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Teaching students to use AI: from digital competence to a learning outcome

by Concepción González García and Nina Pallarés Cerdà

Debates about generative AI in higher education often start from the same assumption: students need a certain level of digital competence before they can use AI productively. Those who already know how to search, filter and evaluate online information are seen as the ones most likely to benefit from tools such as ChatGPT, while others risk being left further behind.

Recent studies reinforce this view. Students with stronger digital skills in areas like problem‑solving and digital ethics tend to use generative AI more frequently (Caner‑Yıldırım, 2025). In parallel, work using frameworks such as DigComp has mostly focused on measuring gaps in students’ digital skills – often showing that perceived “digital natives” are less uniformly proficient than we might think (Lucas et al, 2022). What we know much less about is the reverse relationship: can carefully designed uses of AI actually develop students’ digital competences – and for whom?

In a recent article, we addressed this question empirically by analysing the impact of a generative AI intervention on university students’ digital competences (García & Pallarés, 2026). Students’ skills were assessed using the European DigComp 2.2 framework (Vuorikari et al, 2022).

Moving beyond static measures of digital competence

Research on students’ digital competences in higher education has expanded rapidly over the past decade. Yet much of this work still treats digital competence as a stable attribute that students bring with them into university, rather than as a dynamic and educable capability that can be shaped through instructional design. The consequence is a field dominated by one-off assessments, surveys and diagnostic tools that map students’ existing skills but tell us little about how those skills develop.

This predominant focus on measurement rather than development has produced a conceptual blind spot: we know far more about how digital competences predict students’ use of emerging technologies than about how educational uses of these technologies might enhance those competences in the first place.

Recent studies reinforce this asymmetry. Students with higher levels of digital competence are more likely to engage with generative AI tools and to display positive attitudes towards their use (Moravec et al, 2024; Saklaki & Gardikiotis, 2024). In this ‘competence-first’ model, digital competence appears as a precondition for productive engagement with AI. Yet this framing obscures a crucial pedagogical question: might AI, when intentionally embedded in learning activities, actually support the growth of the very competences it is presumed to require?

A second limitation compounds this problem: the absence of a standardised framework for analysing and comparing the effects of AI-based interventions on digital competence development. Although DigComp is widely used for diagnostic purposes, few studies employ it systematically to evaluate learning gains or to map changes across specific competence areas. As a result, evidence from different interventions remains fragmented, making it difficult to identify which aspects of digital competence are most responsive to AI-mediated learning.

There is, nevertheless, emerging evidence that AI can do more than simply ‘consume’ digital competence. Studies by Dalgıç et al (2024) and Naamati-Schneider & Alt (2024) suggest that integrating tools such as ChatGPT into structured learning tasks can stimulate information search, analytical reasoning and critical evaluation—provided that students are guided to question and verify AI outputs rather than accept them uncritically. Yet these contributions remain exploratory. We still lack experimental or quasi-experimental evidence that links AI-based instructional designs to measurable improvements in specific DigComp areas, and we know little about whether such benefits accrue equally to all students or disproportionately to those who already possess stronger digital skills.

This gap matters. If digital competences are conceived as malleable rather than fixed, then AI is not merely a technology that demands certain skills but a pedagogical tool through which those skills can be cultivated. This reframing shifts the centre of the debate: away from asking whether students are ready for AI, and towards asking whether our teaching practices are ready to use AI in ways that promote competence development and reduce inequalities in learning.

Our study: teaching students to work with AI, not around it

We designed a randomised controlled trial with 169 undergraduate students enrolled in a Microeconomics course. Students were allocated by class group to either a treatment or a control condition. All students followed the same curriculum and completed the same online quizzes through the institutional virtual campus.

The crucial difference lay in how generative AI was integrated:

  • In the treatment condition, students received an initial workshop on using large language models strategically. They practised:
  • contextualising questions
  • breaking problems into steps
  • iteratively refining prompts
  • and checking their own solutions before turning to the AI.
  • Throughout the course, their online self-assessments included adaptive feedback: instead of simply marking answers as right or wrong, the system offered hints, step-by-step prompts and suggestions on how to use AI tools as a thinking partner.
  • In the control condition, students completed the same quizzes with standard right/wrong feedback, and no training or guidance on AI.

