SRHE Blog

The Society for Research into Higher Education

Tag Archives: industry


by Leave a comment

From data to policy: building an evidence-based future for skills, work, and learning in Latin America

by Sabur Butt, Hector G. Ceballos, and Michael Fung

Latin America stands at a familiar crossroads. Once again, a technological revolution is reshaping how work gets done, what skills employers value, and how quickly the workforce must adapt. And once again, the region risks being a generation behind.

During the Third Industrial Revolution of the 1970s–1990s, East Asia invested decisively in microelectronics, computing, and technical education. Latin America, consumed by debt crises and macroeconomic instability, missed the wave. The cost was measured not only in lost output but in institutional habits of looking backward while others prepared to look forward. Today, the Fourth Industrial Revolution is unfolding faster than its predecessor, and the region’s central deficit is not money or talent. It is foresight leading to informed interventions.

A structural mismatch between work and learning

Industries now change in cycles of months. University curricula change in cycles of years. That gap is no longer a minor inefficiency; it is the bottleneck that determines whether a country prepares its workforce in time or trails behind it.

The numbers are sobering. Across the six largest Latin American economies, between 19% and 21% of workers face a high probability of automation-related displacement. Almost half work in informal jobs. University dropout rates exceed 50% in several countries, climbing as high as 76% in the Dominican Republic. Students perform consistently below OECD averages in foundational literacy and numeracy. By the time graduates from the educational system enter the labour market, there is already a structural gap between what they can do and what employers want.

What the region needs is not another labour-market report. It needs skills intelligence, a continuous, forward-looking system that informs policymakers what is rising, what is fading, and where the workforce can realistically move next.

Why traditional forecasting falls short

Most forecasting in the region still relies on expert panels, occupational surveys, and static taxonomies that take years to update. These tools were designed for a slower-paced world.

To illustrate the issue, consider how such static taxonomies treat “database management” as a single skill, when the real labour market is moving between MySQL, PostgreSQL, MongoDB, Redis, Cassandra, and now vector databases, each with a different demand trajectory. Or consider the noise in a single automotive dataset, where the same assembly-line job appears as “ensamblador de carrocerías”, “operador de ensamble”, “ensamblador automotriz”, and “técnico en armado de vehículos”. Human annotators cannot keep up; even expert raters disagree on whether two skill descriptions refer to the same thing.

By the time a traditional taxonomy recognizes “prompt engineering” as a skill category, the labor market has already moved on to whatever comes next.

A data-driven alternative, already running

In our paper, we describe an operational deployment at the Institute for the Future of Education at Tecnológico de Monterrey. It is not a proposal; it is a working system (See Figure 1).

Figure 1. The framework links real-time labour market signals to an AI-assisted, expert-validated skills intelligence layer, which in turn informs curriculum design,reskilling pathways, workforce policy, and governance.

The approach combines large language models with retrieval-augmented generation to build dynamic, hierarchical skill taxonomies that update themselves as new job postings flow in. Each new skill is matched semantically against the existing taxonomy. Known skills are normalized to canonical terms. Genuinely new ones are flagged, classified, and added.

In Mexico’s automotive sector alone, the system has mapped more than 11,000 skill variations across 220 hypernym categories, identified 847 unique skills clustered into 12 occupational groups, and tracked the rise of electric-vehicle competencies in real time. Generative AI skills surfaced in the data months before they appeared in any official classification.

The infrastructure required is modest. A taxonomy covering 10,000 skills across major sectors can be maintained on standard cloud infrastructure for roughly $500–1,000 USD per month, within reach of education ministries in developing economies.

From data to policy

Technology alone does not change a system. The harder work is institutional. To shorten the lag between detecting a skill shift and updating a training program (currently 18 to 24 months) in most regions, curriculum committees must meet more often, must accept real-time data alongside surveys, and procurement procedures must allow timely equipment purchases according to the emerging skills.

