OCR for Historical Newsprint: Four Models Worth Running Locally in LM Studio

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If you work with scanned, typeset documents from archives like the British Newspaper Archive, you will know the frustration of running standard OCR tools on material they were never really designed for: degraded print, Victorian column layouts, eccentric typography, and occasionally deliberate non-standard spelling.

You can leverage the power of local AI models, however, to automate this process, and with free inferencing software like LM Studio, the learning curve isn’t at all steep. Below, I take a look at four specialist OCR models you can run entirely locally using the package – and why you might prefer doing so over handing your documents to a web service.

Why Run OCR Locally?

There are some truly excellent web-based OCR services. There’s Transkribus, for instance, which is widely used in the academic community. Tool like this are powerful and convenient, but they come with some real trade-offs:

  • Privacy: Your document images leave your machine and are processed on someone else’s server. For sensitive archival material or unpublished research corpora, that matters.
  • Cost at scale: Processing hundreds or thousands of newspaper pages through a paid API adds up quickly.
  • No customisation: Cloud OCR engines don’t always offer many pipeline options. You cannot instruct them to preserve dialect spellings, flag ambiguous characters, or respect the orthographic conventions of a specific historical variety of English.
  • Reproducibility: Web services update their models silently. A corpus processed in 2024 may produce different output if you re-run it in 2026. A local model stays consistent – important for methodological reproducibility.

Running OCR-trained models in an inferencing software like LM Studio removes most of this friction. The program handles multiple model download and management through a clean interface, and also allows you to customise model settings, up to the inclusion of system prompts that persist across sessions. For historical document work, that means you can instruct the model once about the linguistic conventions of your material and have it apply those rules to every page you send it.

The Four Models

1. OLMOCR 2 (7B) — Best Overall for Documents

Developed by the Allen Institute for AI (Ai2), olmOCR 2 is built on Qwen2.5-VL-7B-Instruct and fine-tuned using reinforcement learning with unit-test rewards specifically targeting document OCR tasks. It is one of the few models designed from the ground up for this use case rather than adapted from a general vision assistant.

Size: 7 billion parameters. Available as a ~4.7 GB GGUF (Q4 quantisation) or ~8.85 GB at Q8. Needs around 5–10 GB RAM depending on quantisation.

Why it works for newspaper archives: Handles multi-column layouts, mixed content (tables, headings, body text), and degraded print reliably. Scores 82.4 on olmOCR-Bench. It responds well to system prompt instructions, making it a strong candidate for dialect-preservation workflows.

LM Studio: There’s a GGUF in the native catalogue – search and download directly in the app.
🔗 lmstudio.ai/models/allenai/olmocr-2-7b-1025

✅ Pros: Best-in-class document OCR accuracy; strong layout understanding; instruction-following is reliable; native LM Studio support.
❌ Cons: 7B means slower inference on modest hardware; not ideal for rapid bulk processing.

2. NANONETS-OCR-S — Clean Catalogue Option

Developed by Nanonets, a document AI company, this model is also based on the Qwen2.5-VL architecture but fine-tuned specifically on structured document extraction tasks including forms, invoices, and archival print.

Size: Approximately 7B parameters, similar footprint to olmOCR 2. Available directly via the LM Studio model catalogue as a GGUF.

Why it works for newspaper archives: Strong on structured layout extraction and clean Markdown output. Useful when you want transcription that preserves document structure (headings, columns, captions) as well as raw text.

LM Studio: Native catalogue – findable by searching “Nanonets” in the model browser.
🔗 lmstudio.ai/models (search: Nanonets-OCR-s)

✅ Pros: Easy one-click setup; good structural output; reliable on clean and moderately degraded scans.
❌ Cons: Less tested on heavily damaged historical material than olmOCR 2; similar hardware demands.

3. DOTS.OCR (1.7B) — Best for Complex Column Layouts

Released by Rednote (小红书) in late 2025, dots.ocr is a compact 1.7B vision-language model that combines layout detection and text recognition in a single pass. Unusually for its size, it explicitly predicts reading order — the sequence in which text blocks should be read — which is critical for Victorian newspaper pages where columns can be irregular and text wraps around illustrations.

