What is education ultimately for? While the answers have long been debated, rapid advances in digital technologies and artificial intelligence are encouraging us to reconsider many of the assumptions that have shaped education systems for decades. In this article, Prof. John Traxler explores the competing purposes that education has historically been expected to serve, from supporting economic and social priorities to fostering personal development and informed citizenship. He considers how digital technologies and artificial intelligence are reshaping these long-held perspectives and reflects on what this means for educators, learners and the future purpose of education itself.

What’s the Purpose of Education?

Author: Prof John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

St. Gallen, July 27, 2026 – There has always been debate and confusion about the nature and purpose of education and how educators should respond to the different views and pressures being expressed. Some of the most pragmatic and widespread have been about the economy and employment, placing learners into jobs, while others have emphasised the obligation of education systems to produce good citizens, good parents, entrepreneurs and artists. 

The growing presence of digital technologies within our societies and economies changes some of these pressures and views, while also changing how education systems can respond to them. And now, artificial intelligence accelerates and intensifies all of these pressures, adding new possibilities and new problems, often faster than education systems or indeed the edtech sector can respond. This blog from the Avallain Lab unpacks some of these pressures, problems and possibilities in order to give colleagues and clients a broader perspective.

Defining the Tension: Education vs The System

It is perhaps sensible to distinguish between ‘education’ and ‘the education system’ and to accept that there is a difference. The education system is not merely an abstract entity or the specific organisations and institutions that deliver education, and does absolutely nothing else. The education system, and its organisations and institutions, is often obliged to do and deliver all manner of things that might challenge understandings of ‘education’. Precisely because the education system serves many different stakeholders, it is often expected to fulfil purposes that extend beyond education itself.

Of course, countries, communities and cultures differ, and thus so do their understandings of education; furthermore, understandings within individual communities, countries and cultures, among families and individuals, will also differ. Education systems in democracies can, however, embrace many of these different and probably divergent understandings by not exposing them or discussing them too much, ‘letting sleeping dogs lie’ or not exploring too deeply what they might mean in practice or what they might tell us about our community, culture or country that we would rather not acknowledge.

The idea that the education system is basically and cynically a political device supporting a conservative political or economic elite is one such understanding. It is exemplified by the notion that the system is essentially a crèche that frees up parents to service the economy by making them available for employment. It is also reflected in the notion that increased participation in further or higher education hides youth unemployment, a critique often levelled at the UK’s various ‘youth opportunities’ or skills training in the 1980s, which compulsory national service does in many other countries.

Another example is the notion that there is some kind of ‘hidden curriculum’ intended to produce the next generation of compliant consumers, passive citizens and docile workers. There is moreover a notion that education, whatever it might mean, is the engine of social mobility, that education will enable the acquisition of riches, whereas quite often it might be that riches enable the acquisition of education and thus reinforces a state of social immobility, or more crudely for example, from an affluent family you can afford to stay on at school and then progress to a university education. 

The Systemic Pressures on Modern Education

These various depictions, even if plausible, need reworking as the 4IR (the fourth industrial revolution), the synergy of data communications, the internet of things, digital technologies, robotics and, of course, artificial intelligence, bites ever harder. This is effecting the ongoing ‘hollowing out of the labour market’, the erosion of jobs as the machines take over, eventually leaving only jobs for the likes of street sweepers and brain surgeons, at the two extremes, everything in the middle having disappeared. 

If there is, in fact, a cynical and political understanding of the education system, then it will need to embrace increasingly widespread unemployment and the widespread social tensions that accompany it. We could extend this interpretation to suggest that education systems, at the same time, will also have to address the apparently ever-increasing geopolitical instability as the post-war so-called ‘rules-based world order’ disintegrates and will have to address how climate change stresses economies, environments, infrastructure and populations. These are big challenges, not separate, disconnected or unrelated. 

Utopian vs. Dystopian Perspectives

There is also what might be called a utopian alternative to this previous dystopian analysis, namely that education systems are intended in some vague way to produce well-rounded individuals. They are articulate, cultivated, liberal, balanced, creative and wholesome; somehow better citizens, neighbours, voters, parents and perhaps these days, ones that are resistant to demagogy, extremism and crass materialism. This might be reflected in the German notion of Bildung, the Renaissance Man (man, really?) or the Confucian ideals of harmony, order and stability. However, because of its abstract nature and intangible outcomes, it is harder to operationalise and evaluate than the dystopian alternative. 

It is very, very possible to see these two versions as identical in substance but merely viewed through two different lenses. It is also possible to ask whether education systems lead societies, follow societies or merely reflect them. Leading societies might expose educators to accusations of presumption and arrogance in the case of the utopian version, whereas following societies may expose educators to accusations of compliance, complicity and subservience in the case of the dystopian one. 

More consensual, explicit and obvious understandings are somewhere in the middle. Producing well-rounded individuals, while a noble aspiration, might be viewed as inadequate if such individuals are unemployable, not only when they leave the education system but also throughout their lives. Lifelong learning is one sensible and acceptable response, developing the necessary generic skills, attitudes, knowledge and abilities while not seeming to be mere ‘training’. Training is, however, always lurking in the wings, ready to pounce and appropriate the nobler ideals of education to produce, for example, ‘jobs-ready graduates’ and taxpayers. However, it has to be said that any hoped-for alignment of the education system to the needs of the economy is far from perfect, still producing more poets than plumbers. 

Economic Realities and the ‘Hidden Curriculum’

The needs of the economy can also be softened to embrace the nurturing of innovators, creatives and entrepreneurs, and the advocacy of social mobility and lifelong learning, as ways to meet new economic challenges without engendering excessive social or political disruption. The ‘hidden curriculum’ can be re-presented around ‘soft skills’, the need to work together, to be polite, cooperative and tolerant, to speak persuasively, to discuss intelligently and to interpret instructions, oiling the wheels so to speak rather than crashing the gears.

The noble ideals of education now seem to be eroding at both ends. The argument that industry and commerce ought to provide their own training rather than expect the education system to do it for them is long since lost. Now, however, we also hear of those children first entering the education system needing to be potty trained and lacking a range of other basic social or physiological skills. Clearly, in the worst-case scenario, education systems are often servicing both employers and parents.  

How Educators Should Respond

So how should educators respond now? Sadly, educators are usually someone’s employees. They may not have much freedom of action and thus may need to seek support and ideas from all the stakeholders involved in our analysis. Educators themselves are also at risk of being ‘hollowed out’. Moreover, to what extent, in discussing the impact of artificial intelligence on education, are we portraying a version of the status quo ante or a dramatically transformed world? It affects the range of educators’ responses.

From the perspective of education as merely serving the economy in various ways, the responses are simple: continue to serve the economy, albeit a radically changing one, in which the wider social and political repercussions will also need to be managed and contained. From the perspective of education as having a ‘higher’ mission, there is an as-yet poorly understood and evolving relationship between education and knowledge. If education is about the acquisition of knowledge, has artificial intelligence not changed not only how knowledge is acquired but also what knowledge actually is now? Not only which new knowledge is now useful or which old knowledge is no longer useful, but also what is actually the nature of knowledge, and how that now determines our understanding of education and learning.

Navigating Knowledge in an AI-Driven World

Historically, there was a transformation as networked computers became widespread. We moved from knowledge being about knowing stuff to knowledge being about knowing where to find stuff, where navigation replaced recall. Discrete knowing minds had been joined or replaced by networked ones in constant interaction, and the network of knowledge became more important than its nodes. Referring back to the earlier remarks about social immobility, education might not be about what you know but who you know, and how affluence will get you into those parts of the education system to meet them; the acquisition of ‘social capital’ masquerading as ‘educational capital’. Maybe artificial intelligence will change nothing.

At the same time, personal digital technologies, including social media and Web 2.0 applications, became ubiquitous and pervasive, and knowledge became personal. We could each generate, share and consume our own, but this meant knowledge also became fragmented, transient and subjective. Education, in its ‘higher’ sense, is still struggling to redefine its purpose in this new knowledge economy, but artificial intelligence is now widely available, amplifying and accelerating the fragmentation and uncertainty around knowledge, thus further problematising the purpose of education. 

For the time being, education may enable learners not only to find things but also to tell good from bad, useful from useless and benign from malign. Apparently, new technologies, AI in this case, are first used to solve problems thought to be difficult, then to solve problems that are impossible, and eventually to solve problems that were previously inconceivable. We are, with regard to knowledge, knowing and education, for the time being in those early phases, taking responsibility to help learners navigate critically and competently through what is out there. That responsibility may become more important than ever as artificial intelligence continues to reshape our relationship with knowledge.

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About Avallain

For more than two decades, Avallain has enabled publishers, institutions and educators to create and deliver world-class digital education products and programmes. Our award-winning solutions include Avallain Author, an AI-powered authoring tool, Avallain Magnet, a peerless LMS with integrated AI, and TeacherMatic, a ready-to-use AI toolkit created for and refined by educators.

Our technology meets the highest standards with accessibility and human-centred design at its core. Through Avallain Intelligence, our framework for the responsible use of AI in education, we empower our clients to unlock AI’s full potential, applied ethically and safely. Avallain is ISO/IEC 27001:2022 and SOC 2 Type 2 certified and a participant in the United Nations Global Compact.

Find out more at avallain.com

_

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Developing educational technology is not simply a technical challenge or a pedagogical challenge. It is both. The most successful digital learning solutions balance educational effectiveness with processes that ensure quality, maintainability and long-term sustainability. In this article, Prof. John Traxler explores the origins of software engineering and courseware engineering, examining what these disciplines can teach us and why many of these ideas remain relevant today.

What Engineering Can Teach Educational Technology

Author: Prof John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

St. Gallen, June 26, 2026 – Educational technology is often discussed in terms of pedagogy or innovation. Less attention is paid to how educational technology itself should be developed and maintained. Yet many of the challenges facing educational technology today, including quality, scalability, sustainability and cost, are not new. They are challenges that software engineering has been grappling with for decades.

The Origins of Software Engineering

This needs a bit of history. 

Decades ago, perhaps fifty or sixty years ago, computer programs were written ‘by hand’ by skilled, expert people called ‘programmers’, and these programmers were pretty much the totality of the computing workforce, able to do new and wonderful things on a daily basis. So, of course, expectations and ambitions grew bigger and bigger, and, in due course, so did the awareness that things were going badly wrong. What were called programs came to be called projects or systems. The biggest and most ambitious – and the most expensive – were routinely over budget, overrun and not what had originally been required. Even those on time were often unmaintainable and thus quickly unusable as their environment changed.

It became apparent that programs or software systems were not merely slabs of code but large and complex artefacts, comparable perhaps to suspension bridges, power stations or ocean liners. The latter were all developed at huge cost and under contract, respectively, the products of the established disciplines of civil engineering, electrical engineering and nautical engineering. So perhaps programmers should be asking themselves: what constitutes ‘engineering’, what can we learn from it, and whether such a thing as ‘software engineering’ is a possible solution to the growing failings and concerns.

People working with software, both in industry and academia, began to itemise the tools and techniques common across engineering disciplines and assess their relevance to their own work. Some of the tools and techniques they came up with include, among others, mathematics and formal notation, structure and development phases, project management, process modelling, quality assurance, modularity, cost estimation, prototyping, maintenance, usability, design, requirements engineering and specification.

Bearing in mind the need to acknowledge the major difference, namely, that software is just instructions and data, items that need no raw materials and will not rot or rust. It was also worth recognising that complexity alone does not make something an engineering problem; ‘The Lord of the Rings’, both book and film, are large and complex artefacts but were not apparently consciously engineered. The question then became how engineering tools and techniques could be adapted and adopted for software development.

From Engineering to Software Engineering

Part of the problem was not fully understanding what the customer required. Often, the customer did not fully understand the requirements themselves or could not explain them adequately, creating a need to express those requirements completely and unambiguously. Consequently, models, prototypes and diagrams came into the picture, and so too in some cases did the mathematical expression of these requirements. Here, I am thinking of obscure, well-established ‘formal methods’ and their notations, such as Z (Z Notation), VDM (Vienna Development Method) and CSP (Communicating Sequential Processes), which are mathematical approaches used to specify software requirements precisely and unambiguously. 

For anything beyond trivial requirements, producing a software system requires breaking it down into components, often through top-down decomposition, reducing one big requirement into progressively smaller ones. It might also involve reusing previously trusted components and representing how these components were connected, while managing and monitoring the processes by which the product was developed. Then, at the same time, recognising that the requirement may change as the development proceeds or its environment evolves. Costs needed estimating, predicting and controlling, and developers needed the reassurance that a lengthy and complex development process was, at each stage, not deviating from what was required, ready for a final handover where money and software would be exchanged in ways that showed, incontrovertibly, that everything was as it should be, contractually, and nothing as it shouldn’t. 

Often, these lengthy and highly structured development processes were outpaced by an evolving external environment, customers’ evolving understanding or the increasing need to involve actual users in the development process. This led to other approaches, including RAD, the self-explanatory Rapid Application Development, using more and more powerful simulations, prototypes, tools and languages, which shorten development and delivery times. Sometimes, however, poor documentation and structure meant higher costs down the line in the form of maintenance. RAD’s instinct to iterate quickly, involve real users and ship working software early, did not fade so much as harden, first into the Agile movement of the early 2000s, and later into DevOps and continuous delivery, which remain the dominant ways software is built today.

Courseware Engineering

It became obvious that, just like software systems in general, courseware, a term invented to make an analogy with software and to recognise that courseware, namely educational software packages, was also often composed of large and complex artefacts that needed to be engineered, but in a form specifically for education. Courseware arrived, however, with baggage that included competing educational theories, multiple stakeholders and, compared to mainstream software, more interactional complexity and less computational complexity.

It did, however, still require time and effort to develop, and so, among other techniques and tools, courseware cost estimation evolved to account for the costs of different kinds of interaction, media and logic. Ian Marshall at Abertay¹, and others, worked from contemporary industry data to refine the factors and parameters in the equations, and also on the ratio of time taken to develop vs time of usage by an individual learner. The other side of this, pitched against the cost of different media and interactions, was their respective pedagogic efficiencies and how each might relate to different pedagogic strategies, pedagogic ‘bang-for-your-buck’.

Several projects started from the ‘conversational framework’ of digital learning articulated by Diana Laurillard; her ‘Rethinking University Teaching’² of 2002 is still required reading around the world and remains widely cited worldwide. This framework portrayed formal teaching and learning as interactions – ‘conversations’ – between the teacher’s conceptions and the learner’s conceptions and how the teacher had to devise situations or artefacts in the real world, meaning the usual formats like lectures, set books, assessments, lab experiments, field trips, seminars, group projects and online chat, that would enable the teacher’s conceptions to change the learner’s conceptions, meaning the learner would have learnt something from the teacher.

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Each of these situations or artefacts could be classified into one of four broad categories: acquiring, inquiring, producing and practising. The framework was sometimes extended beyond individual learners to groups of learners, and these, too, had their pedagogic artefacts and situations, classified as discussion or collaboration. Of course, they could also each be analogue or digital, synchronous or asynchronous, remote or present, though some of the possibilities might be daft.

These two threads, the cost of developing different functions within educational software and the ways in which these functions map onto various educational situations and artefacts, come together with research that calibrated the various educational situations and artefacts. 

Educational situations and artefacts simply refer to lectures, readings, workshops, field trips, seminars, games, coursework, examinations, practicals, role-play, tutorials, simulations, essays and projects. Then everything digital that evolved from these, web quests, webinars, lecture capture, etc., etc., etc. Researchers attempted to measure their respective educational effectiveness, perhaps in terms of something as simple as the proportion of material that was remembered, understood or applied. In short, this amounted to a form of cost-benefit analysis. For large online universities, the value of such analysis was obvious and widely exploited. The obvious examples are Laurillard’s CRAM, Course Resource Appraisal Model, now in use at London’s UCL³ and Conole’s Media Advisor⁴.

