As artificial intelligence becomes more deeply embedded in education, responsible adoption depends on more than what AI can do. It requires clear principles for how it should be used, safeguards for how data is handled and a commitment to keeping educators and institutions in control. Two new Avallain publications, developed as part of the Avallain Lab’s work, bring these considerations together, connecting the principles of trustworthy, human-centred AI with the technical measures needed to put them into practice.

From Principles to Protection: Building a Practical Framework for Responsible AI in Education

St. Gallen, September 2026 — As publishers, educational institutions and educators expand their use of artificial intelligence, questions around ethics, human oversight, privacy and security are becoming increasingly practical. It is no longer enough to ask whether an AI-enabled tool can support teaching, learning or content creation. Organisations also need to understand how that technology is designed, where responsibility sits, what happens to their data and how human judgement is preserved.

Two new Avallain publications address these questions from complementary perspectives: the Avallain Position Paper on AI Ethics and Security in Education and the Avallain AI Trust Model.

The Position Paper establishes Avallain’s view on the ethical, secure and practical use of AI in education, covering areas including human oversight, risk classification, transparency, bias, content safety, information security, data protection and the protection of learners. The AI Trust Model moves from these principles to technical implementation, outlining a multi-layer approach to protecting personal data across generative AI workflows.

Together, they provide a clearer picture of what responsible AI means not only as a principle, but as a responsibility spanning design, governance and operations.

Why Responsible AI Needs Both Principles and Practice

For organisations adopting AI in education, the challenge is not simply technological.

Publishers need to understand how to integrate AI into professional content workflows without compromising intellectual property, personal data or editorial responsibility. Institutions need confidence that AI-enabled systems can be introduced without weakening safeguarding, governance or educators’ professional authority. Teachers and other professional users need to understand what AI can help them accomplish and where its limitations require human review.

The wider educational sector faces the same challenge at scale. Generative AI can support content creation, lesson planning, feedback, assessment preparation and many other professional activities, but its outputs can also contain errors, bias, outdated information or content that does not reflect a particular pedagogical or curricular context.

The Position Paper therefore establishes a principle central to Avallain’s approach: AI should assist professional users rather than replace professional judgement. Across our products, Avallain Author, Avallain Magnet and TeacherMatic, AI-powered functionality supports clearly defined educational tasks rather than autonomously determining grades, progression, certification or other learner outcomes. Professional review, adaptation and validation remain essential.

This matters because responsible AI cannot be achieved through technical safeguards alone. Nor can principles around ethics and human oversight be meaningful unless they are reflected in how systems are actually designed and operated.

Responsible AI therefore requires both a clear ethical and governance foundation and practical safeguards that translate those principles into the technology itself.

From Avallain Lab: Two Connected Publications

The relationship between the documents is deliberate and explicit, and both initiatives are part of the work of Avallain Lab.

Founded in 2023, Avallain Lab is Avallain’s academic and pedagogical resource hub. It supports product development while contributing to the broader e-learning ecosystem through ethical, research-informed approaches to educational technology.

The Lab is led by Professor John Traxler, UNESCO Chair and Commonwealth of Learning Chair, Academic Director of Avallain Lab and Carles Vidal, MSc in Digital Education, Business Director of Avallain Lab. An advisory panel of leading experts further strengthens its work, including Professor Rose Luckin, Professor of Learner Centred Design at UCL, with extensive expertise in AI in education.

Positioning these publications within Avallain Lab reflects the purpose behind both: to connect academic and pedagogical expertise, responsible technology design and practical guidance for publishers, institutions, educators and the wider educational community.

‘Responsible AI in education requires more than adopting powerful technology. It requires us to connect pedagogical purpose, human oversight and robust safeguards from the outset. Through Avallain Lab, these publications make Avallain’s commitment to the educational community more transparent: one sets out the principles that guide our use of AI, while the other shows how those principles are translated into concrete protections for data and users. Together, they provide a practical foundation for continuing to develop AI that supports educators and institutions without compromising human agency, safety or trust.’

