AI fits like a glove in EFL and second language teaching

“AI is your friend, not your enemy” was the title to my Keynote. That statement can sound provocative in a hall full of language teachers. We have all seen the headlines warning that translation tools are destroying motivation, that chatbots are replacing writing, that students are outsourcing their thinking. It is understandable to feel cautious. Yet when we look at the evidence and, more importantly, at what is actually happening in classrooms and with actual teachers and language learners, a more balanced and optimistic picture emerges.

There is a persistent fear that instant translation will remove the need to learn another language. If a phone can translate, why struggle with grammar, vocabulary and pronunciation? And yes, some learners report that translation tools reduce their motivation, but most do not. The majority remain motivated to learn languages even while using AI tools. Large international surveys of teachers show that human-led language learning remains irreplaceable, and scepticism that AI will replace teachers any time soon. Motivation has not collapsed. It has shifted. The environment has changed, and our role as teachers is not to resist that environment but to shape how students learn within it.

The reality is that students are already using AI, and many are using it daily. They practise speaking and receive feedback, summarise texts, brainstorm ideas, generate content, build flashcards, simulate exams and converse with chatbots. For many learners, AI has quietly become a personal assistant. Ignoring this will not make it disappear. Harnessing it, however, can transform our teaching.

One of AI’s most powerful contributions is psychological. Language learning is emotionally demanding. Students fear embarrassment. They worry about making mistakes. They hesitate to speak. AI provides a non-judgemental partner that is infinitely patient. It offers immediate help, avoids public correction, reduces anxiety and gives affirmative feedback. Learners can rehearse privately before speaking in class. For shy or anxious students, this is not a minor advantage; it can be transformative. Increased confidence often leads to increased engagement and engagement is the engine of progress.

The research evidence supports this. I showed the two major teta-studies examining AI tools in English language learning that report significant improvements in achievement. Reviews of AI-powered chatbots for speaking practice describe strong effects on oral proficiency, interaction and motivation. We are not discussing novelty tools; we are seeing measurable impact. When AI enhances interaction, it enhances language learning, because language learning is interaction.

Voice-based AI is particularly powerful for EFL. A learner can say, “Be my Spanish tutor at B1. Speak slowly, use everyday topics and stop often with questions,” or “I’m A2 French. Keep sentences short and correct me after I finish.” They can switch between languages, practise hotel check-in roleplays, request dictation, drill minimal pairs such as “ship” and “sheep,” practise connected speech like “Whaddaya wanna do?” or complete article drills choosing between “a,” “an,” “the” and zero article. The feedback is immediate and personalised. Each learner can operate at their own level, repeat as often as needed and progress at their own pace. This is differentiation without leaving anyone behind.

AI fits beautifully with Papert’s principle of high ceiling, low floor and wide walls interface. A beginner can have simple, structured conversations – low floor An advanced learner can debate ideas, analyse arguments or rehearse professional interviews – high ceiling The same tool accommodates a wide range of ability levels and creative directions – wide walls.

There is also a deeper pedagogical reason why AI works so naturally in language teaching. From Socrates onward, learning has been understood as dialogue. Socratic questioning draws out thinking. Bakhtin emphasised that meaning emerges through multiple voices in interaction. Vygotsky described the “knowledgeable other” who mediates learning within the learner’s Zone of Proximal Development. AI does not replace the teacher, but it can function as an additional knowledgeable other, available whenever the learner needs it. The teacher remains the orchestrator of learning.

Many teachers are already experimenting. They use AI to create lesson plans, generate materials, personalise exercises, design assessments and increase student engagement. At the same time, many feel underprepared. This is not a reason to retreat; it is a reason to invest in professional development. Teachers need permission to prompt, and students need permission to prompt wisely.

Assessment is another area where AI fits naturally. It can analyse spoken presentations, generate targeted feedback, turn transcripts into personalised error-based flashcards, create grammar drills based on actual student mistakes and help design video-based assessments. What once required hours of marking can now become rapid, formative feedback. Used thoughtfully, AI strengthens rather than weakens assessment for learning.

Perhaps most inspiring is the global dimension. AI systems now support hundreds of languages, including many that have historically lacked digital presence. Initiatives focused on low-resource languages aim to reduce language inequality in technology. Oral traditions can be digitised through mobile devices. Minority languages can gain visibility and vitality online. AI is not simply serving English; it is expanding linguistic possibility.

So where does that leave us as teachers? Precisely where we have always been: at the centre. AI cannot build human relationships. It cannot replace cultural nuance, empathy, humour or inspiration. It can amplify what we do. It gives learners rehearsal space, lowers anxiety, personalises practice, provides role-plays,  and multiplies exposure to language. It gives teachers creative leverage and new forms of feedback. It extends dialogue beyond classroom walls.

AI does not diminish language teaching. Used well, it strengthens it. It fits like a glove. The glove does not replace the hand. It enhances what the hand can do.

US to EU software: you can’t regulate people into better tech

The most obvious problem with a European social media platform and software migration, touted by the EU, is that a political stance is not enough to shift the dial. I’d also like to explore the other reasons for expressing doubt. Network effects don’t care about your values, which is why a ‘European alternative’ rarely become mainstream.

A political push will attract the Europhile fanboys and girls, so become a European BlueSky, a blue bubble of insufferable conformity to the view of almost everyone, part from those who are more libertarian, which is a vast number of even European citizens. Activism and a skewed political push are never wise when it comes to neutral platforms.

The EU’s tech problem isn’t politics,  it is product. We saw this with the European Mastodon and the Fediverse, both disastrous in terms of user experience and never mainstream, despite the efforts to push Mastadon as an alternative to X. BlueSky, is US owned, and has turned into an echo-chamber for those who don’t like X and Musk. These sprove how difficult it is to shift users from the incumbent platforms.

Quality matters

Quality has to be superior to effect a large migration, yet most of the options on the table are significantly inferior to their non-European comparators. There’s a good reason why many have a tiny fraction of users compared to US tech; they are more expensive, unreliable, have poor security and above all have much poorer functionality. 

Europe can’t migrate its way out of big tech

Consumer inertia also means that people stay with what they’ve got, as they are familiar with the platform. You can leave big tech, but your friends won’t. Then there’s the cost of change, losing your past data, friends and have to relearn a new platform and build again from scratch. It’s not often a monetary but time and social cost. But the biggest barrier is the network effect, where you are literally enmeshed in a vast network of friends , others and potential viewers. That’s hard to leave, like leaving home.

Facebook has faced endless claims of imminent death and decline. It has not happened. Network effects and the fact that we are creatures of habit, with a huge backlog of posts and memories on the platform, means we stay. We are not only creatures of habit, we are creatures of memory and identity. 

There’s a reason WhatsApp has not been replaces by Signal and Telegram. Millions said they’d leave but they didn’t. Platforms are sticky and when everyone else is on WhatsApp, you have to disentangle all of your existing social groups, and start again.

Organisational software

With organisational software, most European companies of any size use Teams. This will be fiendishly difficult to shift as they need to reduce risks on cybersecurity, outage and reliability. It is often a procurement as part of a wider Microsoft ‘house’ project. People complain about Outlook, Teams, Office, but the alternative, LibreOffice is worse. There’s also Google Workplace, which has oodles of great functionality. Google has a global revenue model, a massively integrated ecosystem, including its own operating system. It dominates search, video through YouTube and global maps with street view. There is no European entity that comes close to this. Both are solid as they have well-used file formats, are embedded into existing companies and their workflows, and people are familiar with their user interfaces and expected functionality. This inertia is hugely expensive to migrate to other platforms.

One way to push this European initiative is to mandate change in public institutions. But this may only result in even lower productivity in the public sector, compared to the private. That’s the last thing a stagnant economy needs.

Killer objection - AI

But the killer objection, for me, is the failure of Europe to embrace and utilise AI. This could have been a chance to break the US dominance but it is too late. Mistral is good but open source and now a bit player. The US and China are now unstoppable. More worrying is the failure of European companies to embrace AI within their own tech-stacks.

One exception

There is one exception to the rule, that’s Sweden’s Spotify. I’d have said SAP but their shareprice has dropped by a third in six months and they now look like a wounded, if not dying entity, under attack from those who can quickly code alternatives.

Good intentions, bad software

You can’t just legislate them out, there has to be choice, and as long as there is choice European tech companies are largely, but not entirely, screwed. Google, Meta, Microsoft, Amazon, Apple, OpenAI and Anthropic dominate in scale and global reach. Most of those major platform players are US firms but operate extensively in the EU and are regulated locally under laws like the Digital Markets Act, and include WhatsApp, Facebook, Instagram, LinkedIn, TikTok and YouTube.

Europe’s tech strategy is full of good intentions, but bad software. Most of the options are significantly inferior to what is available today. Using outdated technology will only set Europe even further back, through a clear drop in productivity, first in migration, then in poorer functionality and support.

AI Hierarchy of Expertise

There is a widening hierarchy of participants and drivers in AI. This is not unusual, as almost all technological revolutions show this type of fractional distillation.