Summing Up

All these ideas, many rooted in the last century, remain relevant, even if the numbers and technologies have moved on; we are still trying to produce high-quality, maintainable and pedagogically effective educational digital technology with cost-effective, managed and sustainable processes. This piece starts with a fairly general critique to home in on a point at which education, technology and commerce converge in ways that remain relevant.

The lesson from software engineering and courseware engineering is not that educational technology should become more technical. Rather, it is that successful educational technology requires the same rigour, planning and discipline that other mature engineering fields developed in response to complexity.

These principles may matter more than ever as Generative and Agentic AI reshape our processes, our products, and the very nature and delivery of learning.

References

1. Marshall, I.M., Samson, W.B., Dugard, P.I. (1994). A proposed framework for predicting the development effort of multimedia courseware. In: Herzner, W., Kappe, F. (eds) Multimedia/Hypermedia in Open Distributed Environments. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-9361-7_12 ↩︎
2. Laurillard, D. (2002). Rethinking university teaching: a conversational framework for the effective use of learning technologies (2nd ed.). London: Routledge Falmer. ↩︎
3. Kennedy, E., Laurillard, D., Horan, B., & Charlton, P. (2015). Making meaningful decisions about time, workload and pedagogy in the digital age: the Course Resource Appraisal Model. Distance Education, 36(2), 177–195. https://doi.org/10.1080/01587919.2015.1055920 ↩︎
4. Conole, Grainne (2002). Systematising Learning and Research Information. Journal of Interactive Media in Education, 2002(7) ↩︎

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About Avallain

For more than two decades, Avallain has enabled publishers, institutions and educators to create and deliver world-class digital education products and programmes. Our award-winning solutions include Avallain Author, an AI-powered authoring tool, Avallain Magnet, a peerless LMS with integrated AI, and TeacherMatic, a ready-to-use AI toolkit created for and refined by educators.

Our technology meets the highest standards with accessibility and human-centred design at its core. Through Avallain Intelligence, our framework for the responsible use of AI in education, we empower our clients to unlock AI’s full potential, applied ethically and safely. Avallain is ISO/IEC 27001:2022 and SOC 2 Type 2 certified and a participant in the United Nations Global Compact.

Find out more at avallain.com

_

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

The challenge in edtech is not simply to innovate but to do so with purpose. So how do organisations identify the opportunities most worth pursuing? In this piece, Prof. John Traxler draws on academic and consultancy perspectives to explore different approaches to identifying those opportunities, from brainstorming and horizon scanning to market research and AI. This article offers a broad overview and perspective on how the edtech sector can approach innovation more deliberately. 

Scanning, Researching and Rethinking Innovation in Edtech Development

Author: Prof John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

The Challenges

St. Gallen, May 28, 2026 – Where does our next edtech product come from? How do we spot good ideas, or create them, whilst avoiding the bad and the old ones? How do we avoid simply copying our competitors and do more than merely comply with our clients? How do we do more than enhance and improve the status quo? How do we fight off ‘stuckness’? We should also ask whether, in fact, the edtech sector differs much from other sectors where software systems are developed or perhaps any other kind of product? 

Each exists in its own world of stakeholders, regulations, procedures, traditions and resources. What distinguishes software development is that the raw materials are merely data and instructions, free and limitless, perhaps mistakenly suggesting that innovation is without cost. Its traditions and procedures have expanded, evolved and mutated incredibly rapidly, over less than a working lifetime. Together, these mean that the questions we raise do not have tried-and-tested answers. The ethos of developers is also a factor: are they hungry, visionary start-ups, or reliable, quality-conscious corporates, and how does each approach these questions?

Furthermore, edtech products that support education systems operate in contexts where multiple, conflicting stakeholders make achieving consensus on ‘good’ edtech very problematic. 