Carles Vidal, MSc in Digital Education, Business Director of Avallain Lab

The Avallain Position Paper on AI Ethics and Security in Education provides the broader governance perspective. Written for educators, institutions, publishers and decision-makers, it sets out Avallain’s approach using European principles for trustworthy, human-centred AI as a baseline, including the EU AI Act, the EU Ethics Guidelines for Trustworthy AI and GDPR.

It addresses where Avallain’s responsibility lies across the AI value chain, how to consider risk, why human oversight is essential in educational contexts and how organisations should approach transparency, bias, data protection, learner safety and residual risk.

The Avallain AI Trust Model, in turn, focuses specifically on Avallain’s personal data protection strategy within generative AI workflows. It describes a four-layer safeguard framework that covers the journey of data through Avallain’s AI-enabled applications and features, from input to output.

At the User Application layer, secure interfaces, active prompt hygiene and AI literacy support safer interaction from the point where data is entered. The Sovereignty Layer introduces controls including regional data governance, personally identifiable information masking, identity decoupling, audit trails and data minimisation. Secure Transport protects data through encryption and regional endpoint controls, while the AI Inference Engine layer is designed around stateless processing, contractual zero-retention and restrictions that prevent user data from being used to train external AI models.

The accompanying Trust Model infographic reinforces this layered approach, showing privacy and security not as a single control applied at the end of a process, but as protections distributed across the full generative AI workflow.

The connection between the publications also works in both directions. The Trust Model explicitly places its technical safeguards within Avallain’s broader commitment to responsible AI and directs readers to the Position Paper for the wider ethical and governance context. The Position Paper, in turn, points to the Trust Model for the technical measures underpinning Avallain’s privacy and data protection commitments.

One establishes why and under what principles AI should be used. The other explains how key privacy and security principles are translated into technical safeguards.

What This Means for Avallain Intelligence

These publications also further reflect Avallain Intelligence, our framework for the responsible integration of AI in education, with ethics and safety at its core and a human-centred approach throughout.

Avallain Intelligence starts from the principle that AI should expand what educators, publishers and institutions can achieve without transferring professional authority to automated systems.

But human-centred AI means more than keeping a person ‘in the loop’.

Users need to know when AI is being used and understand that generated outputs are probabilistic, not authoritative. Institutions need the ability to decide where and how AI functionality is enabled. Educators need sufficient AI literacy to recognise limitations and review generated material appropriately. Learners need protection from inappropriate or unmediated uses of generative systems.

The data underpinning those interactions must also be handled accordingly.

This is where the AI Trust Model adds another practical dimension to Avallain Intelligence. Human-centred AI depends on protecting the people using it, and that includes protecting their privacy, their data and the content entrusted to educational systems.

Measures such as data minimisation, regional data sovereignty, PII masking, encryption, stateless processing and restrictions on external AI training translate those principles into infrastructure and operational controls.

Together, the publications show responsible AI as a combination of pedagogical purpose, human oversight, ethical safeguards, transparent governance and secure technical architecture.

That combination is particularly important in education, where trust cannot be separated from responsibility towards learners, educators, publishers and institutions.

Shared Responsibility Across the Educational AI Ecosystem

Another important conclusion from the Position Paper is that responsibility for AI does not sit with a single organisation.

Avallain acts as an AI system provider. It designs, integrates and operates educational applications built on third-party AI models, without developing or training the underlying models itself. Avallain is responsible for how AI capabilities are embedded into its products, how they are presented to users and what safeguards apply at the application level.

Model providers, technology providers, clients, institutions and professional users consequently hold different responsibilities within the same value chain.

Clients and institutions determine whether and how to enable particular AI features within their own organisational and regulatory contexts.

Educators and other professional users remain responsible for reviewing, adapting and validating AI-generated outputs before using those outputs in teaching and learning.

This shared-responsibility model is significant for publishers and educational organisations evaluating AI technologies. Responsible adoption cannot be reduced to selecting a technology provider. It also requires appropriate governance, staff understanding, clearly defined use cases and professional oversight within the adopting organisation itself.