Importantly, the intervention did not encourage students to outsource solutions to AI. Rather, it framed AI as an interactive study partner to support self-explanation, comparison of strategies and self-regulation in problem solving.

We administered pre- and post-course questionnaires aligned with DigComp 2.2, focusing on five competences: information and data literacy, communication and collaboration, safety, and two aspects of problem solving (functional use of digital tools and metacognitive self-regulation). Using a difference-in-differences model with individual fixed effects, we estimated how the probability of reporting the highest level of each competence changed over time for the treatment group relative to the control group.

What changed when AI was taught and used in this way?

At the overall sample level, we found statistically significant improvements in three areas:

  • Information and data literacy – students in the AI-training condition were around 15 percentage points more likely to report the highest level of competence in identifying information needs and carrying out effective digital searches.
  • Problem solving – functional dimension – the probability of reporting the top level in using digital tools (including AI) to solve tasks increased by about 24 percentage points.
  • Problem solving – metacognitive dimension – a similar 24-point gain emerged for recognising what aspects of one’s digital competences need to be updated or improved.

In other words, the AI-integrated teaching design was associated not only with better use of digital tools, but also with stronger awareness of digital strengths and weaknesses – a key ingredient of autonomous learning. Communication and safety competences also showed positive but smaller and more uncertain effects. Here, the pattern becomes clearer when we look at who benefited most.

A compensatory effect: AI as a potential leveller, not just an amplifier

When we distinguished students by their initial level of digital competence, a pattern emerged. For those starting below the median, the intervention produced large and significant gains in all five competences, with improvements between 18 and 38 percentage points depending on the area. For students starting above the median, effects were smaller and, in some cases, non-significant.

This suggests a compensatory effect: students who began the course with weaker digital competences benefited the most from the AI-based teaching design. Rather than widening the digital gap, guided use of AI acted as a levelling mechanism, bringing lower-competence students closer to their more digitally confident peers.

Conceptually, this challenges an implicit assumption in much of the literature – namely, that generative AI will primarily enhance the learning of already advantaged students, because they are the ones with the skills and confidence to exploit it. Our findings show that, when AI is embedded within intentional pedagogy, explicit training and structured feedback, the opposite can happen: those who started with fewer resources can gain the most.

From ‘allow or ban’ to ‘how do we teach with AI?’

For higher education policy and practice, the implications are twofold.

First, we need to stop thinking of digital competence purely as a prerequisite for using AI. Under the right design conditions, AI can be a pedagogical resource to build those competences, especially in information literacy, problem solving and metacognitive self-regulation. That means integrating AI into curricula not as an add-on, but as part of how we teach students to plan, monitor and evaluate their learning.

Second, our results suggest that universities concerned with equity and digital inclusion should focus less on whether students have access to AI tools (many already do) and more on who receives support to learn how to use them well. Providing structured opportunities to practise prompting, to critique AI outputs and to reflect on one’s own digital skills may be particularly valuable for students who enter university with lower levels of digital confidence.

This does not resolve all the ethical and practical concerns around generative AI – far from it. But it shifts the conversation. Instead of treating AI as an external threat to academic integrity that must be tightly controlled, we can start to ask:

  • How can we design tasks where the added value lies in asking good questions, justifying decisions and evaluating evidence, rather than in producing a single ‘correct’ answer?
  • How can we support students to see AI not as a shortcut to avoid thinking, but as a tool to think better and know themselves better as learners?
  • Under what conditions does AI genuinely help to close digital competence gaps, and when might it risk opening new ones?

Answering these questions will require further longitudinal and multi-institutional research, including replication studies and objective performance measures alongside self-reports. Yet the evidence we present offers a cautiously optimistic message: teaching students how to use AI can be part of a strategy to strengthen digital competences and reduce inequalities in higher education, rather than merely another driver of stratification.

Concepción González García is Assistant Professor of Economics at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain, and holds a PhD in Economics from the University of Alicante. Her research interests include macroeconomics, particularly fiscal policy, and education.

Nina Pallarés is Assistant Professor of Economics and Academic Coordinator of the Master’s in Management of Sports Entities at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain. Her research focuses on applied econometrics, with particular emphasis on health, labour, education, and family economics.