Size: 1.7 billion parameters; approximately 2 GB as a GGUF. Runs comfortably on 3 GB VRAM.

Why it works for newspaper archives: Reading order prediction alone makes it worth considering for multi-column broadsheet layouts. Supports over 100 languages, outputs JSON, Markdown, or HTML, and benchmarks show Table TEDS accuracy of 88.6% — ahead of Gemini 2.5 Pro on that metric.

LM Studio: Load via HuggingFace GGUF import (paste the HuggingFace URL into LM Studio’s search bar).
🔗 huggingface.co/dotsdocx/dots.ocr-1.7B-GGUF

✅ Pros: Tiny footprint; reading order detection; fast; strong on multi-column layouts; multilingual.
❌ Cons: Smaller context window means system prompts may drift on very long sessions; can hallucinate on heavily degraded scans; not in the native LM Studio catalogue.

4. GLM-OCR (0.9B) — Best for Bulk Processing on Modest Hardware

Released by Z.ai (Zhipu AI) in early 2026, GLM-OCR is built on the GLM-V encoder–decoder architecture and fine-tuned exclusively for OCR. At under 1 billion parameters it is the smallest model here, yet it scores 94.0 on OCRBench and 93.96% Table TEDS accuracy – results that comfortably outperform much larger general-purpose models.

Size: 0.9 billion parameters; approximately 1 GB quantised (Q8). Needs under 1.5 GB VRAM – it will run on almost any laptop made in the last five years.

Why it works for newspaper archives: Speed and low resource use make it ideal for processing large batches of pages. It is not a chat model — it takes an image and outputs text, triggered by the phrase Text Recognition: — so it is best suited to pure transcription pipelines rather than interactive use.

LM Studio: Load via HuggingFace GGUF import using the ggml-org GGUF repository.
🔗 huggingface.co/ggml-org/GLM-OCR-GGUF

✅ Pros: Tiny; fast; runs on minimal hardware; excellent accuracy for its size; good for bulk workflows.
❌ Cons: Not a chat/instruction model — no system prompt support for dialect customisation; requires a separate layout detection step for complex multi-column pages; not in the native LM Studio catalogue.

Quick Comparison

Model Size (GGUF) VRAM LM Studio Route Best For
olmOCR 2 (7B) ~4.7 GB 5 GB+ Native catalogue Best accuracy, complex layouts, dialect workflows
Nanonets-OCR-s ~4.7 GB 5 GB+ Native catalogue Structured document extraction, clean output
dots.ocr (1.7B) ~2 GB 3 GB HuggingFace GGUF import Multi-column layouts, reading order, low VRAM
GLM-OCR (0.9B) ~1 GB <1.5 GB HuggingFace GGUF import Bulk processing, minimal hardware

A Practical Workflow for Newspaper Archives

For a large corpus like material from the British Newspaper Archive, a two-tier approach works well. Use GLM-OCR for the bulk of clean, well-preserved pages – it is fast and accurate enough for standard 20th-century newsprint. Then escalate difficult pages (damaged, illegible columns, unusual typefaces, pre-1880 material) to olmOCR 2 for a more careful second pass. If column order is scrambling your output, switch to dots.ocr for those pages specifically.

For dialect writing research – where you need the transcription to preserve non-standard spellings rather than silently normalise them – load olmOCR 2 or Nanonets-OCR-s and write a system prompt that explicitly instructs the model to treat all orthographic choices as intentional. That single step does something no traditional OCR engine is capable of: it makes the tool linguistically aware of your material.

All four models run fully offline once downloaded. No subscription, no API key, no usage limits — just your hardware and your documents.

The GLM-OCR model running in LM Studio, transcribing a 19th-century newspaper article

The GLM-OCR model running in LM Studio, transcribing a 19th-century newspaper article

Eurovision 2026: Languages, Lyrics and Understanding

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It’s something of a cheat week on Polyglossic this week. I’d normally write a blog on language diversity in the upcoming Eurovision Song Contest around this time. Yes, it’s that time of year again! But the boss asked first, so I’ll point you in the direction of the ESC article I penned for Linguascope this week. There’s a round-up of the songs – from a classroom teacher point of view, of course (not that the glam has no part to play), along with some fun game ideas.