The role of an education technology provider is therefore not to remove that responsibility, but to create systems that enable responsible practice.

What Comes Next: Responsible AI as an Ongoing Process

Neither publication presents responsible AI as a challenge that can be addressed once and considered complete.

Generative AI technologies continue to evolve. Regulation develops alongside them. Educational practices change as institutions and professionals gain experience with new tools. Risks that appear manageable today may need to be reconsidered as capabilities and use cases change.

The Position Paper therefore commits to continuous monitoring and improvement, drawing on ongoing research, systematic testing, collaboration with Avallain product teams, external pilots with educators and schools, and partnerships with researchers and experts in digital education and AI. Ethical considerations, user feedback and evolving regulatory expectations are intended to feed back into product design, user guidance and recommendations for the wider educational community.

The work of Avallain Lab is an important part of that process, providing the academic and pedagogical perspective needed to examine new developments critically and connect technological innovation with research, educational practice and responsible design.

The AI Trust Model follows the same principle at the infrastructure level. Its safeguards are designed around current requirements for secure generative AI workflows while allowing data sovereignty arrangements to respond to different regional and organisational requirements.

The papers do not set out a fixed product roadmap. Instead, they establish the direction in which Avallain intends to continue working: evaluating risk, strengthening safeguards, learning from real educational use and adapting its technology, guidance and recommendations as AI itself evolves.

For our clients, this provides a more transparent basis for understanding the principles behind Avallain’s AI-enabled products and the controls supporting them.

For educators and institutions, it reinforces the importance of purposeful adoption, AI literacy and continued professional oversight.

For publishers, it provides a clearer framework for considering how AI can support content and product workflows while protecting data, intellectual property and editorial responsibility.

Finally, for the wider educational technology sector, it reflects a broader requirement: innovation and responsibility cannot be treated as separate stages of AI adoption. They have to develop together.

Read the Avallain Position Paper on AI Ethics and Security in Education

The Avallain Position Paper on AI Ethics and Security in Education provides the broader context for Avallain’s approach to trustworthy, human-centred AI.

It explores human oversight, professional responsibility, risk classification, transparency, ethics and bias, information security, privacy, learner protection and the limitations of generative AI, offering practical context for organisations considering how to introduce and govern AI in education.

Explore the Avallain AI Trust Model

The Avallain AI Trust Model examines how personal data is protected throughout generative AI workflows, from the user interface and regional governance layer through secure data transport and AI inference.

It provides a closer look at the technical and operational safeguards behind Avallain’s approach to data protection, including data sovereignty, PII masking, encryption, stateless processing, zero-retention and restrictions on the use of customer data for external AI model training.

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.

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Is research simply the pursuit of knowledge, or is there more to it? How does funding shape its priorities? How might different cultural contexts shape our understanding of its purpose, and how is AI changing the way we approach it? What does all this mean for the edtech industry? In this article, Prof. John Traxler explores the purpose of research, questioning how knowledge is created, whose interests it serves and what it means for the future of education and educational technology.

What’s the Purpose of Research?

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

St. Gallen, September 25, 2026 – This blog echoes our earlier one titled ‘What’s the Purpose of Education?’, adopting similar perspectives and raising similar questions about the role and impact of AI. Education and research might both be understood as the pursuit of knowledge, though in both cases this is not quite the case, even if there were any clear consensus about what actually constituted ‘knowledge’.

For many people, research is often assumed to mean the gathering of information, facts and data. It does, however, also embrace something more theoretical, abstract or conceptual, something else that might constitute knowledge; not merely observing and recording the falling of apples from trees but reflecting on what these observations might mean, developing an understanding of gravity and using that understanding to predict the behaviour of other material bodies.

Defining Research

Several ideas spring from this more abstract or conceptual view of research. One is that the conclusions are somehow durable and general; it is not just about apples, and it is not just today. Another is that research implies re-search, searching again, for findings that will challenge or refine or refute earlier conclusions, as illustrated by Eddington’s observations of the 1919 solar eclipse, which provided evidence supporting Einstein’s refinement of Newtonian gravity. In that sense, the knowledge generated by research is only ever provisional; good until there’s better, durable but not permanent.