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How do we teach international students in the UK?

by Sylvie Lomer and Jenna Mittelmeier

  • Sylvie Lomer
  • Jenna Mittelmeier

This has been the guiding question for our current SRHE-funded research project. We are looking at how pedagogies and practices have been developed or shaped within the context of changing student demographics across the UK higher education sector. We have conducted 40 out of the 50 planned interviews and have really appreciated academics’ time and enthusiasm during a completely unprecedented semester. Our data collection and analysis continue but we wanted to communicate early findings and the types of language used by participants to communicate their pedagogy.

Many of our participants taught predominantly, or talked mainly about, postgraduate teaching, where students’ professional or life experience was frequently highlighted as important. The limitation with our participant sampling so far is an overrepresentation of applied disciplines (education, business, health-related, etc) and an underrepresentation of ‘pure’ disciplines (physics, maths, philosophy, etc) (Biglan, 1973). It’s quite possible that this represents a teaching approach that’s dominant in certain disciplines and not others.

Teaching approaches

Most participants represented their teaching in strikingly similar ways. Through careful reflection on the key information that needs to be ‘delivered or conveyed’, lecturers sought to maximise the amount of class time spent on ‘real learning’, which was understood to happen primarily in social or group settings. There appears to be consensus across the disciplines, institutions, and geographic locations of participants that an active and social approach to learning is optimal.

We anticipated variation across disciplines and contexts in the pedagogical approaches adopted by lecturers working with international students, but most participants have described largely similar approaches to managing their physical classrooms in pre-COVID times. These are commonly characterised by:

  • Chunking talking time and lectures into ‘gobbets’ of 15-20 minutes
  • Following up with small group activities (eg discussions or concrete tasks)
  • Concluding with plenary or whole group feedback

Sometimes this pattern was repeated during longer teaching sessions. Pedagogies were also mediated in different ways: through technology; with the help of teaching assistants; or in collaboration with a range of campus services. Yet, the core of how most participants represented their teaching has shown striking similarity, with reflection on the importance of social or group settings.

Participants reported challenges in implementing their approaches, particularly given that massification and growing class sizes have largely coincided with international student recruitment. Infrastructure, such as lecture theatres with fixed seating, was also commonly criticized as a limitation to pedagogy. Adaptations to online or hybrid classrooms during Covid-19 included ‘flipped’ approaches where readings or recordings were available initially online, with ‘live’ sessions designed to be solely interactive.

Representations of international students

We explored how the presence of international students influences the micro and macro practices of lecturer; in that respect, how we define ‘international students’ has been a prominent angle of questioning. Most participants defaulted to using the term as adopted in the press and public policy – non-EU degree level students. However, they also highlighted other groups of students who may also be subsumed by the international label – EU students, short-term students on exchanges or top-up programmes, and students classified as British by residency but who have been primarily educated overseas. These nuances matter, because, as participants highlight, the key point is not what students’ nationality is, but what their previous educational experiences are.

Challenges around ‘cultures of deference’ to the authority of teachers and texts were highlighted, as well as individual confidence and skills to participate orally in discussions. While some participants referred to common stereotypes of, for example, ‘silent’ Chinese students, others were quick to challenge deficit-based assumptions. The latter tended to describe the perceived benefits of having international students across cohorts and unpack the diversity of experiences that underlie such stereotyping. Diversity, in this regard, was often described as a ‘learning resource’ (Harrison, 2018), whereby international students were assumed to support classroom learning environments by sharing knowledge and experiences from their country or culture.

An alternative consideration noted by a smaller number of participants is that students should not be seen as embodiments of some abstracted form of national culture (Lomer, 2017), but rather through recognising that people are different and know different things. Some participants criticised the  binary distinction – created by fee and visa restrictions – between ‘home’ and ‘overseas’ students, given that factors which affect learning are more likely to be a culmination of previous educational experience, language, and confidence – of which none fall neatly between political borders. In that regard, participants highlighted the importance of ‘good teaching’ and a desire to develop an inclusive ‘ethos’ which works for all students.

We asked participants what they feel makes a good teacher, and were surprised to see relatively similar responses between participants, regardless of their career stage or teaching contexts. Their responses emphasised empathy, reflexivity, humility, curiosity, disciplinary passion, and the capacity to value difference. However, there was less reflection about how key learning outcomes might be underpinned by Eurocentric assumptions about education or students’ behaviours, or how things like critical thinking or academic integrity may be culturally shaped.