This blog addresses these kinds of questions, but as understood by an academic who has moved into the edtech sector, drawing on consultancy and research experiences, hoping to provoke questions and reactions and perhaps some changes. This reflects part of the Avallain Lab’s wider mission to foster productive relationships between academia, the sector and its clients.

A previous blog discussed the echo chamber/revolving door that seems evident in the people and processes of institutional IT procurement, and, in the current context, this may be a brake on change and innovation, excluding some technical voices and perspectives, fostering incremental quantitative improvement rather than radical qualitative transformation. Again, problematised by uncertainty about what constitutes ‘good’ edtech products in education systems that are very unconfident and uncertain about their own purpose.

Brainstorming

To start upstream, brainstorming is a recognised technique, widely described across the media, not just undisciplined musings or mutterings. Brainstorming is a creative technique for generating many new ideas about a problem or topic, focusing on quantity over quality initially, deferring judgement, encouraging wild ideas and building on others’ suggestions in a free-flowing, often group-based session. The basic rules are: 

• Suspend judgement during the initial phase; don’t let criticism kill the momentum.
• Encourage wild ideas; they contain the seed of a practical, breakthrough feature.
• Go for quantity; clear out the obvious ideas to reach the innovative ones underneath.
• Combine and improve participants’ ideas; transform simple ideas into better ones, building on shared contributions. 

Common sense suggests the best number of people to be involved is fewer than perhaps ten, to avoid chaos and confusion, and ‘hiding’, but more than perhaps five, to avoid stagnation. Still, clearly, composition is important, with similarity and homogeneity fostering candour and spontaneity, whilst differences in hierarchy might be inhibiting. There is, however, an argument for neurodiversity or diverse cognitive styles, but all this presupposes a large enough pool of potential participants in which to make these kinds of choices. Obviously, the physical setting is important; different settings all send different signals to different demographics and cultures, as does the timing. One possibility is the away-day format, cut off from daily pressures and obligations, and a moderator might prevent groupthink and give space to quieter, tentative voices. There is perhaps some overlap with the heuristics for effective focus groups, including tips for effective moderation that ensure a free-flowing, non-judgemental event.

Incidentally, boredom too has its uses, all the more so as phones and computers often keep it at bay, creating opportunities for creativity or originality.

These established formats and prescriptions for effective brainstorming are mostly pre-COVID and assume that working and meeting face-to-face are the norm. This is clearly no longer the case with many people, perhaps the more creative or imaginative, who are either working online from home or digitally nomadic. Their varied individual settings, disruptive external events, such as a delivery at the door, lunch burning and the changed cues, language and tacit protocols of online interaction, might not be so conducive to spontaneity or candour.

Perhaps the move of the Delphi technique events from face-to-face synchronous to asynchronous online, for all sorts of pragmatic reasons, might suggest a compromise format that reconciles individual creativity with group interactivity, with the added bonus of the latter being digitally recorded and preserved. 

Whilst these might be prescriptions for effective brainstorming, they do not address when to brainstorm in relation to any product development cycle or how to feed the outcomes into the mainstream of developments; there are presumably good ways and bad ways, and at the risk of going off at a tangent, this looks like an opportunity for ‘diffusion of innovations’ approaches to find the good ways and the factors that determine which best way.

Horizon Scanning

Horizon scanning is a way of spotting possibilities coming towards us, for example, of managing those possibilities that brainstorming has surfaced.

Some background: several years ago, I collaborated with Alison Potter from the TEL division of Health Education England (South), part of the UK NHS, to review horizon scanning and to formalise and embed it in their routines. Horizon scanning attempts to spot concepts, opportunities and technologies before they reach the market (and before they reach the competitors, hoping to catch the next Teflon or Post-it before they do), especially those not immediately and obviously relevant, the ones off in left field. 

