught sessions where AI literacy is embedded, students may seek rapid outputs rather than engage in processes that build judgement, verification, and reflective practice. In short, despite institutional acceleration, urgency is not yet consistent or widespread.
Why isn’t urgency taking hold?
1. Changing patterns of engagement
In the UK, declining attendance and shifting student expectations around contact time make consistent exposure to new ideas harder to sustain. With financial pressure rising, 68% of students in 2025 reported paid employment, up 12 percentage points from the previous year, causing variable attendance. Consequently, educators design AI literacy to be built through iterative practice, but irregular attendance disrupts the cycle, so effort and uptake fail to meet.
2. Misconception about what AI literacy involves
When students only encounter AI as a tool for rapid task completion, literacy can appear deceptively simple. Framing GenAI as a neutral “tool” or “assistant” can disguise how strongly it shapes thinking, which makes it easier for students to reduce AI literacy to tool-use rather than judgement and verification. Process-focused teaching including verification, evaluation, and making reasoning visible can be experienced as friction because it slows output. This narrows AI literacy to “tool use,” reinforcing shortcuts and false confidence, and reducing the perceived need to develop deeper capability.
3. Invisible learning processes
This misconception is reinforced when students mainly use AI for low-level tasks that speed up completion. The DEC report reflects this pattern: students prioritise access to tools (66%) and clear guidelines (47%), yet only 31% report using AI as a learning companion. This limited use keeps expectations low and hides what more advanced, learning-oriented AI work involves, so students are less likely to see a need for deliberate skill development.
4. Psychological distance from workplace realities
Students are aware of AI‑driven workplace change, but its consequences often feel distant. Even when employers anticipate workforce reductions due to automation, students may view these implications as “future concerns” rather than immediate motivators. This temporal distancing makes urgency easy to postpone.
What universities are already doing
Institutions across the sector are taking thoughtful steps to address these gaps.
- Partnership and co-creation
Many universities are extending existing student partnership models into AI literacy. For example, Queen Mary Academy’s Learner Interns Programme enables students to collaborate with staff on education and employability projects, including AI in assessment, digital capability development, and student‑led resources. This co-creation helps turn AI literacy from a distant concept into a shared, student-owned priority, strengthening awareness and healthy urgency.
- Workshops, launch events, and cross-department collaborations
Universities are also using high‑visibility events to raise awareness. At the University of Leicester, the AI & Robotics Symposium brought together industry speakers, demonstrations, and hands‑on activities to help students experience AI beyond the metaphor of “a tool”. By making AI concrete and situated, these events turn curiosity into urgency, signalling that AI literacy is relevant now, rather than a future add-on.
These initiatives matter, but their reach still tends to be uneven. Partnership roles involve small groups of students; one‑off events can largely attract the already‑interested. Moving from pockets of innovation to widespread engagement requires more sustained and embedded approaches.
Moving towards healthy urgency: what needs to strengthen
If students are to develop urgency without feeling overwhelmed, universities need routine experiences that integrate AI literacy into everyday learning. The goal is constructive motivation, where students understand that developing AI capability is relevant now, not later. Key strategies include:
- Communicate AI trends clearly and regularlythrough short, consistent touchpoints linked to disciplines and employer expectations.
- Make AI visible in everyday teachingvia weekly activities, formative tasks, and assessment design so it is normalised and not an optional add-on.
- Provide explicit and consistent assessment guidanceto remove uncertainty as a barrier to action, so students can engage early, openly, and intentionally.
- Start early in the student journeyby introducing AI expectations in induction, first‑year modules, and foundational activitiesso expectations are set before habits form.
- Leverage peer mentoring and student champions so norms spread through a learning community and urgency feels shared, not imposed.
- Build a safe, exploratory learning culturewhere experimentation is supported and mistakes are treated as part of learning, so urgency does not turn into avoidance.
Conclusion: from passive awareness to constructive urgency
Urgency will not take root if students remain passive recipients of institutional messages. Instead, universities need approaches that are collaborative, routine, and grounded in authentic workplace realities. Building healthy urgency requires more than communicating risks, as it requires embedding practice, aligning messages across stakeholders, and creating conditions where students feel both supported and challenged.
A sense of urgency is healthy. Panic is not.
The goal is to cultivate confident, capable graduates who understand AI not as an optional skill, but as a core part of their learning and future working lives.