Reflections on professional identity

A final consideration for this project is how lecturers’ professional identities are shaped by their work with international students. Participants reflected on the loneliness of being ‘the pedagogy person’ or ‘the internationalisation person’ in departments or schools. In such contexts, some told stories about past and current colleagues or other academics in their networks who voiced explicitly racist views about international students. Most suggested these were now outliers and that the dominant discourse has changed towards a more positive view of international students.

Language used by academics when communicating the implementation of active and social learning approaches with international students positions the academic as in control and the (international) student as subaltern. For example, many participants spoke in terms of ‘being strict’, ‘setting expectations’, ‘forcing them to speak’. This was often explained with reference to meeting key learning outcomes or developing professional skills, but sits in contrast with the more emancipatory discourses often associated with student-centred approaches to teaching.

Earlier career academics have only ever taught in a highly internationalised sector, while those with a longer professional experience reflected on the change they had seen during their career. For most, internationalisation was reflected as a fact of contemporary academic life; some commented that they hadn’t thought about the particularities of teaching international students before their interview with us. For some, this was a characteristic of the discipline, particularly those in areas like business and international development; they positioned their subjects as inherently international, with assumptions that internationalised teaching followed ‘naturally’.

Get involved

The responses so far have been encouraging and suggest that, across UK institutions, academics are dedicated to: developing pedagogies that value diversity on multiple axes; working with international students; and valuing the knowledge and perspectives that an international student group can co-create.

We are still collecting data and would love to hear from anyone who teaches international students in any UK HEI, but particularly if you:

  • Teach in a STEM or Arts subject
  • Teach in Wales or Northern Ireland
  • Disagree with or don’t recognise the account above or have a different viewpoint.

All responses are strictly confidential, although participants will be invited to participate in a webinar at the end of the project.

We are working on building up a repository of case studies about teaching innovations with international students, hosted here, and welcome submissions from all (even if you do not wish to participate in an interview). Contact sylvie.lomer@manchester.ac.uk or jenna.mittelmeier@manchester.ac.uk for more information.

SRHE member Sylvie Lomer is Lecturer in Policy and Practice at the University of Manchester, in the Manchester Institute of Education (MIE). Her previous research focused on policies on international students in the UK, and now focuses more broadly on internationalisation in policy and practice in higher education, with a critical approach to pedogogy and policy enactment.

SRHE member Jenna Mittelmeier is Lecturer in International Education at the University of Manchester, in the Manchester Institute of Education (MIE). Her research expertise focus broadly on the internationalisation of higher education,  taking a critical perspective on issues of power, privilege, and ethics in international higher education.

Our thanks to Parise Carmichael-Murphy for reviewing the blog before it was submitted.

References

Biglan, Anthony (1973) ‘The characteristics of subject matter in different academic areas’, Journal of Applied Psychology 57(3): 195

Harrison, N (2015) ‘Practice, problems and power in ‘internationalisation at home’: Critical reflections on recent research evidence’, Teaching in Higher Education, 20(4), 412-430

Ian Kinchin


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Pedagogic paupers: where’s the distinctiveness?

By Ian Kinchin

When you scan a range of university web sites, they all seem to claim their institution offers a distinctive student experience. In many respects, this may be true. However, when it comes to pedagogy, I wonder if there has been a trend towards homogenisation rather than distinctiveness?

Pressures of work and the emphasis on research outputs appears to drive many academics to “play it safe” when it comes to classroom practice. The irony is that these same academics would claim to be serious researchers – people to reflect, innovate, question and experiment. And yet these ‘researcher traits’ don’t seem to be carried over into teaching. The ‘it’ll do’ sentiment of the unenthusiastic amateur seems all too common, though (I would hasten to add) not universal.

I would not for one moment claim that the pressures on university academics are not real, the squeeze on resources and the restrictions posed by accrediting bodies (for example) all appear to drive academics towards a conservative approach to teaching. The old adage, “if it ain’t broke, don’t fix it” seems to be the underpinning philosophy for many.

However, my own undergraduate friends and relatives often provide me with stories and anecdotes that suggest that, if not broken, much university teaching still requires something of an upgrade. Continue reading →