Research in this conceptual or abstract sense, however, embraces research on research itself, not just a search for better data and better theories but also a search for a better understanding of theories themselves, of concepts, of abstractions, of knowledge, questioning the questions. 

This is sometimes portrayed as a greater criticality, questioning the meanings, language and methods of research, as well as the wider context in which it takes place. It also means considering researchers themselves; their motives, their options, their beliefs, their personalities, their backgrounds and their communities. We might ask, for example, why Newton had so much free time for speculation while so many of his contemporaries had so little, labouring perhaps as gardeners. Had their positions been reversed, what might their contributions to knowledge have been? Perhaps advances in fructivorous arboriculture rather than celestial mechanics.

The Modern Funding Landscape

Some of the more critical and contemporary questions about research continue to concern this wider context: who is funding it, and why are they funding it? Who benefits from it? Who loses from it? What is making it happen, or not happen? These questions bring us to the present day, prompting us to consider the arms trade, big pharma, market research and AI techbros; each funds vast amounts of research and raises questions about its purpose and motives.

Alongside this corporate research, there is also public funding for research, mostly in our universities. In our previous blog, we explored the ideal of education as a means of developing well-rounded individuals, drawing on the concepts of Bildung and the ‘renaissance man’. A similar ideal might once have been associated with university research: well-spoken, well-educated middle-class white men pursuing what we now call ‘blue skies’ or ‘curiosity-driven’ research. But if there was ever such a golden age, those days are over.

Public funding of research in many countries is now about national competitiveness, accounting for public money, bibliometric data such as citations, ‘taking ideas to market ’, and producing measurable ‘impact’. It is also about competition between individual researchers and their universities, encapsulated, for example, in the UK’s Research Excellence Framework. 

These days, the purpose of research is whatever the funder says it is. As has been frequently remarked, we live in an era of ‘policy-based evidence formulation’, not ‘evidence-based policy formulation’, in which ‘curiosity-driven’ or ‘blue-sky’ research has no purpose.

One consequence of this research environment is that no one wants to back losers; funders do not want to be responsible for wasting public money, so riskier research questions struggle to get answered, and so do the uncomfortable ones. Whilst a complementary perspective holds that, in large commercially funded research projects, ‘these are doomed to succeed’; they can’t go wrong because of the prestige invested in them.

Utopian, Dystopian and Different Cultural Perspectives

These depictions lead to competing utopian and dystopian views of the purposes of research, comparable to those earlier ones of the purposes of education. The utopian may be the Olympian pursuit of knowledge for its own sake and the greater good of humanity, undertaken by some noble elite. The dystopian, by contrast, is more likely the rush to fund any question that leads to some market advantage. 

This, though, should encourage us to ask whether these perspectives are universal, or are they shaped by particular cultural and intellectual traditions? Equally, we should not assume that other traditions and cultures don’t share these ideas. Do these purposes look different elsewhere, away from globalised universities, organisations and institutions? Does the question of purpose take on a different meaning or is it even relevant in every context? Are ideas such as pursuing, questioning and extending knowledge shared?  

Communities may understand, create, develop and share knowledge in different ways, encompassing the physical and social worlds as well as, in some traditions, the natural and spiritual. Rather than attempting to characterise these knowledge systems collectively, we might ask how different traditions broaden or challenge our understanding of what research is and what purposes it serves. 

We might also consider what happens when knowledge traditions with different levels of institutional, economic or political power encounter one another. This raises questions about who benefits from knowledge and research, who controls it and how knowledge originating within particular communities is recognised and used.

The Role and Impact of AI in Research

So what is AI’s impact on research? Decades ago, I was told that exploiting a technical innovation happens in three phases: firstly, it is used to solve existing previously difficult problems; then to solve previously impossible ones; and finally to solve previously inconceivable ones. Recent workshops on the impact of AI on research suggest that we are mostly at the first stage, merely making existing research procedures and practices happen quicker, bigger, sooner and cheaper. Researchers are mostly just reacting to stay in the game and exploiting these new tools, though, of course, ‘A rising tide lifts all boats’, the boats being researchers’ competitors. 

With tools like Anthropic’s Interviewer, which can interview thousands of people in days more competently than many human social scientists can in months, we are clearly entering the second phase. So how should researchers be addressing the third phase? When this happens, will it be changing the nature of knowledge, not merely rendering some of it obsolete but changing something fundamental? At this stage, it is, of course, impossible to conceive of the inconceivable, bringing to mind the Rumsfeld Matrix with its ‘unknown unknowns’. 

Finally, what is the purpose of research for the edtech industry? It sits at the intersection of the purpose of education and the purpose of research and is a fluid and unstable compound of all the features of both, utopian and dystopian. 

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

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

In this article, Carles Vidal, Business Director of the Avallain Lab, explores the importance of human agency and oversight in AI-driven digital environments. He considers what these principles mean for educators, learners, educational institutions and edtech providers and how they are reflected in the current AI regulation. He also examines why human judgement, choice and control should remain essential to designing technology as AI takes on a greater role in education.

Preserving Human Agency in AI-Driven Digital Education Environments

From regulation to product design: keeping users in control

Author: Carles Vidal, MSc in Digital Education, Business Director of the Avallain Lab

St. Gallen, August 24, 2026 – One direct consequence of the current integration of artificial intelligence (AI) in educational software is the surge of AI-driven features designed to assist teachers, students and administrators across a range of key educational tasks. These tools are used to generate personalised content, automate feedback and assessment, provide guidance and report on students’ performance, to name a few, all aimed at reducing workload and augmenting users’ productivity in digital education environments. 

Educational institutions demand these kinds of capabilities, and edtech companies are delivering them in multiple ways. From an AI ethics perspective, however, implementing AI-based automation features may compromise core tenets of trustworthy AI: human agency and human oversight. In education, these principles mean keeping educators as the final pedagogical authority on the platform while ensuring students can make informed decisions throughout their learning journey. The challenge for the sector is to design tools that fully preserve human agency while meeting the demand for greater AI-driven system autonomy.

Human Agency and Human Oversight

To understand what these principles mean, it is worth examining two relevant sources: the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (signed in 2024 and not yet ratified), which aims to set an international standard to protect fundamental rights throughout the AI lifecycle, and the European Union Artificial Intelligence Act (2024), which addresses these principles in the context of a binding legal and technical regulation within the EU market.

From a human rights perspective, the Council of Europe calls on states to protect human dignity, individual autonomy and transparency throughout the AI lifecycle: from its design to its implementation (articles 7 and 8). The framework establishes that individuals must retain self-determination, requiring adequate oversight so users can understand, oversee and challenge automated systems.

In turn, from a regulatory perspective, the EU AI Act recognises human agency and oversight in Recital 27 and Article 14 as key requirements for trustworthy AI. Drawing on the High-Level Expert Group’s Ethics Guidelines (2019), human agency implies that users should be able to make informed, autonomous decisions when interacting with AI systems, and critical to this concept is the right of the individuals not to be subject to a decision based solely on automated processing, when this produces legal effects on users or similarly significantly affects them. Article 14 translates human oversight into actionable regulation by establishing that high-risk AI systems must feature human-machine interface tools that allow persons to oversee, interpret and override automated outputs in real time.

Together, these frameworks define human agency as a key requirement to ensure the users’ right to be autonomous decision makers, and human oversight as a range of technical safeguards to achieve this end. 

User Agency in Digital Educational Environments

In the context of educational software, it is important to keep in mind that the EU AI Act regulates AI-driven features, not traditional automation tools that have been on the market for years. Specifically, the Act focuses on ‘machine-based systems’ able to generate ‘predictions, content, recommendations, or decisions that can influence physical or virtual environments’ (Article 3.1). Therefore, common features like pre-defined adaptive pathways or assessment based on standard scoring fall outside the regulation, as they do not rely on AI. 

