- By Assoc. Prof. Dr. Eleni Meletiadou, Guildhall School of Business and Law, London Metropolitan University, PFHEA, NTF, UTF, MCIPD, MIIE.
As higher education embraces artificial intelligence (AI) to drive digital transformation, there is a growing risk that older, non-traditional, or mature students will be left behind. This blog post draws on insights from the QAA-funded “Using AI to promote education for sustainable development and widen access to digital skills” project I have been leading alongside findings from the EU COST Action DigiNet (WG5), where I co-lead research into media portrayals and digital inequalities impacting mature learning workers.
Through this work, and in collaboration with international partners, we have identified what genuinely supports inclusion and what simply pays lip service to it. While AI is often heralded as a tool for levelling the educational playing field, our research shows that without intentional support structures and inclusive design, it can reinforce and even widen existing disparities.
Supporting mature students’ AI literacy is, therefore, not just a pedagogical responsibility; it is an ethical imperative. It intersects with wider goals of equity, social justice, and sustainable digital inclusion. If higher education is to fulfil its mission in an age of intelligent technologies, it must ensure that no learner is left behind, especially those whose voices have long been marginalised.
Why Mature Students Matter in the AI Conversation
Mature students are one of the fastest-growing and most diverse populations in higher education. They bring a wealth of life and work experience, resilience, and motivation. Yet, they are often excluded from AI-related initiatives that presume a level of digital fluency not all possess. However, they are often left out of AI-related initiatives, which too frequently assume a baseline level of digital fluency that many do not possess. Media portrayals tend to depict older learners as technologically resistant or digitally inept, reinforcing deficit narratives that erode confidence, undermine self-efficacy, and reduce participation.
As a result, mature students face a dual barrier: the second-order digital divide—inequity in digital skills rather than access—and the social stigma of digital incompetence. Both obstruct their academic progress and diminish their employability in a rapidly evolving, AI-driven labour market.
Principles that Support Mature Learners
The QAA-funded project, developed in partnership with five universities across the UK and Europe, embedded AI literacy through three key principles—each critical for mature learners:
- Accessibility
Learning activities were designed for varying levels of digital experience. Resources were provided in multiple formats (text, video, audio), and sessions used plain language and culturally inclusive examples. Mature students often benefited from slower-paced, repeatable guidance and multilingual scaffolding.
- Collaboration
Peer mentoring was a powerful tool for mature students, who often expressed apprehension toward younger, digitally native peers. By fostering intergenerational support networks and collaborative projects, we helped reduce isolation and build mutual respect.
- Personalised Learning
Mature students frequently cited the need for AI integration that respected their goals, schedules, and learning styles. Our approach allowed learners to set their own pace, choose relevant tools, and receive tailored feedback, building ownership and confidence in their digital journeys.
Inclusive AI Strategies That Work – Based on What Mature Learners Told Us
Here are four practical strategies that emerged from our multi-site studies and international collaborations:
1. Start with Purpose: Show AI’s Relevance to Career and Life
Mature learners engage best when AI tools solve problems that matter to them. In our QAA project, students used ChatGPT to refine job applications, generate reflective statements, and translate workplace policies into plain English. These tools became career companions—not just academic add-ons.
‘When I saw what it could do for my CV, I felt I could finally compete again,’ shared a 58-year-old participant.
2. Design Age-Safe Learning Spaces
Many mature students fear embarrassment in digital settings. We created small, trust-based peer groups, offered print-friendly guides, and used asynchronous recordings to accommodate different learning paces. These scaffolds helped dismantle the shame often attached to asking for help.
3. Make Reflection Central to AI Literacy
AI use can be empowering or alienating. We asked students to record short video reflections on how AI shaped their thinking. This helped them develop critical awareness of what the tool does, how it aligns with academic integrity, and what learning still needs to happen beyond automation.
4. Use Media Critique to Break Stereotypes
Drawing on my research into late-life workers and digital media, we used ageist headlines, adverts, and memes as classroom material. Mature learners engaged critically with how society depicts them, transforming deficit narratives into dialogue, and boosting confidence through awareness.
How We Measured Impact (and Why It Mattered)
We evaluated these strategies using mixed methods informed by both academic and lived-experience perspectives:
- Self-reflective journals and confidence scales tracked growth in AI confidence and self-efficacy
- Survey data from mature students (aged 55+) in the UK and Albania (from my older learners study) revealed the key role of peer support, professional experience, and family encouragement in shaping digital resilience
- Narrative mapping, developed with COST DigiNet partners, was used to document shifts in learners’ digital identity—from anxious adopter to confident contributor
- Follow-up interviews three months post-intervention showed sustained engagement with AI tools in personal and professional contexts (e.g., CPD portfolios, policy briefs)
Policy and Practice: Repositioning Mature Learners in AI Strategy
As highlighted in our Tirana Policy Workshop (2024), national and institutional policy often fails to differentiate between age-based needs when deploying AI in education. Mature students frequently face a “second-order digital divide,” not just in access, but in relevance, scaffolding, and self-belief.
If UK higher education is serious about digital equity, it must:
- Recognise mature learners as a distinct group in AI strategy and training
- Fund co-designed AI literacy programmes that reflect lived experience
- Embed inclusive, intergenerational pedagogy in curriculum development
- Disrupt media and policy narratives that equate older age with technological incompetence
Conclusion: Inclusion in AI Isn’t Optional – It’s Foundational
Mature learners are not a marginal group to be retrofitted into digital learning. They are core to what a sustainable, equitable, and ethical higher education system should look like in an AI-driven future. Designing for them is not just good inclusion practice—it’s sound educational leadership. If we want AI to serve all learners, we must design with all learners in mind, from the very start.