As I’ve said there, it’s no secret that the contest has had a tough ride lately, falling into a highly contested, politically charged space. And that’s to be expected, as a living, breathing event out there in a very complex world. There are simply no easy answers that reconcile the many hues of opinion in the fan community. The most we can do is respect others’ points of view and their right to express them.

For my part, I’m on the side that believes there’s something worth fighting for there. Eurovision is a unique event that has celebrated different, not mainstream, quirky – whatever you want to call it. For kids (and adults) who feel some or all of those things, that’s always been a very special place to escape to.

Superfan Matti Bunzl summed it up well to Austria’s Profil magazine this week. He’s the director of the Wien Museum, host site for this year’s Eurofan House by wiwibloggs. Matti explains:

Warum soll der Song Contest von solchen Dynamiken ausgeschlossen sein? Natürlich kann man wünschen, dass sich die Welt für ein paar Stunden auf völkerverständigenden Eskapismus einigt, aber man kann die Menschen ja nicht zwingen.

(Why should the song contest be excluded from such dynamics? Of course you can wish for the world to unite for a few hours of international, understanding escapism, but you can’t force people.)

Profil, Saturday May 2nd 2026

If you are following the shows next week, then have a wonderful Eurovision. May the best song win, and long live healthy argument about whether it really was the best song!

Forever the Optimist : Hugo’s In Three Months Language Courses

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I loved Hugo’s In Three Months books as a language-obsessed teenager. Slim volumes, tiny, dense chapters… And of course, that promise that you’d reach some level of fluency in a quarter of a year.

I’ve collected a fair few of them over the years. My first was Italian in Three Months, of Hugo’s late-80s dark blue incarnation. I bought it ahead of a school wind band trip to Venice, and probably made my way through about a third of it before the trip was over, I packed my tuba away, and faddishly drifted onto the next project.

They’ve had a much longer history than that. Hugo’s Language Learning Books pops up in 1950s UK, and quickly starts churning out titles for speedy learners. Like other language series in the mid-20th Century, they expanded their Simplified System rapidly across languages, and became bookshop staples.

Expansion, incorporation and multiple reincarnations

By the end of the 80s, they’d already moved well beyond French, German and Spanish. The second-hand trail on eBay shows that spread, with the publication of courses from Arabic to Scottish Gaelic (still a solid reference for Gaelic grammar if you can get a copy). Curiously, the first foray into Japanese dropped the ‘in three months’, instead going for Japanese Simplified – the confidence wobble didn’t last long, as Japanese in Three Months is the title that made it into the 90s!

The In Three Months books have never really gone away. The series was picked up by Dorling Kindersley (DK), gaining a splash of the prototypical DK colour and gloss. While the range of languages is a little shrunken now, they’re still going strong, now as part of Penguin’s catalogue. Gone are the boxes of cassettes, replaced by online audio. But the familiar format remains: tight, reference-style chapters giving that “all the essentials for very busy people” vibe.

It’s in that spirit I picked up one of the new editions recently – appropriately, Italian in Three Months again, this time in its fancy new green sleeve. It’s a refresher ahead of a trip to Milan for a conference, and the perfect choice for that – not too heavy (for the suitcase or the reading).

But coming full circle like this takes me right back, and I can sense the excitement I felt in that titular promise all those years ago. Somehow, that promise still works. Long live the In Three Months series – may they continue to lure language nerds to their next obsession!

Michel Thomas on Tap : Language Courses for Spotify Premium Users

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I’ve always been a bit baffled by how un-trumpeted the Michel Thomas courses are. For sure, they do pretty well – they’ve been around in some form or other since the 80s, and have made the transition from cassettes, to CDs, to digital. But I rarely see them as a first-choice recommendation in polyglot circles.

Which is a shame, because there’s something quite uniquely effective about the approach of Thomas and educators like them (Paul Noble has carried that torch admirably well, too). They use what you might call a ‘modified Socratic’ approach to language tuition. Each course follows a teacher-student conversational format that builds language knowledge with gradual exposure and prompting. As the listener, you are the third person in the room, answering and learning along.

If you were seeking a fancier term, then perhaps structured elicitation is the one. And it works. Especially for getting into a new language – I’ve used them as intros to several languages, and the stuff really sticks. Perhaps its usefulness tapers off at more advanced levels, but the format is such a great stepping stone.

Michel Thomas on Tap

In any case, I was browsing Spotify for podcasts and audiobooks the other day. As a premium subscriber you get free access to a certain amount of premium content a month, and as I’ve recently ditched Audible (part of my ecosystem economy drive), I was interested in what was available. And there I spotted them – the entire catalogue of Michel Thomas courses (and Paul Noble!).

That’s quite a cache of premium language learning content. Years back, I paid a small fortune for those CD packs. And they’re all there, from the foundation, to the intermediate, and even the vocabulary builder courses, with plenty more titles added in the meantime. Needless to say, I’ve added the Hindi one to my current playlist as I fancy a dabble. If you’re up for the same, you’ll be surprised how much is available!

While I’m at it, let me give LanguageTransfer a big shout out, too. It uses the same techniques as the courses above, but is a personal labour of love for creator Michalis Eleftheriou, and completely free! His Greek course in particular is a resource I’d recommend to anyone to start learning that language.

Gramophone Language Courses: The Original Multimedia Learning

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If you’ve ever wondered about the origins of the multimedia language course, then some newly published archive material might surprise and intrigue you. The British Newspaper Archive recently added the early 20th-century title Sound Wave magazine to its growing catalogue. This record review title served phonographic fans from 1907 to 1933, and it’s surprisingly full of language learning history.

In those days, of course, it was the gramophone that reigned supreme. Recordings on the new flat disc format had been around since the late 1880s, but by the first decade of the 20th century, gramophones had become affordable enough for middle-class households. Sound Wave dates from that early tech spread, the publishers no doubt spotting a gap in the market for listening recommendations.

Only it wasn’t just music. What we’d now recognise as audiobooks was already in circulation – outfits like The Talking-Book Corporation were pumping out gramophone literature for adults and kids. There were elocution resources for improving one’s spoken English, too. One particularly enticing release was this special set of discs with the voice of Bernard Shaw himself (life imitating art – his own art!).

And language learning was there from the start too, in the form of regular ads from the Linguaphone Institute.

A 1907 advert from the Linguaphone Institute in the magazine Sound Wave

A 1907 advert from the Linguaphone Institute in the magazine Sound Wave

Linguaphone – a brand built on gramophone

Linguaphone is a real heritage brand for language learners, and pops up all over the newspaper archives. It started up in 1901, and is still going today – you may have come across their language training centres. Competition may have widened since then, but for over half a century they were the first word in audio course materials.

Testimonials in this 1927 edition vouch for their success. One C.B. of London reports that the Spanish course made travels “much easier and cheaper than they would otherwise have been”. A reviewer in 1929 praised the “French as it is really spoken” in a dialogue set in a hairdresser’s, on record no. 21 of that set. Yes, record 21 – these sets could run into dozens of discs, and usually shipped in a hefty, solid case.

No wonder they came with an equally solid price tag. In 1907, a box would set you back £3 and three shillings, easily several hundred pounds in today’s money. You can still pick them up second-hand today, and for much less – a lovely bit of language learning history.

Proto Language Lab

Beyond the well-heeled turntable owner, the gramophonic method wasn’t just for individuals; it was used in classrooms too. In 1914, a Leicester teacher, Mr. Cunfliffe, introduced records into his lessons at the Working Men’s College, to great success. One particularly modern-seeming innovation of Mr. Cunliffe’s was the provision of “24 pairs of hearing tubes” for the students! In this way, one element of language teaching that seems so late 20th-century, so proto language lab, had its roots decades before tape reels and CDs.

The BNA‘s inclusion of Sound Wave offers some lovely insights into the history of language learning and teaching. There’s doubtless much more to find in there. Let me know in the comments if you come across any other gems!

Screenscot of Cell to Singularity, an immersive casual clicker game available on Steam.

Cell to Singularity : Casual Play for TL Immersion

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Osmosis isn’t just for cells – it’s for language learners too! Soaking up target language simply by placing it in your everyday line of sight is one of the most effective strategies for fluency. From your instagram feed to cosy telly-watching, consolidation can be about throwing more of the things you love in your way.

Gaming is another entz stream that is really easy to target language-ify, since many titles have multiple language options. The Steam platform is a particular goldmine here – a huge multi-platform marketplace, with loads of free-to-play offerings. The trick is to find quite text-heavy games with dialogue and interactions, exposing you to as much content as possible in-play. There’s honestly something for everyone here, from word games to fully-fledged RPG.

This week, I chanced across a casual clicker on Steam that has been working its quiet way into the hearts of users since its inception in 2018. It’s Cell to Singularity, a game that simulates the blossoming of life on Earth, from eukaryotes, to jellyfish, to humans (and beyond). It’s the kind of game you can have running inconspicuously in the background while you work, slowly developing and growing like a bonsai that needs occasional tending. Very Zen.

Screenscot of Cell to Singularity, an immersive casual clicker game available on Steam.

As you can see from the screenshot, it’s also a great way to revise the building blocks of life. That’s the root educational application the game has been feted for, covering evolutionary biology in a fun, laddered way. Switching my interface to German gives me a ton of fun natural world vocab.

Beyond word level

But the game is also full of conversational exchanges you have with the ‘supercomputer’ running your life simulation, as well as Wikipedia-style descriptions of all your finds. In short, it supports word, sentence and text-level language skills in a rich, engaging environment. What more could you ask for?

Screenshot from Cell to Singularity showing dinosaurs

The range of languages available right now is already impressive. Not only the ‘mainstream’ school ones, but also Korean, Japanese, Polish and Portuguese, amongst others.

Screenscot of the language options in Cell to Singularity, an immersive casual clicker game available on Steam.

Cell to Singularity currently has an 89% positive rating from thousands of Steam users. I wonder how many of them are playing to improve their target language? Hopefully I’ve enticed a few more of you to do just that!

Escaping the Ecosystem : AI Edition

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We live in such unexpected, shifting, fracturing geopolitical times just now. A stability taken for granted for decades no longer seems a given. So much so, that many have begun to question the global tech ecosystem we are embedded in, considering the safety of our data and workflows, and seeking less exposed, closer-to-home alternatives.

It’s something we can explore without straying into conspiracy territory, and it goes beyond data security. Tech writer Cory Doctorow has written at length on the downsides to walled garden platforms that make leaving costs high while degrading (or enshittifying – Macquarie Dictionary’s 2024 word of the year) their services. Linguaphiles should know – our own beloved Duo is one of them. It’s a compelling argument, and one that national consumer protection agencies are starting to incorporate into policy. The notion that we can take meaningful steps to decouple from tech monopolies is beginning to take hold.

Ecosystem creep : AI

This leads us to AI firms – arguably the fastest growing of tech behemoths, whose services nonetheless are working their way into many of our workflows. It’s not all doom and gloom here, though; Anthropic in particular has emerged as one US company willing to stand up for an ethical stance in the field.

That said, most European LLM traffic still goes down that American route, collecting on servers users’ states have no jurisdiction over. Users come to rely more and more on these services for key elements of their day-to-day, although have little control over their place in that ecosystem.

So what to do? LLMs are incredibly useful tools for a number of creative applications. For language teachers, they are particularly good at creating authentic-sounding materials for worksheets. In fact, I’ve often argued that LLMs are a tech almost tailor-made for language learning and teaching – in few other fields is the language structure more important than the actual content! They’re genuinely brilliant at creating copy, often highly nuanced, for learning.

AI Swaps

Well, one quick and easy swap is Le Chat by French AI company Mistral. It’s a ‘full fat’ LLM on a par with the big US names, running your prompts remotely on a multi-billion parameter model. Not so remote, though – their server activity remains within EU jurisdiction.

Then, of course, there is the ‘peak privacy’ option – running your own LLM. That’s a lot easier than it sounds, thanks to easy-setup software like LM Studio or Ollama (both US-based projects, but run locally on your own machine). Install, download a model, and prompt away. While few (to no) people will have the hardware to run full-sized LLMs, small models are getting better and better, rivalling the biggies for everyday use.

