Creative Futures Innovation Toolkit

This week I led a session for the Manchester Metropolitan University Digital Futures Community of Practice on the theme of digital skills. I wanted to share some of what came out of it, because the conversation was realy rich and I think it surfaces some things the sector needs to sit with.

I started with a provocation. Most of our students last had formal IT teaching in Year 9. Think about that. By the time they arrive at university, they’ve spent years immersed in technology, but almost none of it has been purposeful, professional, or deliberately developmental. They’re fluent on TikTok. They’re lost in a spreadsheet.

This isn’t a pipeline problem alone. I carried out a small practitioner inquiry with eighteen apprenticeship students before a video pitch assessment. Working professionals. Already in employment. Average confidence with digital tools: two out of five. One student wrote: “worried that having a video might make me stutter and get nervous and worry and cost me a grade.” These aren’t school leavers finding their feet. The gap persists into professional life because nobody ever addressed it directly.

The digital natives myth has been comprehensively dismantled by researchers. Kirschner and De Bruyckere argued that students may be equipped with technological skills but are not always digitally literate in using technology to support their learning. Prensky, who coined the term, eventually moved on from it himself. But a lot of institutional practice still hasn’t caught up.

What I’ve been trying to do in my own teaching is close the gap between exposure and capability through deliberate design. The portfolio module at levels four and five builds professional digital identity through RISE, IBM Skills Build, LinkedIn Learning, and the JISC discovery tool. Recruitment simulations ask students to deploy subject knowledge, digital capability, and professional judgement simultaneously. Adobe Express appears repeatedly across the programme because familiarity reduces cognitive load and leaves space for the actual thinking.

The bigger argument is about programme-level design. Biggs was right, students learn what they’re assessed on. If digital capability doesn’t appear in the learning outcomes, it stays invisible. It becomes something individual enthusiastic lecturers do until they leave or the module changes. Constructive alignment makes it structural rather than personal.

Then there’s AI. I didn’t want to spend too long here because this is the theme of the next session, but the connection to digital skills is too important to ignore. We used to cringe when students cited Wikipedia uncritically. Not because Wikipedia is useless, but because citing it without judgement showed that the student hadn’t developed the capacity to evaluate a source. AI is the same problem at a completely different scale.

My research, just accepted for publication in the Journal of Learning Development in Higher Education, found that where AI appeared in university assessment policies at all, it appeared in supplementary guidance rather than core frameworks, and was framed around misconduct detection rather than pedagogical possibility. Institutions are training students and staff to see AI as a threat to manage rather than a tool to interrogate. That’s the wrong frame.

The expert interrogates. The novice accepts. A student who has built disciplinary knowledge and critical thinking can sit down with an AI tool and do something genuinely useful with it. They can spot the hallucination. They can identify what’s missing. They can push back. A student without that grounding will use AI to fill the gap, and they won’t know the gap is there.

I used two examples in the session that seemed to land. The first was equity and diversity. Ask AI to write a job description and it will produce something that looks professional and complete. Ask a student who has studied structural inequality and understands what the Equality Act actually requires, and they’ll immediately see what the AI got wrong. The second was reflection. Students who have had genuinely formative experiences on placement or in simulation hand that experience to an AI and ask it to write their reflection for them. The AI produces something coherent and hollow. The student had the raw material for genuine insight. They just didn’t trust themselves to articulate it.

Digital skills matter enormously to our students. But digital tools should accelerate learning, not replace it. The messy moment when a student doesn’t know how to structure their argument, that’s not a problem to be solved by AI. That’s the moment learning happens.

The session used a How Might We approach from IDEO and the Stanford d.school, and the responses from the room were revealing. A few that stayed with me: “frame skill development around the things you do rather than the tool you use, so data analysis instead of Excel.” “Develop an autonomous digital mindset rather than a digital skills set.” “Teach principles, not tools, wherever possible.” “The only constant in future workplaces is change.”

Rod Cullen made a point that’s worth repeating. His old boss Jim Petch used to say we’ll know we’ve cracked a problem when we stop talking about it as much. We’re not there yet with digital skills. But sessions like this one suggest we’re at least asking better questions.

Our students arrive fluent. The job is to make them conceptualised, powerful, and transformed. Not just TikTok fluent.

You can catch the recording here: