Creating meaningful, impactful AI tools relies on collaboration and real-world testing. The ‘AI for Teaching Innovation’ project at the Edinburgh Futures Institute (University of Edinburgh) applies this principle by involving educators, students and learning technologists in the co-design and classroom testing of AI tools. In this interview with Javier Tejera, Senior Learning Technology and Design Advisor, he explains how this approach ensures AI supports authentic learning and teaching.

When Educators Become AI Designers: Inside Edinburgh’s AI for Teaching Innovation Project

An interview with Javier Tejera, Senior Learning Technology and Design Advisor, Edinburgh Futures Institute, University of Edinburgh, conducted by Carles Vidal, MSc in Digital Education, Business Director of Avallain Lab

As part of the Memorandum of Understanding between the Centre for Research in Digital Education (University of Edinburgh) and Avallain AG, signed earlier this year, both institutions are strengthening their collaboration to bridge the worlds of research and industry. The partnership helps the University engage more closely with technological and commercial trends, while supporting Avallain in deepening its research awareness and developing pedagogically rich, ethically grounded learning technologies.

Building on this shared vision, we want to highlight one of the University’s most exciting recent initiatives: the AI for Teaching Innovation project led by Professor Siân Bayne and Javier Tejera from the Edinburgh Futures Institute. Now entering its second phase, the project explores how generative AI can be used to create meaningful, field-specific teaching tools through a co-design process that actively involves professors in defining each app’s scope, refining prompts and, crucially, testing the tools in real educational settings. In its first year, this innovative project delivered ten AI-powered applications across disciplines such as medicine, business administration, law, history and environmental studies.

The results of this initiative are now being presented in different venues and have been unanimously welcomed by educators eager to create meaningful AI tools. At the same time, the project sets an example for edtech companies on how to develop AI apps with genuine value for education.

Interview with Javier Tejera

Javier, at the Edinburgh Futures Institute, you and your team have led the AI for Teaching Innovation project, exploring how generative AI can open new possibilities for teaching and learning. The project supports educators in designing and building AI-driven applications that respond to real pedagogical needs, fostering creative human/machine partnerships and helping academic staff develop confidence and skills in working with AI.

1. Let’s start with the origin of the project. What prompted the creation of this project, and what key questions or objectives guided your exploration of generative AI’s role in teaching and learning?

When ChatGPT was released, I asked it a couple of questions about a course I was teaching elsewhere. The responses seemed perfectly fine at first sight, but if I paid close attention, they were quite wrong, frankly. I thought this had potential as a way to prompt students to think critically about a given text, but I thought it wasn’t enough: I also wanted to add specific style, tone, configurations, or, in other words, to have some degree of control over the AI. I was thinking that this could open endless possibilities to be creative in teaching.  

This led me to start building small web applications where I could have more control over what the AI generated. As a small pilot, I created a couple of applications that were used in an MBA and an MSc in Heritage here at the University of Edinburgh, and the teachers and students quite liked the experience.  

From this point, we launched the AI for Teaching Innovation project, where we co-create web applications for teaching and learning. Our main idea here is to be creative while moving away from the hype and disillusionment of AI and education, and to try to explore and understand collectively whether AI might actually help us to teach or not. 

2. In your view, what makes this project unique compared to other AI-in-education initiatives happening today?

I think it is our commitment to a collaborative, ground-up approach that actively involves teachers, students and learning technologists in the design and implementation process. Many existing products in the AI and education space are developed in isolation, often far removed from the realities of classroom teaching. As someone who works closely with educators on a day-to-day basis, I have a firsthand understanding of their needs and challenges. I can see clearly that many of the current solutions simply do not resonate with teachers or meet their requirements.  

Teachers know very well what will work in their specific contexts. However, it is often difficult (if possible at all) for them to be involved in the design process of educational products. This project tries to bridge that gap. I often see the project as transforming teachers from software consumers into software producers. I think this is the uniqueness of this initiative.

3. You involved lecturers closely from the very beginning. How did their participation in the co-design process shape the direction or outcomes of the project?

Their participation is not just an element, but we believe it’s the entire foundation of the project. We have a very structured co-design process. It starts with workshops where we reflect on the ‘Big Ideas’ of AI and education while also providing a hands-on space to sketch ideas using UX design activities. From there, the ideas selected go to the next phase, where we have learning design clinics and discovery meetings to polish the ideas before the build. 

What the project team does is facilitate this whole process, but the ideas come from the teachers. When an app is ready, they are its best ambassadors because of the sense of ownership created from the very beginning. 

They come up with ideas we genuinely hadn’t thought about. Now, thanks to them, we have new tools and application patterns that we can adapt to other courses. This participation highly influences the direction of the project, but I would even argue that it is the direction of the project itself. 

4. The examples you developed, such as simulated stakeholder interviews or clinical case scenarios, seem both creative and practical. Could you share more about the value these tools bring to the classroom and how educators and students have responded to them?

Take the ‘Entrepreneurial Personas’ app, for example, which was co-designed with the Business School and used in an MBA course. It’s designed to help students practice B2C and B2B market research. Students come in with their own business ideas, and the tool helps them challenge those ideas, refine their concepts and discover potential new product features that would be useful for their target market (which they can also customise). 

This was the first application created, and it was fascinating to see that students were obviously learning about business and entrepreneurship, but they were also actively learning about the possibilities and limitations of using AI in a real-world business context. 

It sparked a much richer conversation that covered not only the topics being taught but also the technology itself. We see this ‘dual’ learning (subject matter expertise and AI) in all the apps. Based on the initial survey data we are gathering, students like the experience. Many report that they start feeling a bit sceptical, but then they end up quite enjoying it. And in this line, if there is one common piece of feedback across all the apps, it’s the teachers mentioning that students are highly engaged during the learning experience. 

5. You’ve managed to deliver ten tailor-made, highly specific AI applications in a remarkably short time. Could you tell us more about the development framework and workflow that made this possible?

Speed and flexibility during development are essential due to our limited resources, so we decided to use React for all project applications, ensuring they are quite modular. Each app sits on a set of shared components: conversation modules, feedback areas, saved interactions, scenario branching and so on. This modularity allowed us to rapidly swap features in and out, tailoring each application to what the teachers want. 

Interestingly, all the apps cover very specific use cases, but we are experiencing the ‘paradox of specificity’ where the more closely we tailor, the more widely applicable the core patterns become. Also, we deliberately avoided time-consuming tasks during the build, for example, ‘LLM benchmarking’ because our competitive advantage isn’t in shaving a few percentage points off model accuracy, but in leveraging the learning design and subject-matter expertise provided by the teachers. The real value is the workflow, not the technology: teachers bring domain and teaching expertise, we bring the scaffolding for rapid iteration and deployment while combining it with learning design and a creative but critical approach to AI. 

6. From a pedagogical perspective, what does the project tell us about the ways AI can meaningfully contribute to improving teaching and learning practices, rather than simply replicating or automating existing ones?

This is a crucial distinction, and it’s at the heart of our project. If you look at the current landscape, most AI-in-education products are based on productivity and efficiency. They’re designed to (supposedly) help teachers grade faster, plan faster, speed up admin tasks, etc. This is not pedagogy really; they don’t fundamentally change the learning experience. 

We’re moving away from the efficiency-first mindset and asking, ‘How can this technology foster teaching creativity?’ or ‘How can it help us to teach differently?’ This is where we think AI gets exciting. It’s not just about replicating or making a task faster; it’s about exploring forms of active, exploratory, fun, engaging learning. The project shows that when teachers lead the design, they ask for tools that help their students think critically, practice complex skills and engage with the content in a deeper way.

7. This project seems to challenge the traditional ‘top-down’ model of educational technology development. What are the advantages of an academic-led innovation process?

In the usual procurement model, educators tend to be left out of key decisions and become mere software consumers. Even when teachers are consulted, they rarely get a real say in what gets built or how it works. Choices are limited, and their expertise doesn’t reach the design stage. With this project, we deliberately flipped this situation. Here, educators are brought in from the outset, shaping the actual product design. The advantage is obvious: the tools reflect real, lived classroom needs, not generic assumptions. Academic-led innovation means faculty don’t have to compromise with solutions built for and by somebody else. They co-create resources that fit their teaching, their students and their pedagogy and values. This kind of direct involvement brings more enthusiasm and ownership, encourages creative risk-taking and creates new ideas that most commercial vendors miss entirely. Ultimately, the result is technology that feels genuinely created for education by educators themselves. 

8. The AI for Teaching Innovation project has a strong emphasis on educational research. What are the initial results showing? And what other specific research proposals can we expect?

It’s still early, so data collection is starting now, but the initial feedback is promising. Anecdotally, we have teachers reporting that students have better results on the assessment, feeling more comfortable, confident and clear about what is expected from them. But I want to highlight that we are just as interested in the qualitative elements. We’re looking closely at how the experience is perceived by both students and staff. Those elements are just as critical as quantitative data.  

We see this project as a ramp-up for educational innovation and research. It’s not just one single research project; it opens the door to many different angles of research. Because each application is so closely connected to a specific field, we’re seeing the teachers themselves take the lead. They are already showing these ideas at conferences and are planning to write papers for their particular disciplines, applying these pedagogical findings in medicine, business, environmental studies, law, etc. The project’s research output won’t be a single paper from the core team; it will be a collection of discipline-specific studies led by the educators who conceptualise and use the apps. 

9. Looking ahead, do you imagine this collaborative model of AI app creation evolving in universities and the wider edtech sector?

Indeed, and I think we need more! I certainly believe that teachers have endless ideas for educational products, but they don’t have the space or opportunities to bring them to life. They know what their students actually need. So, risking an over-generalisation here, I think there is a disconnect between teaching contexts and the edtech sector. 

On one side, you have educators with deep subject-matter expertise and on-the-ground pedagogical knowledge; they aren’t developers, nor should they be, as their job is to teach and research within their fields. On the other side, you have the edtech sector, which possesses the technical expertise, development resources and infrastructure to build and scale products, but many of the brilliant, context-specific ideas teachers have don’t reach their design teams.  

So, I truly think that fostering more collaboration can unlock a wealth of creativity that the edtech sector has largely left untapped, and it would be fantastic to move in that direction more seriously as a sector. 

10. Finally, on a more personal note, what has this experience taught you about the future of human/AI collaboration in education, and what message would you share with educators who are just starting to explore AI in their teaching?

My message to educators would be quite simple: start playing around with AI, but do it critically. It’s easy to be overwhelmed. We are all swimming in a sea of hype, flooded with blanket statements and grand promises about how AI will ‘fix’ or ‘revolutionise’ education. This hype can push people into two camps: uncritical adoption or total rejection. I don’t think either is very helpful. 

For me, being critical is not about rejecting the technology. It means getting your hands dirty and trying things while asking nuanced and difficult questions: Who built this, and what are their values? What is their design context and intended purpose? Does it actually work in my specific context? When they say ‘it works,’ what evidence are they using? 

This critical engagement is how we move forward responsibly. It’s how we, as an educational community, get to shape this technology towards better futures for education, rather than just accepting the future that is simply sold to us. So, my advice is to be curious, be cautious and be the one who decides what ‘good’ looks like.

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About the CRDE at the University of Edinburgh

The Centre for Research in Digital Education is part of the University of Edinburgh, based in the Edinburgh Futures Institute and the Moray House School of Education and Sport. It does research, teaching and knowledge exchange in areas relating to digital education including policy, practice, artificial intelligence and education futures.

We work with many partner universities as well as policymakers, the cultural heritage sector, schools and other public and private sector organisations. Our partners value us for our critical approach to learning, teaching and technology in formal and informal education, and for the ways in which we combine our research with world-leading practice in digital education.

Find out more at de.ed.ac.uk

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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

Language carries assumptions, cultural context, and implicit meaning that make comprehension and translation difficult for humans and even more complex for AI to master. Expanding on the first half, this piece explores language for persuasion, showing how cultural norms, reasoning patterns and rhetorical conventions shape communication, learning and the complexities of teaching or interpreting language effectively.

AI and Why It’s Impossible to Learn or Understand Language: Cultural and Cognitive Challenges

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

This piece continues to argue that it is impossible to learn, understand or discuss what anyone else says or writes beyond the simplest, most specific and concrete level, even perhaps among people with a shared mother tongue. This makes conversation, learning, translation and reasoning more difficult than they initially seem, especially when they involve artificial intelligence. 

The piece is divided into two halves. The first, ‘AI and Why It’s Impossible to Learn or Understand Language’, already deals with language as idiom, whilst the second half deals with language for reasoning. We transition from language for description to language for persuasion. 

As I said in the first half of this piece, language as an idiom is a challenge for learners or translators outside the culture or community that actually hosts that idiom, and this clearly also applies to the chatbots of GenAI.

Sadly, but obviously, reasoning also depends on language, and reasoning is not usually about anything as concrete and specific as ‘the cat sat on the mat’. In fact, it seems safe to say, as we did in the previous piece, that language is mostly not about the specific and the concrete, rather that language, especially language that is in any sense important, is metaphor, simile or analogy, and each of these is based on implied notions or assumptions of ‘likeness’.

Deductive and Inductive Reasoning

Reasoning, or rhetoric or argumentation, in the West, is defined as either deductive or inductive.

Deduction, the former, is true by definition, like ‘2 and 2 is 4’ or ‘Socrates is a man, all men are mortal … etc, etc.’ This is because that is how ‘4’ and how ‘men’ are defined. It has to be true because that’s how the terms are defined, strictly speaking, not true but valid; it is a tautology; it is circular. On inspection, we may be unclear whether it is males, homo sapiens or hominids being discussed and unclear how some things are counted, clouds, for example. 

The latter, induction, works from specific instances towards inferences about the general, say from ‘every dog I’ve met at the park has been friendly’, to ‘all dogs are friendly always’. Somehow, those dogs in the park are ‘like’ all dogs all the time. In making this inference, we preserve or favour one aspect and neglect others, such as ‘in the park’. Even statistical inferences work the same way; the ‘sample’ being analysed is ‘like’ the ‘population’, and somehow representative of the ‘population’, to use the statistical terminology. 

But, and this is the kicker, they all depend on some tacit or shared consensus about the ‘likeness’ that is going on, that, ‘this one is like that one and like that other one and like all those others’, and they have something in general, in common, and that depends on culture, that the people within a culture or subculture basically agree. As we said, once you get slightly more abstract than cats sitting on mats, all language is metaphor, analogy or simile, sometimes in plain sight when we see ‘like’ or ‘as’ in a sentence, sometimes hidden, with only an ‘is’.

The Challenge of Abstract Thought

Plato’s ‘Allegory of the Cave’, from ‘The Republic’ (Book VII, 514a–520a), expresses the notion that we humans only experience separate poor solid instances of some higher, hidden, abstract and immutable reality. ‘The dog,’ for example, is perhaps the wrong way around; we experience each of those poor, solid real dogs and assume we can group them together as some abstract ‘dog’ and discuss them accordingly, whereas different cultures might do the grouping and thus the reasoning differently. We assume that the distinction between ‘dog’ and ‘not-dog’ is clear-cut and sharp, or the ‘park’ and ‘not-the-park’, with nothing vague and nothing in between. 

Fuzzy concepts as opposed to sharp ones are another challenge for logic and reasoning, needing the duality of ‘either/or’ with nothing smeared out in between. In fact, even ‘park’ or ‘dog’ might not be so clear; are feral dogs or wild dogs included and is Hampstead Heath a park? We could be pragmatic and use the rule-of-thumb attributed to Indiana poet James Whitcomb Riley (1849–1916), ‘When I see a bird that walks like a duck and swims like a duck and quacks like a duck, I call that bird a duck.’ So Hampstead Heath is a park; a car park is not.

One way or another, these mental processes do not generate new knowledge; they expose and perhaps distort knowledge already beneath the words being used.

Examples of Culture Shaping Understanding

To take some specific examples where different words are used to describe basically the same process. That is, the process of culture shaping reasons, of words not describing our experiences but shaping them:

Hammer and nail: Abraham Maslow said, ‘If the only tool you have is a hammer, you tend to see every problem as a nail,’ meaning the extent to which preconceptions or interpretations shape understanding or analysis, the solution shaping the problem.  

Evolution and creation paradigms: Creationists argue that God created fossils to test the Christian faith, whilst evolutionists argue that fossils were the product of sedimentary deposition. The culture of a specific community, whether creationist or evolutionist, determines how it understands the evidence rather than the evidence determining the understanding.

Policy and evidence: The (cynical) notion is that ‘evidence-based policy formulation’ is often ‘policy-based evidence formulation’, a suspicion familiar to many of us who have worked for ministries and ministers; that the interpretation of evidence precedes the gathering of it, and of course, logically speaking, there is no evidence for evidence, that would be circular, it would be a logical fallacy.

Personal construct theory: Those ways, major or minor, that individuals use to understand or organise their experiences, those dogs in the park, for example, partial or over-simplified or over-generalised explanations that help us live our lives.

We might use different words, paradigms, policies, cultures or constructs, for example, but these are all essentially the same process at work: words actively shaping experience rather than passively describing it. I admit to being on shaky ground when analysing the workings of words with words, but what choice do I have? The aim was to point out, however weakly, the difficulty that GenAI might have in conversation, translation and education.

The Principle of Linguistic Relativity

The Sapir-Whorf Hypothesis, aka the ‘principle of linguistic relativity’, is relevant here. It is the notion that language shapes thought and perception, meaning speakers of different languages may think about and experience reality differently, in mutually incomprehensible ways, and may never truly understand each other. 

The Hypothesis suggests that a language’s structure influences how its speakers conceptualise their respective worlds. In essence, it suggests that language shapes thought and perception, meaning speakers of different languages may think about and experience reality differently. The language we learn influences our cognitive processes, including our perception, categorisation of experience and even our ability to think about certain concepts. 

There are two versions. The strong version of the hypothesis proposes that language determines thought, meaning that thought is impossible without language. A provocative view, rejected mainly by linguists and cognitive scientists, but resonating with George Orwell’s idea of Newspeak, which we mention later. The weak version, proposing that language influences thought, suggests that whilst thought is not solely determined by language, it is significantly shaped by it, a more acceptable interpretation. 

So we have differing vocabularies for snow in English. Languages like Inuit suggest that English speakers might have a less nuanced understanding of snow-related concepts. Secondly, some languages assign grammatical gender to objects, potentially influencing how speakers perceive those objects; meanwhile, the Chinese ideogram or character for ‘happiness’ was derived from ‘woman in house’, an interesting trajectory from the concrete to the abstract.

The point about Orwell is that the appendix in his novel ‘1984’ describes a political system that, by eliminating problematic or challenging words from its language, Newspeak, eliminates problematic or challenging thoughts from the population, suggesting again that the possibility that language can shape culture (or society in this case), or Orwell thought so. Any resonance with current concerns about political and corporate influence on the news media is, of course, purely coincidental.

Cultural Dimensions and the Definition of ‘Culture’

At some point, we ought to introduce ‘cultural dimensions’ and Geert Hofstede’s work, among others, as much of this piece mentions or implies culture as a fundamental mechanism in shaping language. Being abstract, we can only define ‘culture’ using either metaphors or other abstractions, so we will settle for something simple, ‘the way we do things around here’, ‘here’ being our society, our friends, our organisation, our profession or wherever else a group of people have shared values.

Cultures are obviously different from each other; ‘cultural dimensions’, based on Hofstede’s work, are a tool for describing in what respects and by how much they differ. So we might say that some cultures are risk-taking, others risk-averse; some are consensual, others authoritarian; some take the long view, others the short one; some are individualistic or even selfish, others communal and collectivist, and so on, giving us scales by which to calibrate different cultures. 

So these are alternative perspectives, language and conversation shaping culture and thought, and the opposite, culture and thought shaping conversation and language, or perhaps a dynamic between the two.

Culture, Reasoning and the Diffusion of Innovations

If we are to use language to reason, question, analyse, judge, evaluate and critique, rather than merely locate the cat, then we have to recognise how language is shaped by culture. This may be national culture, regional culture, gender culture, class culture, generational culture, ethnic culture, or indeed a mixture of all of these, as they still shape language. 

Reasoning, questioning, analysing, judging, evaluating and critiquing are essential components of higher-level learning and of higher-order language learning, if learning is to be about reasoning as well as rote reciting. Cultural dimensions, however, suggest that some cultures may be less tolerant of dissent and the outcomes of reasoning than others (and may not even have the language to express it), or might find some conclusions less palpable, less conforming or more risky. 

A further complication is the theorising behind the Diffusion of Innovations, which suggests that changes to opinions, attitudes or beliefs, in effect acceding to reasoning or argumentation, are all dependent on various factors; culture is one of these, as we can deduce from Hofstede’s ‘cultural dimensions’, for example, the risk-aversion/-acceptance and the consensual/authoritarian dimensions. There are, however, others, for example, the ‘relative advantage’ of the changed opinion, attitude or belief and its ‘trialability’, ‘observability’, ‘compatibility’ and ‘complexity’ are also factors in acceding to an argument or reason representing the changed opinion, attitude or belief. The pure logic of GenAI may well see these complications as unreasonable.

Language and Its Cultural Influence

The work of linguist Robert B. Kaplan comes at this from a different direction. Analysing essays from English, Romance, Semitic and Asian students suggested that every language is influenced by a unique thought pattern characteristic of that culture, or by the collective customs and beliefs of its people. 

Rhetoric, argumentation and thus reasoning exhibit culturally distinct patterns. Rhetorical conventions vary across cultures, affecting how students compose essays. English rhetoric in this depiction follows a linear, logical structure influenced by Western philosophical traditions. This harks back to our depiction of inductive and deductive reasoning, whilst other cultures may employ parallelism, helical, zig-zag or indirect approaches in writing, leading to different expectations in composition and argumentation, ones that make less sense to the processes of GenAI. 

Western Reasoning

Interestingly, a paper from Harvard published in November 2025 observes that, ‘LLM responses … their performance on cognitive psychological tasks most resembles that of people from Western, Educated, Industrialized, Rich, and Democratic (WEIRD)’

Returning briefly to Western, or WEIRD, reasoning: whilst we have described the established ways of reasoning correctly, the deductive and the inductive, there are also Western ways of reasoning incorrectly. When learning logic, you start with the fallacies of irrelevance: fallacies that introduce irrelevant information to distract from the main argument. 

For example:

Ad Hominem: Attacking the character or personal attributes of an opponent rather than the argument itself.  

Appeal to Emotion: Manipulating emotions, such as pity or envy, instead of using logical reasoning to win an argument.

Ad Populum: Claiming something is true or right because many people believe it. 

The Red Herring: A distracting point to divert attention from the actual issue. 

Straw Man: Misrepresenting an opponent’s argument to make it easier to attack. 

There are also Fallacies of Weak Induction, such as: 

The Post Hoc Fallacy: Assuming that because one event followed another, it must have caused it. 

The Slippery Slope: Asserting that a small first step will lead to a chain of related, often negative, events. 

Finally, there are Fallacies of Presumption, including:

Circular Reasoning: Using the conclusion as a premise to support a conclusion.

False Dichotomy: Presenting only two possible options when more options exist.

Cultural Significance of Logical Flaws

Our point is that these errors, even if valid within a Western context, may not be obvious or convincing to a non-Western language learner accustomed to reasoning differently, and that their weight may be less or different in other cultures. So, more hierarchical or authoritarian cultures may find Ad Hominem arguments perfectly valid when made by someone with sufficient status, whilst more consensual or communal cultures might be happy with Ad Populum arguments rather than standing out in the crowd. Additionally, cultures are probably each on a continuum from emotional to rational, and this, too, will determine how they react to reasoning and argument.  

Culture or individual cultures are, however, not immutable. According to historian Ian Mortimer, the Elizabethan hand mirror and the vernacular Bible, Tyndale’s in English and Luther’s in German, moved the needle towards greater individuality or individualism and lessened communality or collectivism in their societies, as the mobile phone selfie has done more recently. Relating individually to AI chatbots might have similar consequences, as individuals use them for emotional and intellectual support, or it might involve a completely different cultural dimension.

Other Linguistic Dimensions

Another attribute of language is lexical distance, the distance and differences within and between language families. So, for example, German is very close to Dutch but distant from Chinese, so German speakers might struggle to learn Chinese but not Dutch. Are GenAI chabots somewhere amongst the Anglo-American, low-context, consultative and slightly out-of-date languages, lexically distant from many other language families?

This linguistic metric might also apply to different literary genres or, indeed, different literary authors. Is James Joyce lexically distant from Ernest Hemingway, or a haiku from a sonnet, and thus more or less difficult to understand or translate, especially for chatbots rooted in the GenAI culture they inherit from their trainers?

Languages are also sometimes classified on a continuum from high-context to low-context, with greater or lesser baggage, background, assumptions and preconceptions, and metaphorically expecting more or less bandwidth for successful comprehension or translation. Clearly, low-context language learners will struggle to hear, for example, the irony, euphemism, hyperbole or sarcasm at work in high-context languages. In a high-context language, the neurodiverse will have much less metaphorical bandwidth; they, me, in this context, are low-context, missing cues and signals from their higher-context culture or colleagues.

The Challenge for Educational AI

It is difficult to imagine how to converse with each other with all of these issues going on, so perhaps we should spend more time talking about the cat sitting on the mat and less time on democracy and freedom, and perhaps, for safety’s sake, conversations with GenAI chatbots should also stick to cats. Language is not as simple as it seems, nor is learning it or teaching it, nor hoping that GenAI will be good at either.

The challenge for educational AI is how to proceed safely, from helping learners with the specific and concrete to the abstract and general, from learning about cats to learning about freedom.

The mention of ‘safely’ does, however, add the additional element of ‘harm’. This piece was not really about the ethical dimension of educational AI. This too can be tackled, like our account of deduction and induction, from the top down, from abstract general principles, such as beneficence, to the concrete and specific, such as bomb-making recipes, or from the bottom up, from a long list of concrete and specific misdemeanours, to some abstract general principle that unites them. 

Either way, we hope AI can replicate human reasoning, but human reasoning is flawed, and those flaws will likely be replicated in the training of GenAI. This is precisely the two approaches being explored and investigated at Avallain Lab.

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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

Intelligence, whether human or artificial, cannot be determined purely through rational or quantitative measures. It also involves interpreting context, nuance and metaphor, the unpredictable elements of human thought. This piece examines how these aspects affect learning and understanding a language, and the challenges of participating in a community, especially as AI becomes more widely used for teaching and learning.

AI and Why It’s Impossible to Learn or Understand Language

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

St. Gallen, October 29, 2025 – In this piece, we argue that it is impossible to learn, understand or discuss what anyone else says or writes at anything beyond the simplest, most specific and concrete level. This even perhaps applies to people with a shared mother tongue, making conversation, learning, translating and reasoning more difficult than they initially seem, especially when they involve artificial intelligence and computers. 

The discussion is, in fact, divided into two halves: the first deals with language as idiom, and the second deals with language for reasoning. In other words, we are discussing language and learning, and language learning, thus discussing intelligence, artificial and otherwise.

Language as Idiom

AI and the Turing Test

Artificial intelligence is the ongoing effort to develop digital technologies that mimic human intelligence, despite the undefined nature of human intelligence. It has been through various incarnations, such as expert systems and neural nets, and now generative AI or GenAI, seeming to finally deliver on the promises of 40 or 50 years ago.

Over all this time, there has, however, been a test, the Turing Test, to evaluate AI’s apparent intelligence, revealing insights into both intelligence and language. GenAI, the current incarnation, is in effect pattern matching with a conversational interface, a sophisticated form of autocomplete, completing responses based on the world’s vast digital resources. However, because of this, it can produce ‘hallucinations’, responses that are plausible but wrong, and can also perpetuate harm, bias or misinformation.

The Turing Test imagines a human, the ‘tester’, able to interact independently with another human and a computer. If the tester cannot tell when he or she is interacting with the human or the computer, then the computer can be said to be ‘intelligent’; it passes the Turing Test. 

Expanding the Boundaries of Intelligence

We should, however, consider how this would work with a seemingly intelligent mammal, say a chimpanzee, conversing in American Sign Language, or an extraterrestrial, say ET, the visiting alien scientist. The film Arrival illustrates the possible superiority of other intelligences, their languages and their differences. These, too, might manifest ‘intelligence’ and challenge ours, widening our notions of intelligence and thus what we might expect from AI.

There is an alternative model of what is going on with intelligence, specifically with conversation, translation and learning; the Chinese Room. This thought experiment imagines a person passing words or perhaps phrases or sentences, called the ‘tokens’, into the Chinese Room. An operative looks them up in a large dictionary or some similar reference book or ‘look-up table’. The operative passes the answer or the translation or the learning out as another ‘token’, there seeming to be no intelligence or consciousness involved, only what is in effect an automaton.

However, it does raise questions about the operative; do they have any taste or ethics? Could they or should they be subject to Asimov’s Three Laws of Robotics? Is such an operative even possible? Is the operative merely another Chinese Room inside the Chinese Room or a way of disguising an algorithm as a human operative? Would the Chinese Room pass the Turing Test?

Human Understanding and the Limits of Machine Interpretation

Incidentally, in the film The Enigma of Kaspar Hauser, about a foundling, a boy with no past, set in Germany in the early nineteenth century, the eponymous hero is asked, ‘How to discern the villager who always tells the truth from the villager who always lies?’. Instead of applying deductive logic, Kaspar offers a simple, childlike answer from his unique perspective: he would ask the villager, ‘Are you a tree frog?’. His innocence allows him to see things differently, and his absurd question and approach might sidestep the issue of formal logic and thus rationality and intelligence. The Turing ‘tester’ just asks, ‘Is it raining tree frogs?’, revealing how a machine may struggle to interpret common sense and the outside world in the way humans do. 

What is relevant here, however, is not a generic human ‘tester’ but a human learner wanting to be taught. Could this learner tell the difference between a human teacher and an artificial one, GenAI in this case? It depends, of course, on the learner’s expectations of pedagogy. If the learner expected a didactic or transmissive pedagogy, GenAI could give a very competent lecture, essay, summary or slide deck, ‘hallucinations’ notwithstanding.

If, on the other hand, the learner expected something discursive, something that engaged with them personally and individually, building on what they already knew, correcting their misunderstandings, using a tone and terms familiar to them, then ‘raw’ GenAI would struggle. This is even before considering the added dimension of emotional intelligence, meaning recognising when the learner is tired, frustrated, bored, upset or in need of a comfort break or some social support.

Language for Reasoning

Early AI and Challenges in Language Learning

Let’s draw on two early efforts we had in 1960. PLATO was a computer-based learning system using ‘self-paced learning, small cycles of feedback and recorded traces of incremental progress’ (Cope & Kalantzis, 2023:4), showing that simple didactic teaching was possible, however crudely, very early on. Additionally, in about 1966, ELIZA, one of the earlier natural language processing programs, provided non-directive psychotherapy, that is, psychotherapy led by the client, not by the therapist. Psychotherapy led by the client’s problems or constructs that might have translated into non-directive or learner-centred pedagogy, heutagogy, perhaps, self-directed learning.

So, how does this relate to learning a language? Curiously, GenAI is based on the so-called large language models, and the medium for exploring intelligence seems to be the conversation, certainly not any IQ test!

Learning a language, even our own mother tongue, from any kind of computer is likely to be tricky. Firstly, it is difficult because computers lack body language, hand gestures and facial expressions.

Plurilingual Societies 

Then, in plurilingual societies such as South Africa, or even most modern societies, we have code switching, the switching between languages, even within individual sentences. There are also potential problems with language registers, ranging from frozen, formal, consultative, casual, to intimate. In a monocultural society, these should be straightforward. However, in multicultural societies, characterised by different norms, speakers may gravitate toward the more formal or the less formal; there can be uncertainty, confusion and upset. These are a kind of ‘cultural dimension’ that we will explore later, suggesting there is no easy correspondence between languages.

Euphemisms, Neologisms and Internet Language

Then we have euphemisms, puns and double entendre, not meaning what they say, and hyperbole and sarcasm, sometimes meaning the opposite of what they say. Furthermore, we have humour in general, but black humour in particular, but why ‘black’? What is it about blackness? We have neologisms, new words from nowhere, sometimes only fleeting, occasionally more durable, skeuomorphs, new meanings from old words, and acronyms, especially those from the internet and World Wide Web. All these pose problems for learners, who need to understand the cultural context and current culture. Similarly, problems arise for GenAI, especially when it always lags behind human understanding and skims across the surface, missing human nuances. 

Community Languages and Cultural Assimilation

We also have subversive, perhaps rebellious, perhaps secretive languages. For example, Polari, the one-time argot of the London gay theatre community, derived partly from Romani. Cockney, rhyming slang, historically from London’s East End, and based on a strict mechanism, which, for example, gets you from ‘hat’ via ‘tit-for-tat’ to ‘titfer’ or from ‘look’ via ‘butcher’s hook’ to ‘butchers’, so ‘can I have a butchers at your titfer?’.

There is also back slang, which forms a vocabulary from words spelt backwards. In Scotland, ‘Senga’ for Agnes. None of these examples is necessarily accessible, inclusive or open. Two textspeak examples make the same point: Arabish, the messaging language using a European keyboard for Arabic sounds, and Mxlish, the one-time language of South African teenagers using the messaging platform, MXit, both with enormous footprints. 

Each of these, in its own way, is the property of a particular community or culture, perhaps waiting to be appropriated, ridiculed, sanitised or ignored by others, and eventually, perhaps, to be ‘taught’, the kiss of death.

In fact, we could argue that learning these languages is an integral part of acceptance and assimilation into a defined community, in just the same way as talking about differential calculus and only then talking about integral calculus is part of acceptance and assimilation into the community of mathematicians. Our point is that displaying intelligence, acquiring language, being part of a culture, having a conversation and learning a subject are all very closely intertwined and necessarily complex for strangers or chatbots to join in with.

Metaphor and Abstraction

Then we get on to the metaphor. In a quarter of an hour of a television drama, I heard ‘black people’, ‘landmark decision’, ‘high art’ and ‘ wild goose chase’, none of which was literally true. I listen to ‘The Freewheelin’ Bob Dylan’, safe in the knowledge that Bob Dylan is not a bicycle. I worry about ‘raising money’, knowing this will not involve lifting the money upwards. ‘The Lord is my shepherd’, in the Psalms, does not tell me that I am a sheep. We also get bombarded with the language inherited from Aristotle, of ‘correspondences’, ‘the ship of state’, ‘the king of the jungle’ and ‘the body politic’, whilst thinking the car needs a wash, even though being inanimate, it has no needs. As a university professor, I have two chairs, neither of which I can actually sit upon, whilst on the news, I hear that the office of the president has been tarnished, though I also hear it has just been redecorated. Confusing, isn’t it?

Parables, such as the ‘Good Samaritan’, from the Gospel of Luke, and the ‘seed falling on stony ground’, from the Gospel of Matthew, are, in fact, just extended metaphors delivered in the hope that the meaning could be inferred by people familiar with the cultural context of their origin. People refer to the Prodigal Son, from the Gospel of Luke, with no idea of the meaning of prodigality. However, they are perhaps meaningless to other cultures, those remote from historical Palestine. The same is true of many fables, such as ‘The Hare and the Tortoise’.

However, as all are ripped out of their cultural or historical context, the moral point is needed now to explain the parable or fable, rather than the other way round, as originally intended; nowadays, sowers, samaritans, hares and tortoises are no longer everyday items. They are, in fact, clichés, remarks bereft of meaning, another challenge for language learners and large language models. 

While metaphor takes words from the concrete to the abstract, the use of ‘literally’ seems to drag them back again, so perhaps Bob Dylan is literally freewheeling, and money is literally being raised. ‘Literally’ is, however, sometimes used for emphasis and sometimes just used weirdly. Yesterday, I heard a podcaster talking about being ‘literally gobsmacked.’ Did he mean he had been smacked on the gob? Actually? Literally? As someone who is autistic, understanding language from a largely concrete interpretation, this confusion, uncertainty and ambiguity is a daily struggle. 

Once we get away from anything as simple and concrete as ‘the cat sat on the mat’ and approach the abstract of love, democracy, freedom, race, virtue and truth, we enter our own small community where some understanding is possible inside, but little is possible outside. These concepts of love, race, democracy, freedom, virtue and truth may all have very different meanings among, say, Marxists, Buddhists, Stoics, Confucians, feminists, humanists and Calvinists, unlike cats and mats. So how can we learn about them and converse about them? And how can our large language models ever engage with them meaningfully, except in a manner reminiscent of the Chinese Room model?

Conclusion

So, the conclusion, so far, is that while it might just be possible to have a meaningful dialogue across a shared culture and mother tongue, especially at the level of simple description and action, is there much hope of having one with computers?

Perhaps, this reinforces the importance of keeping humans at the centre of teaching and learning. AI, no matter how sophisticated, cannot keep up with the diversity, transience and cultural complexity of language. Responsible human mediation remains essential, and we must recognise that computers will never be fast enough or flexible enough. Owning up to these limits is an ethical response in itself, not just from Avallain but across the educational AI sector and its clients. 

However, safeguards like Avallain Intelligence provide a first line of defence. This strategy for ethically and safely implementing AI in education aims to put the human element at the centre. While it cannot solve all the challenges of the evolution of language, ethics or learning, it establishes a framework to ensure that technology remains guided by human understanding, creativity and judgement, enhancing rather than replacing human agency. 

This pair of blogs, the first half and the following second half, is about language, about how understanding language is tricky for humans and even trickier for computers; it is about the medium, not the message. Understanding this might not stop people from saying or promoting nasty, harmful things, but it might perhaps prevent them from being misunderstood.

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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

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

What if mobile learning had the intelligence and context it lacked 25 years ago? This piece revisits the rise and fall of early mobile learning projects and considers how the convergence of artificial intelligence, contextual mobile data and educational expertise could support more responsive and personalised learning today.

Bringing Mobile Learning Back with AI, Context and Expertise

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

St. Gallen, July 28, 2025 – Around 25 years ago, many members of the European edtech research community, myself included, were engaged in projects, pilots and prototypes exploring what was then known as ‘mobile learning’. This roughly and obviously referred to learning with mobile phones, likely 3G, nearing the dawn of the smartphone era. Learners could already access all types of learning available on networked desktops in their colleges and universities, but they were now freed from their desktops. The excitement, however, was around all the additional possibilities. 

One of these was ‘contextual learning,’ meaning learning that responded to the learner’s context. Mobile phones knew where they were, where they had been and what they had been doing¹. These devices could capture images, video and sound of their context, including both the user and their surroundings. This meant they could also understand and know their user, the learner. 

So, to provide some examples:

• Walking around art galleries like the Uffizi and heritage sites like Nottingham Castle, learners with their mobile phones could stop at a painting randomly and receive a range of background information, including audio, video and images. The longer they stayed, the more they would receive. Based on other paintings they had lingered at, they could get suggestions, explanations and perspectives on what else they might like and where else they could go.

• Augmented reality on mobile phones meant that learners standing in Berlin using their mobile phone as a camera viewfinder could see the Brandenburg Gate, but with the now-gone Berlin Wall interposed perfectly realistically as they walked up to and around it. Similarly, they could see Rembrandt’s house in Amsterdam. Learners could also walk across the English Lake District and see bygone landforms and glaciers, or engage in London murder mysteries, looking at evidence and hearing witnesses at various locations.

• Recommender systems on mobile phones analysed learners’ behaviours, achievements and locations to suggest the learning activity that would suit them best based on their history and context. These recommendations could be linked to assignments, resources and colleagues on their university LMS, providing guidance and practical advice. For example, in a Canadian project, there are specific applications in tourism.

• Using a system like Molly Oxford on their mobile phones, learners could be guided to the nearest available loan copy of a library book they wanted. They could also be given suggestions based on public transport, wheelchair accessible footpaths and library opening hours.

• Trainee professionals, such as physiotherapists or veterinary nurses, in various projects across Yorkshire, could be assessed while carrying out a healthcare procedure in ‘real-life’ practice. Their mobile phones would capture the necessary validation and contextual data to ensure a trustworthy process.

• Some early experiments, with Bluetooth and other forms of NFC (near-field communication), allowed passers-by or students to pick up comments or images hanging in discrete locations, such as a subway or corridor on a university campus, serving as sign-posting or street art. 

These pilots and projects implemented situated², authentic³ and personalised⁴ learning as aspects of contextual learning, and espoused⁵ the principles of constructivism⁶ and social constructivism⁷. This was only possible as far as the contemporary resources and technologies permitted. They did not, however, encourage or allow content to be created, commented on, or contributed to by learners, only consumed by them. Also, they usually only engaged with learners on an individual basis, not supporting interaction or communication among learners, even those learning the same thing, at the same place and at the same time.

So what went wrong? Why aren’t such systems widespread across communities, galleries, cultural spaces, universities and colleges any more? And how have things changed? Could we do better now?

The Downfall of Mobile Learning: What Went Wrong?

Mobile phone ownership was not widespread two decades ago, and popular mobile phones were not as powerful as they are today. The ‘apps economy’⁸ had not taken off. This meant that projects and pilots had to develop all software systems from scratch and get them to interoperate⁹. They also had to fund and provide the necessary mobile phones for the few learners involved¹⁰. 

Once the pilot or project and its funding had finished, its ideas and implementation were not scalable or sustainable; they were unaffordable. Pilots and projects were usually conducted within formal educational institutions among their students. Also, evaluation and dissemination focused on technical feasibility, proof-of-concept and theoretical findings. They rarely addressed outcomes that would sway institutional managers and impact institutional performance metrics. As a result, these ideas remained optional margins of institutional activity rather than the regulated business of courses, qualifications, assessments and certificates. Nor was there a business model to support long-term adoption. 

In fairness, we should also factor in the political and economic climate at the end of the 2000s. The ‘subprime mortgage’ crisis¹¹ and the ‘bonfire of the quangos’¹² depleted the political goodwill and public finances for speculative development work. Work that had previously and implicitly assumed the ‘diffusion of innovations’¹³ into mainstream provision. That ‘trickle down’ would take these ideas from pilot project to production line.

The Shift in Mobile Learning: What Changed?

Certainly not the political or economic climate, but mobile phones are now familiar, ubiquitous and powerful, and so is artificial intelligence (AI), also familiar, ubiquitous and powerful. Both of these technologies are outside educational institutions rather than confined within them. 

These earlier pilots and projects were basically ‘dumb’ systems, with no ‘intelligence’, drawing only on information previously loaded into their closed systems. Now, we have ‘intelligence’, we have AI and we have AI chatbots on mobile phones. However, currently, AI lacks context and cannot know or respond to the location, history, activity or behaviour of the learner and their mobile phone. Unfortunately, many current AI applications and chatbots are stateless and do not retain memory across interactions, and this represents a further challenge to any continuity.

The Possibilities of Mobile Learning: Could We Do Better Now?

Today’s network technologies can enable distributed connected contributions and consumption, enabling writing and reading. These might realise more of the possibilities of constructivism and social constructivism. They could enable educational systems to learn about and respond to their individual learners and their environment, connecting groups of learners and showing them how to support each other¹⁴. 

So, is there the possibility of convergence? Is it possible to combine the ‘intelligence’ of AI, the ‘memory’ of databases and the context provided by mobile phones, including both the learner and their environment? Could this be merged and mediated by educational expertise, acting as an interface between the three technologies, filtering, selecting and safeguarding?

What might this look like? We could start by adding ‘intelligence’ and ‘memory’ to our earlier examples.

The Future of Mobile Learning: What Could it Look Like? 

In terms of formal learning, our previous examples of the Uffizi Galleries, the Lake District, the Berlin Wall and Nottingham Castle are easy to extrapolate and imagine. Subject to a mediating educational layer, learners would each be in touch with other learners, helping each other in personalised versions of the same task. They could receive background information, ideas, recommendations, feedback and suggestions, cross-referenced with deadlines, schedules and assignments from their university LMS, all based on the cumulative history of their individual and social interactions and activities. 

When it comes to community learning or visitor attractions, systems could be created that encourage interactive, informal learning. For example, a living local history or 3D community poem spread around in the air, held together by links and folksonomies¹⁵, perhaps using tags to connect ideas, a living virtual world overlaying the real one. These systems could also support more prosaic purely educational applications, combining existing literary, artistic or historical sources with personal reactions or recollections.

Technically, this is about accessing the mobile phone’s contextual data, but sometimes other simple mobile data communications, for context. It also requires querying a relational database¹⁶ to retrieve history and constraints, and perhaps an institutional LMS, to retrieve assignments, timetables and course notes. AI can then be prompted to bring these together for some educational activity. Certainly, a proof of concept is eminently feasible. The expertise and experience of the three core disciplines are still out there and only need to be connected, tasked and funded.

Conclusions and Concerns

This piece sketches some broad educational possibilities once we enlist AI to support various earlier kinds of contextual mobile learning. Specific implementations and developments must address considerable social, legal, ethical and regulatory concerns and requirements. The earlier generation of projects might have already worked with these, privacy and surveillance being the obvious ones. Still, AI adds an enormous extra dimension to these, and there are other concerns like digital over-saturation, especially of children and vulnerable adults.

Nonetheless, this convergence of AI, contextual mobile data and educational expertise promises a future where learning is not confined to traditional settings but is a fluid, intelligent and deeply embedded aspect of our daily lives, making education more effective, accessible and aligned with individual and societal needs.

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Mobile Learning & GenAI for the Less Privileged, Refugees & the Global South

How can mobile learning and GenAI reach those traditionally left out of educational innovation?

In a recent episode of Silver Lining for Learning, an award-winning webinar and podcast series, Prof. John Traxler joined a panel to discuss how mobile learning and generative AI can support less privileged learners, including refugees and communities in the Global South. 

The episode, ‘Mobile Learning & GenAI for the Less Privileged, Refugees & the Global South,’ builds on many of the questions raised in this article. It explores how mobile technologies have and haven’t fulfilled their potential, and what role GenAI might now play in addressing longstanding educational inequalities.

Watch the full episode:

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1. There is considerable literature, including:
   Special editions: Research in Learning Technology, Vol. 17, 2009. 
   Review articles: Kukulska-Hulme, A., Sharples, M., Milrad, M., Arnedillo-Sanchez, I. & Vavoula, G. (2009). Innovation in mobile learning: A European perspective. International Journal of Mobile and Blended Learning, 1(1), 13–35.
   Aguayo, C., Cochrane, T. & Narayan, V. (2017). Key themes in mobile learning: Prospects for learner-generated learning through AR and VR. Australasian Journal of Educational Technology, 33(6).
   Edited books: Traxler, J. & Kukulska-Hulme, A. (Eds) (2015), Mobile Learning: The Next Generation, New York: Routledge. (Also available in Arabic, 2019.) 
   More philosophically, Traxler, J. (2011) Context in a Wider Context, Medienpädagogik, Zeitschrift für Theorie und Praxis der Medienbildung. The Special Issue entitled Mobile Learning in Widening Contexts: Concepts and Cases (Eds.) N. Pachler, B. Bachmair & J. Cook, Vol. 19, pp. 1-16. ↩︎
2. Meaning, ‘real-life’ settings. ↩︎
3. Meaning, ‘real-life’ tasks. ↩︎
4. Meaning, learning tailored to each separate individual learner.  ↩︎
5. Educational technology researchers distinguish between what teachers say, what they ‘espouse’, and what they actually do, what they ‘enact’, usually something far more conservative or traditional. ↩︎
6. An educational philosophy based on learners actively building their knowledge through experiences and interactions. ↩︎
7. A variant of constructivism that believes that learning is created through social interactions and through collaboration with others. For an excellent summary of both, see: https://www.simplypsychology.org/constructivism.html  ↩︎
8. For an explanation, see: https://smartasset.com/investing/the-economics-of-mobile-apps ↩︎
9. A common term among computing professionals, referring to whether or not different systems, such as hardware, software, applications and peripherals, will actually work together, or whether it would be more like trying to fit a UK plug into an EU socket.  ↩︎
10. A more detailed account is available at: https://medium.com/@Jisc/what-killed-the-mobile-learning-dream-8c97cf66dd3d ↩︎
11. For an explanation, see:https://en.wikipedia.org/wiki/Subprime_mortgage_crisis ↩︎
12. For an explanation, see: 2010 UK quango reforms – Wikipedia, which impacted Becta, the LSDA, Jisc and other edtech supporters.  ↩︎
13. For an explanation, see: https://en.wikipedia.org/wiki/Diffusion_of_innovations ↩︎
14. The proximity of physical or geographical context that the location awareness of neighbouring mobile phones could extend to embrace social proximity, meaning learners who are socially connected, or educational proximity, meaning learners working on similar tasks. The latter idea connects to the notions of ‘scaffolding’, ‘the more knowledgeable other’ and ‘the zone of proximal development’ of the theorist Vygotsky. For more, see: https://en.wikipedia.org/wiki/Zone_of_proximal_development ↩︎
15. Databases conventionally have a fixed structure, for example, personal details based on forename, surname, house name, street name and so on, with no choice. Folksonomies, by contrast, are defined by the user, often on the fly. For example, tagging with labels such as ‘people I like’, ‘people nearby’, ‘people with a car’. Diigo, a social bookmarking service, uses tagging to implement a folksonomy. ↩︎
16. Relational databases, unlike ‘flat’ databases based solely on a file, capture relationships, such as a teacher working in a college or a student enrolling in a course, and include all the various individual teachers, courses, students and colleges. ↩︎

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About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com