The work examined organisations comparable in size and technology to the NHS, including the UK government’s Cabinet Office, and distilled their procedures into a set for the NHS. Our initial research question was, ‘What models exist for identifying and then prioritising which new and emerging technologies might add value to healthcare education in the UK?’ We conducted a literature review of horizon-scanning methods to identify existing models and systems. Then we conducted interviews with six experts across education, government, healthcare and the independent strategic foresight sector. The findings from the literature shaped the interview design. Interviews comprised of three parts: a short experiment to gauge how each expert horizon scans, their reaction to our proposed framework and lastly, their thoughts on the skills and tools necessary to horizon scan.

Alison’s final version of the horizon-scanning framework, the culmination of the whole research process, features a sequence of several distinct activities, and her paper goes into greater detail. 

• Identification, or scanning a defined set of sources, addressing what is out there
• Classifying, or filtering, then prioritising, addressing what is relevant
• Assessing, addressing, what is its potential impact
• Disseminating, or navigating, addressing where it needs to go
• Evaluating, or reflecting, addressing how we do it better

And then, start again, perhaps on some predetermined cycle time matched to the organisational timescales and responsiveness. 

We should, however, always bear in mind, when defining the sources to be scanned, assessing the impact of any discoveries and disseminating them, that any such discoveries need to align with various commercial, technical and organisational factors. These factors might include the headroom and skill set among staff, the alignment with the existing product portfolio and client base, and the organisation’s management of change.

These factors are, in effect, among those identified in the diffusion of innovations community, a body of expertise stretching back many decades, tackling innovations from new technical products to changed farming practices to improved attitudes to smoking and drink-driving. In this context, the ‘innovation’ is the horizon-scanning discovery. Diffusion of Innovations work in its various forms over the years looks at factors such as the characteristics of the people involved, perhaps the developers inside edtech or the clients outside, whether they are naturally risk-taking or risk-averse, the development, whether it can be deployed without a tangle of interoperability issues, whether it can be easily explained and understood (and sold), the nature of any competitive advantage, so on.

It does, however, leave the sources to be scanned unanswered. Horizon scanning is one; others might exploit the expertise and experience of researchers, described briefly later, exploiting their literature searching skills, their contacts and their colleagues, and also their intuitions and ability to pick up ‘weak signals’.

Market Research

Looking now at market research as another source of innovative ideas, I am deeply indebted over the years to the work of Gordon Rugg on knowledge and its elicitation, in every kind of research that involves people, meaning clients, users, learners and the wider market. This work recognises that people know, believe and feel all sorts of different things and that finding out what they are thus requires all sorts of different techniques and tools. This work is expressed as the ACRE, ACquisition of REquirements, framework, a tabulation that goes from every type of knowledge or feeling or value to the most effective tool or technique for eliciting it, from the conventional, namely surveys, questionnaires, etc., to the ‘contrived’, such as card sorts, rep grids and laddering, to the physical, such as models and prototypes. Within this overarching framework, there is still the need for adaptation, refinement and common sense, so don’t ask compound or double-negative questions; do make sure participants are not hungry, uncomfortable or embarrassed and so on.

In my work, I have often lambasted ‘the usual suspects’ of social science (and market research), namely the focus group, the interview, the questionnaire and the survey, rounded up unthinkingly to answer every conceivable question, as ethically problematic, methodologically deeply flawed and usually inappropriate. 

Without unpacking and explaining all of the alternatives to the ‘usual suspects’, which you can unpack here, it might suffice to say that asking questions only provides the answers to those questions, even assuming the respondent is able and willing to give an adequate, honest answer, rather than finding out what is actually important to the respondent. Furthermore, asking questions about desirable futures only elicits answers based on modified presents and remembered pasts rather than any radically reimagined futures. 

These are the weaknesses in expecting clients or users – actually, users are not always asked, often their managers or IT do so on their behalf  – to guide future products or projects; merely asking them will likely elicit only requests for what they already have, but faster, easier, bigger or bug-free. So perhaps academic research can represent a more rigorous version of market research?