Governance matters equally. Sustainable implementations bring together labour ministries, education ministries, economic development ministries, national statistics offices, industry associations, and universities. Each contributes something the others cannot: data access, curriculum authority, methodological rigor, domain expertise, and research capacity. No single actor owns skills or intelligence; the legitimacy of the system depends on shared ownership.

Localization also matters. Global taxonomies like ESCO and O*NET are useful starting points, but they need to incorporate regional terminology, indigenous skill categories, and sector-specific competencies. A skill system that does not speak the local language of work will not be trusted by the people meant to use it.

We also acknowledge real-world limitations. Many countries still hire through newspaper classifieds, physical noticeboards, and informal networks. Supplier tiers and small enterprises seldom advertise online. A system built solely on digital postings produces a geographically and structurally biased picture. Integrating offline data sources, replicating the approach across countries, and validating its predictions over time are the necessary next steps.

What success would look like

Executed well, skills intelligence reshapes reskilling itself. Instead of generic, fixed-duration programs, workers receive personalized pathways: a manufacturing technician’s quality-control experience mapped onto automated-systems monitoring; a mid-career professional alerted 12–24 months ahead of emerging demand; a young job-seeker pointed toward a stackable micro-credential that the market will actually reward.

For Latin America, this is more than a technical upgrade. It is a chance to break a historical pattern of arriving late to every industrial revolution and start arriving prepared. The region has the data, the talent, and increasingly the tools. What it has lacked is the institutional capacity to anticipate. That capacity is now within reach.

The Fourth Industrial Revolution will not wait. But for the first time, neither does the evidence.

Reference: Butt, S, Ceballos, HG, and Fung, M (2026) ‘From Data to Policy: Building an Evidence-Based Future for Skills, Work, and Learning in Latin America’ Policy Reviews in Higher Education 

Sabur Butt is a research professor at the Institute for the Future of Education, Tecnológico de Monterrey. His work focuses on artificial intelligence, natural language processing, and dynamic skills taxonomies, with a particular interest in how AI-driven labour-market intelligence can inform education and workforce policy across Latin America.

Hector G Ceballos is Director of the Living Lab & Data Hub of the Institute for the Future of Education (IFE) at Tecnológico de Monterrey. His research spans data science, knowledge engineering, and educational analytics, with a focus on building evidence-based systems that connect higher education to evolving industry and labour-market needs.

Michael Fung is Executive Director of the Institute for the Future of Education at Tecnológico de Monterrey. He was formerly the Deputy Chief Executive at SkillsFuture Singapore (SSG). He led the development of a comprehensive education and training ecosystem under the national SkillsFuture movement, which has become a global benchmark and reference for workforce skills development and lifelong learning across society.


by Leave a comment

The missing middle ground between research-led and practice-led education

by Saeed Talebi and Nick Morton

A peer reviewer recently challenged our pedagogical approach. We had described embedding an industry-led research project on Digital Twin development into our built environment curriculum as ‘research-informed teaching’. The reviewer disagreed: this was ‘practice-led rather than research-informed,’ they argued, because students weren’t producing research outputs themselves.

The comment revealed a conceptual confusion we suspect is widespread in higher education. We often assume that if students aren’t producing original research, then any industry-focused teaching must simply be vocational training with academic window-dressing. This leaves practice-facing disciplines in an awkward position: industry engagement is essential to what we do, but it risks being dismissed as less scholarly. There is, however, a middle ground.

Healey and Jenkins’ (2009) model offers a useful way through this confusion. They identify four modes of engaging undergraduates with research: research-led (learning about current scholarship), research-oriented (learning research methods), research-based (undertaking inquiry), and research-tutored (engaging in research discussions). These are mapped across two dimensions: whether students are positioned as audience or participants, and whether the emphasis falls on research content or processes. The model’s key insight is that students can be meaningfully engaged with research even when they aren’t producing research outputs themselves. The question isn’t simply whether students are ‘doing research’, it’s whether they’re positioned as passive recipients of established knowledge or as active participants in scholarly inquiry.

Practice-led teaching operates on different logic, though that logic has a closer relationship to applied research than is sometimes acknowledged. Its primary aim is developing professional competence through authentic engagement with messy problems and competing stakeholder priorities. The distinction isn’t whether industry is involved – it can be present in both approaches. The distinction lies in how students are positioned in relation to knowledge. In practice-led education, knowledge tends to be treated as relatively settled. In research-informed education, knowledge is contested, evolving, and open to question. An opportunity arises when these approaches coincide without conscious design, and a risk emerges when they collapse into one another. Research-informed teaching can become performative, referencing staff publications without changing how students learn. Practice-led teaching can slip into employability theatre, where live briefs are added without interrogating what knowledge students are actually developing.

As Professor Hanifa Shah OBE recently argued in Times Higher Education, STEAM education at its best equips students to “move fluidly between analytical and imaginative modes of thinking“, asking critical questions, considering ethical implications, and bringing meaning to innovation. This is precisely the disposition that research-informed teaching seeks to develop. In STEAM disciplines, including architecture, built environment, computing and engineering, emerging technologies create spaces where research and practice intersect meaningfully. Digital Twins and real-time monitoring tools, for example, allow students to work with live systems while engaging critically with the assumptions and ethics embedded within them. Students aren’t merely applying research after the fact, nor mimicking professional routines. They’re learning to question how data is generated, how models simplify reality, and how decisions are shaped by both evidence and judgement. Practice becomes a site of inquiry.

There’s an institutional dimension here too. Across the sector, promotion frameworks, workload models, and teaching quality metrics often reward research visibility and industry engagement without asking how either is translated pedagogically. Academics are encouraged to ‘bring research into teaching’ and ‘embed employability’, yet rarely supported in doing the difficult design work that meaningful integration requires. Recent discussions within the sector have highlighted how delivery models shape the possibilities for integrating academic and workplace learning. These are sector-wide conversations, and they reflect shared challenges around diverse learner cohorts, blended delivery, and the risk of compliance overtaking genuine learning. As a result, many innovative practices remain dependent on individual effort rather than structural support.

None of this means practice-led and research-informed approaches are mutually exclusive. The most effective curricula often blend elements of both. But blending deliberately is quite different from conflating accidentally.

When designing industry-engaged teaching, it’s worth asking honest questions. Are students positioned as inquirers or executors? Are they engaging with contested knowledge or settled practice? Does assessment reward critical reflection or merely competent performance? Is the industry project a vehicle for scholarly inquiry, or is scholarly framing a veneer over vocational training?

The answers won’t always be clear-cut, and that’s fine. But asking the questions helps us design with intention rather than stumbling into confusion – and helps us articulate what we’re doing when a peer reviewer, a sceptical colleague, or a university committee asks us to justify our approach.

Dr Saeed Talebi is an Associate Professor in the Department of Architecture and Built Environment at Birmingham City University and a Senior Fellow of the Higher Education Academy (SFHEA). He has held a number of T&L leadership roles, including Departmental Lead, Course Leader, and Academic Lead for Teaching Excellence and Student Experience. He has a keen interest in pedagogy in higher education, with particular interest in research-informed teaching and the integration of emerging technologies and practice-led projects into built environment curricula to enhance student outcomes and experience. He has also led the delivery of large STEAM research projects.

Professor Nick Morton is the Academic Director of Partnerships and STEAM at Birmingham City University. A Principal Fellow of the Higher Education Academy (PFHEA), he was awarded a National Teaching Fellowship in recognition of his track record in curriculum development. He has held a number of senior leadership roles at BCU, including Associate Dean for Teaching Education and Student Experience, overseeing Computing, Engineering and the Built Environment. He was elected Vice-Chair of the Council of Heads of the Built Environment (CHOBE) in 2012 and is a Fellow of the Royal Institution of Chartered Surveyors (FRICS).