A Strong First Step, But Not the Finish Line

The first issue of the ELT NEWS Digital magazine has done something important. It has proven that there is an audience willing to engage with a new format, to spend time with it, and to explore what it offers.

The numbers tell part of the story. Over 12,700 users engaged with the issue, generating more than 28,000 views, with an average session lasting over five minutes. That suggests that once readers entered, they stayed.

But staying is not the same as moving forward.

Traffic sources offer another angle. A significant portion came directly, over 60%, with organic channels contributing meaningfully and paid efforts still relatively modest . This points to an existing, loyal base carrying the launch. It also quietly signals a limitation. Growth will not come from the same circle repeating itself.

Then there is the device split. More than half accessed the issue from desktop, while a substantial 42% came through mobile . That balance matters. It means the experience must work equally well across both environments, not as an afterthought, but as a design priority. A digital magazine that feels natural on desktop but slightly inconvenient on mobile will lose readers without them even noticing why.

Geographically, the audience remains anchored in Greece, but not exclusively. Nearly 42% of readers came from outside the country, with the USA and parts of Europe already present in the data . That is not expansion yet, but it is a signal. The ceiling is higher than the current reach.

So where does this leave us?

This was a good start. Better than many first attempts. The engagement time alone suggests that the content resonates. But a strong opening does not guarantee momentum. It only creates the opportunity for it. The next step is less comfortable, with sharper editorial decisions and tighter structure. Not everything deserves equal space. Not every page earns the reader’s time in the same way. And perhaps most importantly, this needs to remain a conversation, not a product delivered from one side.

If you read the first issue and something did not work for you, say it. The digital format allows us to adapt quickly, but only if we know where to look. We are building this together, whether we admit it or not.

If you have suggestions, ideas, or even criticisms, do not hesitate to reach out at dimitris@eltnews.gr

IELTS Explores AI Integration in Language Assessment

The British Council has signaled increased investment in AI technologies to support English language assessment systems such as IELTS. The organization is exploring how AI can enhance scoring consistency, reduce examiner workload, and provide more detailed feedback to candidates. Early developments include AI-assisted writing evaluation and speaking test analysis using speech recognition technologies. However, officials emphasize that human examiners will remain central to maintaining reliability and fairness. The move reflects a broader shift in the language testing industry, where AI is being integrated to improve efficiency without compromising validity. For teachers and students, this signals future changes in exam preparation, with greater emphasis on authentic communication skills rather than formulaic responses. The development may significantly reshape high-stakes English language certification.

UNESCO Highlights Risks of AI Bias in Language Education

UNESCO has issued a new warning about the potential risks of bias in AI-driven language learning systems. The report highlights that many AI models are trained predominantly on English-language data from specific cultural contexts, which may lead to biased outputs and limited representation of global English varieties. This has implications for learners developing intercultural communicative competence. The organization calls for more inclusive datasets and transparent AI systems that reflect linguistic diversity. In ELT, this is particularly critical, as English is used globally in diverse contexts. Teachers are encouraged to act as mediators, helping students critically evaluate AI-generated language and understand its limitations. The report reinforces the need for ethical guidelines and responsible AI integration in language education.

KPG Exams Continue to Emphasize Communicative Competence

The State Certificate of Language Proficiency (KPG) continues to evolve toward communicative and task-based assessment. Recent exam formats place greater emphasis on real-life language use, including integrated skills tasks that combine reading, writing, and speaking. The shift aligns with CEFR principles and reflects modern approaches to language assessment. Teachers are encouraged to prepare students through authentic communication activities rather than rote memorization. The KPG remains a key certification pathway in Greece, particularly for public sector recognition and academic use. The continued evolution of the exam highlights the broader trend toward competency-based language assessment.

Teaching in the Age of Intelligent Machines:

More Human Than Ever

By Alejandra Musalem Lechuga

Teacher of English and Spanish as foreign languages with more than 20 years of experience in higher education.

There is a moment, usually quiet, when teachers realise something has shifted. Not dramatically, not with announcements or policy changes, but through small disruptions. A worksheet appears in seconds. A student submits an essay that feels… too polished. A grammar explanation comes not from the teacher, but from a screen.

It raises an uncomfortable question. If machines can now perform so many of the visible tasks of teaching, what exactly remains ours?

The instinctive response is often defensive. Replacement. Obsolescence. Redundancy. Yet this misses what is actually happening in the classroom. AI does not teach. It produces. It predicts. It responds to prompts without understanding why those prompts matter in the first place. The difference is not technical. It is fundamental. Teaching is not the delivery of language, it is the shaping of experience around it.

That distinction becomes clearer the moment something goes wrong. A slightly inappropriate example. A task that does not quite land. A student who disengages halfway through. The machine carries on regardless. The teacher does not.

What is emerging instead is a shift in expertise. Not less teaching, but different teaching. The ability to design prompts is part of it, yes, but that alone is not enough. Anyone can type a request. The difference lies in knowing what to ask for, why it matters, and how it will play out in a specific classroom, with specific learners, on a specific day.

This is where things become less tidy. AI can generate content quickly, but it cannot judge its own appropriateness. It does not recognise when an activity feels flat, when an example carries unintended cultural weight, or when language misses the emotional tone of a group. That responsibility does not disappear. It shifts, quietly but decisively, back to the teacher.

There is also a practical tension that is not often discussed openly. The more efficient content generation becomes, the easier it is to overlook the process behind learning. Writing, for instance, has always involved friction. Drafting, hesitating, revising. Now that friction can be bypassed. The result may look better, but something essential risks being lost along the way.

And then there is the part we rarely name explicitly. The human layer.

A classroom is not just a space where language is practised. It is where confidence is built in uneven steps. Where students test identities, take risks, retreat, and try again. A machine can simulate conversation, but it cannot read the room. It does not notice the student who suddenly goes quiet or the one who is performing confidence rather than feeling it.

Teachers do. Constantly, often without realising it.

This is why the conversation around AI and academic integrity feels so unsettled. The problem is not simply whether a piece of work is original. It is whether it reflects learning at all. When output becomes detached from process, assessment itself starts to wobble. We are not just evaluating language anymore, but the invisible decisions behind it.

So teaching begins to look different. Less about providing answers, more about designing conditions. Tasks that cannot be outsourced entirely. Activities that require thinking, interaction, reflection. Not because AI is a threat, but because it exposes what was always essential.

There is no neat resolution here. Only a recalibration. The presence of intelligent machines does not reduce the importance of the teacher. If anything, it removes the illusion that teaching was ever about information delivery in the first place. It sharpens the focus. Guidance. Judgement. Relationship. Timing.

Those are features that cannot be automated. They are decisions, made in context, moment by moment.

So perhaps the question is not what remains for teachers. It is what becomes more visible now that everything else is easier. And the answer is not smaller. It is more demanding, more nuanced, and unmistakably human.

The Pronoun Shift: ‘It Made’ and the Future of Teaching

By Panagiota Papadimitriou

English Teacher

There is a moment I keep returning to. A student looks up from their screen, genuinely excited, and says, “Look what it made.” Not what I made. What it made.

That small shift in pronoun says more than any policy document or conference keynote. It quietly redraws the boundaries of authorship, effort, even pride. And it forces a harder question onto the table: if the tool is doing the producing, what exactly are we teaching?

Artificial intelligence did not arrive as a distant idea. It is already woven into the everyday rhythm of the classroom. It writes essays, explains grammar, builds lesson plans, and answers questions at a speed no teacher can match. For those of us who have spent years refining materials and sequencing learning carefully, that shift is not just technical. It is personal.

And yet, the disruption has been unexpectedly productive.

In my own practice, preparation has changed first. Tasks that once absorbed entire evenings now take minutes. I use AI tools to generate grammar theory sheets and differentiated worksheets that are clean, structured, and usable immediately. The gain is not efficiency for its own sake. It is reclaimed time. Time that goes back into the students in front of me.

Literature teaching, too, has opened up. A lesson linking Romeo and Juliet with Erotokritos became something far more immersive than I could previously design alone. Visual coherence, narrative flow, interactive elements, all built faster than expected. Then it shifts again, into something closer to a game. Students move through challenges, compete, collaborate, stay engaged in ways that traditional formats rarely sustain.

And inside the classroom, something else is happening. Energy. Movement.

Students solving language puzzles in escape-room scenarios. Writing songs to memorise structures. Laughing while working through complex ideas. None of this replaces teaching. It reframes it.

But the real tension appears when we reach writing.

“When a tool can produce a paragraph in seconds, what am I teaching when I teach writing?”

Not the paragraph. That much is clear. It is the thinking behind it. The hesitation, the selection, the decision to reject one idea and follow another. The invisible work that no generated answer can replicate. That is where the value sits now, and it is far less visible than before.

Which means some things become non-negotiable. We need to hold on to critical thinking, not as a slogan but as a daily habit. Students must learn to question what they see, not just accept fluency as truth. AI produces convincing language. It does not produce judgement.

We also hold on to relationships. Quietly, stubbornly. No system notices the student who has stopped trying because something outside school has shifted. No tool understands the exact moment when encouragement matters more than correction. That awareness still belongs to us.

And reading, real reading, becomes almost an act of resistance. Sitting with a difficult text, staying with it long enough to feel discomfort, to misinterpret, to return and try again, that process cannot be outsourced. It is slow. It should be.

Ethics sits underneath all of this, not as an abstract principle but as daily practice. Transparency, honesty about how tools are used, refusal to let convenience quietly redefine standards. The teacher is still a moral presence in the room, whether we acknowledge it or not.

At the same time, some things are simply no longer worth defending.