For those edtech companies whose software, or a particular feature of it, falls under the scope of the EU AI Act, these principles have direct regulatory consequences. In this sense, Annex III of the EU AI Act establishes that AI systems that evaluate learning outcomes, assess the appropriate level of education or determine access to educational institutions are classified as high-risk, (except if they ‘do not pose significant risk of harm to the health, safety or fundamental rights of natural persons, including by not materially influencing the outcome of decision-making’ (Article 6.3)). When the exception does not apply, Article 14 requires software providers to build technical oversight controls directly into the user interface, such as review dashboards, score override options, output explainability tools and system stop controls so educators can validate or override AI suggestions.  

At a practical level, this means that, for example, an AI feature able to decide if students progress to the next academic year would be classified as high-risk, since it directly determines a critical educational outcome for users. In this case, the software provider must implement oversight measures that allow educators/administrators to review, override or cancel the system’s decisions. 

In contrast, an AI chatbot that suggests optional study resources to students, in accordance with the student’s performance, and that students can ignore, should not be considered high-risk, as it does not dictate any decision affecting those users. Therefore, no oversight measures would be required in this case, although even in lower-risk scenarios, user agency can also be supported by making AI interactions transparent or allowing users to disable recommendations.

These two examples illustrate clear risk classification cases and their corresponding mitigating strategies. In practice, following the same logic, software providers need to assess their AI-based features, determine their risk category and implement the appropriate oversight controls or transparency safeguards.

User Agency in Practice: From Principles to Product Design

How can these principles be promoted throughout the lifecycle of digital educational environments? In our research-based report, From the Ground Up (2025), a set of 12 core controls was identified as critical to ensure the ethical use of AI in education. Among these controls, the document recommends User Agency Preservation, which involves designing AI systems that empower teachers and students with choice and control, ensuring that AI in education does not undermine student autonomy or teachers’ professional judgement.

This principle operates at two levels:

• For educators: Ensuring AI acts as a supportive assistant rather than an autocratic system, preserving the educator’s professional decision-making authority.
  
• For students: Providing meaningful options to shape their learning process, such as setting personal goals, choosing between learning modalities or overriding AI recommendations when appropriate.

The degree to which students exercise their agency will depend on age and context; for younger learners, educators and guardians may carry out this responsibility by overseeing AI interactions on their behalf.

Avallain’s Approach

At Avallain, these principles form the foundation of Avallain Intelligence, our strategy for the responsible use of AI in education, with ethics and safety at the core. This informs the design of AI features and tools across our product suite. AI-powered capabilities in Avallain Author and Avallain Magnet, alongside the TeacherMatic AI toolkit, are built to support publishers, institutions, content authors and educators with tasks such as drafting content, generating ideas, creating practice activities and improving productivity in content creation and planning. In all cases, AI outputs are positioned as a starting point rather than a finished product. Professional review, adaptation and validation remain essential steps before content reaches learners.

This design philosophy has direct consequences for risk classification. Avallain’s AI features are deliberately designed to preserve human agency and oversight, an approach that avoids high-risk categorisation under the EU AI Act. AI does not determine final grades, progression, certification or learner outcomes. It does not make autonomous educational decisions or assess learners without human involvement. By ensuring that AI outputs are always mediated or supervised by qualified professionals, the software supports assistance and safeguards decision-making.

Human agency is also fostered through transparency and user control. AI features are clearly presented as assistive tools, not as authoritative sources. Users are informed when AI is being used and are encouraged to understand that AI-generated output is probabilistic, may contain errors and requires review. 

Conclusion

The integration of AI in educational software presents real opportunities to reduce workload and support productivity across publishers and educational institutions. However, these benefits must not come at the expense of human agency and oversight: the authority of educators to make final pedagogical decisions and the right of learners to remain autonomous actors in their own education. 

International frameworks and European regulation recognise and implement these principles, and responsible product design must respond accordingly. The challenge for edtech providers is to harness the potential that AI systems can deliver while ensuring that educators and learners remain at the centre of every decision. At Avallain, this is the approach we have chosen: AI as a transparent, controllable support layer, always subject to human judgement.

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

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.

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.

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) ↩︎

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