1) AI creators (working on models & other source techniques)

2) AI experts (implementing structured projects and products)

3) Experts-in-the -loop (using tools like Claude code etc)

4) Humans-in-the loop (using chatbots)

5) Humans who don’t use AI

It is particularly acute in AI, as there are a huge conceptual and competence gaps between these different types of participants.

AI creators

Those who work in the large foundational model companies, such as OpenAI, Google, Anthropic, Meta and others are experts both in the fundamental mathematics and techniques. It is a mistake to focus entirely in the training of models here as the field has widened out into advanced techniques before and after training that are proving effective.

This is the most opaque group, as the surrounding system that makes those models useful, reliable and economical  viable in the real world are highly technical. Companies now rely on techniques to ground responses in external knowledge, tool use and function calling to let models act rather than just speak. They use agentic architectures to plan and execute multi-step tasks and long-term memory to maintain context and personalisation across sessions. There’s also serious evaluation and guardrails, synthetic and curated data pipelines, inference optimisation to cut costs and latency, even human-in-the-loop training. The centre of gravity has shifted to these more mature enhancing techniques because real value now depends on accuracy, trust, controllability, cost and integration into workflows. These problems that are solved at the system and product level rather than in the training run.

AI experts 

These are the companies and researchers who are building the next layer, on the basis of the fundamental technology. They may enable model orchestration, routing tasks across multiple specialised models as a service to customers. They are also creating vertical products in law, healthcare, training, education etc.

Many are involved in RAG and similar techniques to create useful projects and products across and within organisations. This is similar to the growth of internet companies in the early 2000s on top of the internet, now a massive portion of the world economy. I’ve been involved in this layer for over a decade. It’s pioneering, exciting and difficult but without this layer of experimentation the core technology would remain aloof and unusable. It takes commitment by CEOs and a real push to get through prototypes to product.

Experts-in-the-loop

As the technology matures a new group emerged which used more advanced techniques and tools, often the ‘pro’ models. This can be the sophisticated use of AI to handle and use data. But the dominant use has turned out to be coding and on a wider scale media production (text, graphics, images and video). My book on AI and Productivity’ explains why this emergent group is the key to solving the productivity problem and overcoming productivity paradoxes using AI.

Experts bring domain knowledge, this is usaally a mix of tacit and explicit knwoe3ldge and skiils that allow them to get the best out of the available tools. Coders make goof users fo Clade Code, graphic artists of image creation tools and will video creators in creating video. Experienced researchers will get the best out of the technology for research and so on.

Humans-in-the loop

By far the largest group are the ordinary users. One could split them into the two lower fremium groups of paid and free users but both sit on a rising tide of quality and functionality. They tend to single promtt and use the technology as a form of performance support, namely solving an immediate problem; finding something they don't knopw, communications issue, summarising, translating, rewriting and so on. They tend to prompt simply and as the technology improves get more and more bang for their buck.

Humans who don’t use AI

This last group are the disinterested or those who have no real need, which is fine. It also includes those who actively dislike the technology. They have all sorts of objections, some rational, others irrational and ill-informed. This is where the biggest, some say yawning, gap exists.

Least informed but loudest are this last group, at least on social media. They often invoke an eye-roll from the rest, not because they don’t have a point but because the claims are often unsubstantiated or just plain wrong. Typically they will refer to content from old models, rely on anecdote and make claims about the technology hitting brick walls.

Integration

There are people who find ways to integrate AI into organisational culture. They follow the hierarchy, as it depends on the degree of integration. Most organisational institutions focus on encouraging 'humans-in-the-loop' activity, with but this caps your productivity gains at mere text search. Capping effort at 'share prompts' and AI literacy courses, is to neither understand the capabilities of current AI, nor make serious organisational gains in productivity.

Conclusion

We should not be surprised by any of this. The fractional distillation into these sedimental layers of expertise is both a normal and necessary part of the evolution of a technology. In truth, as the technology matures and improves, this hierarchy tarts to massively expand in the middle layers, while the bottom layer shrinks. Think printing, cars, television internet. In this sense resistance is futile.

Ultimately the hierarchy turns in to fully realised rout and it becomes a normalised consumer product woven into the fabric of our lives. That will happen faster tyan most people think, within a few years. It is a hierarchy that automates, thereby blurring the lines, flattening it out and dropping the doubters.

At this stage of AIs development, only 3 years in, the last group is still the largest and therefore the loudest, that does not mean it is the most significant, merely the least informed and practiced.