Memorisation as proof of understanding has lost its credibility. Access to information is instant. What matters now is transformation, what a student can do with what they find.

Authority based on knowing more is fading as well. Students can often access the same information, sometimes faster. Authority now rests elsewhere, in how we think, how we guide, how we model curiosity without pretending certainty.

And perhaps most importantly, the idea that using AI somehow diminishes the teacher needs to go. It is a false dilemma. A teacher who builds interactive tasks, experiments with format, or integrates new tools is not stepping back. They are making deliberate choices about how learning happens.

What remains, unchanged, is responsibility. Every student still needs the conditions to grow, not just cognitively, but emotionally and socially. That obligation has not shifted. No system shares it. No algorithm carries it.

So the role evolves. From delivery and answering to design and questioning.

Letter formation is often treated as a simple, mechanical step in early writing. In reality, it sits at the centre of a much more complex process. Before learners can write fluently, they need control, coordination, and a clear understanding of how letters are constructed. When this foundation is weak, difficulties tend to surface later, not only in handwriting but also in confidence and overall literacy.

What makes this more challenging is the diversity we now see in our classrooms. Some learners need repetition. Others need movement, tactile input, or visual cues to make sense of letter shapes. A single approach rarely works for everyone, especially when working with young learners or students with additional learning needs.

Letter formation becomes far more effective when learners can physically engage with it. This resource offers a hands-on approach, using playdough to help learners shape and build each letter step by step. Instead of simply tracing, learners actively construct letters, strengthening both understanding and muscle memory.

As they work, learners develop fine motor control, hand strength, and coordination, while the tactile element keeps them focused and involved. This makes the resource particularly useful for learners who benefit from multisensory input, including those with learning difficulties or attention challenges.

The pack is flexible in use. It can be introduced in whole-class settings, adapted for small groups, or used in one-to-one sessions where learners need more explicit guidance in recognising and forming letters accurately.

By Constantina Constantopoulou

BA English Language and Literature, MEd Special Education, MSc (cand.) Education Management and Leadership

A Practical Guide to Letter Formation: Hands-On Activities That Work

You walk into your class with a plan. Not just any plan, a proper one. Pre-reading, while-reading, post-reading. Skimming, scanning, gist, detail. You’ve got it all lined up.

Then you look at them. Half the class hasn’t opened the book. One is staring at their phone under the desk. Another is reading, technically, but you can tell nothing is going in. You ask a simple question about the text. Silence. Or worse, random guesses pulled from isolated words.

So let’s ask it plainly. How the heck do you teach reading to students who don’t actually read?

Let’s get one thing out of the way. The problem is not that students can’t read. Most of them can decode words just fine. They can pronounce, they can recognise vocabulary, they can even follow instructions.

What they don’t do is engage. They don’t slow down. They don’t build meaning. They don’t sit with a text long enough for it to say anything. Reading, for them, is either a task to complete or something to avoid. There’s very little in between.

And we don’t help. We give them strategies. We train them to hunt for answers. We show them how to eliminate distractors and match keywords. It works, up to a point. They pass tests. They tick boxes. Everyone moves on.

But strip away the questions, and they’re lost. So if the usual approach isn’t working, what do you do? You stop starting with the text. That’s the first shift.

Instead of opening the book and saying “read this”, you build a reason to read. Bring in something real. Not simplified, not perfectly graded, not polished for exams. A short article, a post, a headline, even something slightly messy.

Something they might actually react to. You don’t explain everything upfront. In fact, resist that urge. Highlight a few words, sure. Let them guess. Let them get it wrong. That confusion is not failure, it’s the entry point. Then comes the uncomfortable part.

You slow things down. Not everyone likes this. It feels inefficient. It looks messy. Students hesitate, ask questions, lose track, go back, disagree. Good. That’s reading.

If they finish in two minutes, they didn’t read. They scanned and escaped.

Give them space to go back to the text. Not because they have to answer something, but because they need something from it. A piece of information, a reason to support an opinion, a detail to prove a point.

Now the text has a job. You can still use structure. You’re not abandoning everything.

But instead of “find the correct answer”, try:

• group ideas

• match statements to opinions

• decide what the writer really thinks

• argue with the text

And sometimes, just ask them what annoyed them. You’d be surprised how quickly engagement appears when students are allowed to disagree.

Here’s the part that most teachers don’t like hearing. Progress will look slower.

You won’t “cover” as much material. You won’t fly through units. Some lessons will feel like they went nowhere.

What’s happening is less visible, but far more important. Students are starting to process language instead of jumping over it.

And eventually, something shifts. They stop asking, “Which paragraph is the answer in?” They start asking, “What does this actually mean?”

That’s the moment you’ve been aiming for. Not perfect answers. Not exam tricks. Just real reading.

Because in the end, you’re not teaching them how to pass a task.

You’re teaching them how to stay with a text long enough for it to matter. And honestly, that’s the hard part.

How the Heck Do I Teach Reading to people who do not read?