‘Real’ Research

Separating market research from ‘real’ research is an artificial and unnecessary distinction, since both should be activities aimed at acquiring, analysing, understanding and contextualising what people know or want or feel in ways that are trustworthy, cheap, appropriate, ethical and efficient. Both can suffer from exactly the same flaws because each, in its own sphere, is subject to very similar pressures and constraints. The distinction might in fact be between the people, the market research researchers on the one hand and academic researchers on the other, and on the expectations, timescales and resources around their different professions. The question here is, what can academic researchers contribute?  

Two things, really, namely, what might be called primary and what might be called secondary research, the former being actually doing stuff, conducting empirical studies, setting up interventions, taking measurements, listening to people, building prototypes and running workshops, the latter being connecting with the outputs and activities of the people who are doing primary research, using experience and expertise, to understand what is happening and what might be useful, an informal version of horizon scanning in practice.

It has to be said that primary research, especially in the context of commercial edtech, is probably a waste of time, since any commercial advantage is likely to be small and short-term, though it may have value as an agent of culture change within an organisation, raising awareness of methods and limitations, and this may be something of indirect commercial value. There is a far better case for secondary research since it spreads the risks and costs and is perhaps a semi-intuitive version of horizon-scanning, based on gut feelings and looking for otherwise undetected ‘weak signals’. This rationale underpins the Avallain Lab, built on expertise and experience that a search engine or chatbot can’t simulate and tapping into contacts and colleagues before their work hits the public domain. This model is still being refined.

Process Maturity

To go off at a tangent, process maturity models have recently been spotted being applied to AI development, though not yet to educational AI development, and that may be an important or provocative opportunity.  

Process maturity and its models are ways of describing how well an organisation handles bugs, mistakes and mishaps. If an organisation just deals with them as they crop up, it might be categorised as relatively immature. It may, however, document or record them, perhaps analyse and reflect on them, and have procedures for analysis and reflection, and indeed departments and specialisms for doing this, indicating a progressively more mature organisation. These stages have been formalised as process maturity models, progressing from chaotic (Level 1) to consistently effective and optimised (Level 5), using models to standardise procedures, enhance quality, boost efficiency and ensure scalability to achieve strategic goals (and, accordingly, to gain certification). This approach was adopted in large-scale software development in the 1990s; for example, the Capability Maturity Model of the 1980s. Also, later, in courseware development and now, it seems, in some AI development and perhaps next in future edtech development, why not?

The relationship between notions of process maturity and the other earlier topics is, however, oblique; the first ones talk about qualitative or strategic jumps, thinking ‘outside the box’, about breaking away from the established trajectory, whereas the last one talks about incremental quantitative or technical improvement, about moving along the established trajectory but more effectively and efficiently, ‘inside the box’. They must, however, be reconciled; otherwise, organisations risk either forever improving the past or never shaping the future. 

The way forward may be to treat brainstorming and horizon scanning as processes in their own right, ones that, on reflection, could be monitored and measured and thus improved, but also then feed into roadmaps. In essence, the way forward must reconcile the tensions between the ‘stay hungry’ of start-ups and the quality assurance expected of established organisations.

Artificial Intelligence

These are all largely pre-digital accounts, and we should now perhaps look for digital tools that capture these methods and techniques, especially for AI tools, the generative ones that answer our questions and the agentic ones that execute our processes. At the moment, however, the best advice might be to proceed with caution. Current AI, working on probabilistic mechanisms, risks emphasising the existing norms rather than breaking away from them; perhaps ‘hallucinations’ have a part to play. One under-researched area comprises scenarios depicting how society, its education systems, the economy, its labour markets and the workforce and their skill sets will evolve under the impact of artificial intelligence. In their different ways, these all form the contexts of edtech products and how they are developed.

Finally

This piece outlines how disparate techniques from disparate communities might have productive synergy. Each technique, and probably others, deserves greater attention in order to explore adaptation and integration. Taken together, they offer useful perspectives on how edtech organisations might think more deliberately about identifying meaningful opportunities, challenging established assumptions and navigating future change. The impact of AI is currently limited to answering questions and making discrete activities more efficient. Clearly, it won’t stop there.

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com