Google’s Gemma 4 is a case in point, a new small model (you can get a sub-20gb version) achieving some really impressive benchmark scores. Multi-language support is one of its strengths, and believe me, it does more than a good enough job of worksheet authoring and lesson planning. And it comes with an extra ‘externalities’ bonus, too – the only energy it’s using it your laptop battery, rather than spinning up some red-hot servers on a remote farm somewhere.

That has to be a win-win – using open source releases from the industry leads, without getting trapped inside the matrix.

We may have little control over geopolitics. But there are always choices when it comes to our exposure to it in the tech we use. I’m working on a list of these swaps as part of my own digital hygiene plan, and hope to share much more of this in coming weeks!

The Victorians Who Wanted Welsh to Die

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Supporters of Britain’s Celtic linguistic heritage will be feeling optimistic glee this week, with the excellent news that Welsh learning is booming. While a Welsh revival leads the way, there are hopeful signs elsewhere, too;  the ‘I have some Gaelic’ crowd has doubled over the last ten years.

If the gains seem modest, it’s worth remembering how far we’ve come. I was rummaging in the British Newspaper Archive this week, hunting some material on serialised Welsh lessons in old newspapers. “Folk lessons” in Gaeilge abound in the early 20th Century, and the length of some series suggests that newspaper learning went down quite well with the readership.

The 19th-century trail for Welsh, though, is a lot patchier than for Irish. What you do find, on the other hand, is plenty of column inches spent on disparaging the language. Editorials don’t hold back, either. They talk of utter uselessness, disguising the offence with that thin veil of Victorian ‘progress’.

Welsh Barb

One letter writer, choosing to remain anonymous (aware, perhaps, of the cruel barb of their remarks) weighed in on the provision of Welsh-speaking bishops to Wales in a letter to the Chester Chronicle in 1847. The letter states:

“I would hazard an opinion as to the necessity of the Welsh Bishops knowing the language of the country. … I believe 9 out of every 10 of enlightened and thinking Welshmen  would agree with me that the Welsh language is an evil and a positive disadvantage. …the lower orders are striving throughout the Principality to learn English – and why? The most ignorant servant who can speak broken English and can write, writes a letter to his friend or his sweetheart in English generally – this I know to be a fact.
The mechanic who can speak English, read and write, gets to the top of his business, while his less accomplished countryman, though equally ingenious as a workman is glad to be employed under him. … I should be extremely rejoiced to see some of my excellent countrymen promoted to the bench; but so long as a small nook of a small island is isolated by its language they are much less likely to do so than if the Welsh language was reckoned among the dead.”

An Observer

Chester Chronicle, Friday 26th March 1847

Lands with a thud, that, doesn’t it?

But if we collect our objective thoughts (deep breaths!) there is so much going on in this short passage. It’s the reduction of language to job opportunity, a way for the “lower orders” to improve their lot. It’s the ranking of languages as more primitive and more civilised (that linking of English with reading and writing). Even more bafflingly, the letter sets language use up as an either/or choice. The author doesn’t admit the idea of bilingual speakers as even a remote possibility.

And remarkably, it’s come to be viewed that way by those, like our letter writer, who count themselves as members of those communities. It’s the outcome of a homogenising, colonial Victorian project that steamrolled out difference in the name of Britannia. Regional difference is simply burden; there is no Wales – just the Principality, and the economic gains to chase within it. We see the very same attitudes towards regional dialects of English.

Sign of the Times?

A lot of our reaction to this, of course, is a product of changing times. Victorian society engaged almost obsessively in social improvement and optimisation; improving woeful working and living conditions could force this kind of all or nothing mentality that left no room for nuance.

Thankfully, this kind of binary thinking about community languages sounds, rightly, very old-fashioned. The Observer got their wish for over a century, but views like that have a lot less currency nowadays. That said, a glance at the comments section of any National article on Scottish Gaelic shows that the work in speaking up for Britain’s Celtic languages is never quite done.

It’s a reminder that languages never die because they are useless. They die because speakers are convinced they are. With a bit of work, that trick is getting harder and harder to pull off.

Speaking dialect in the mountains

AI and Dialect: Can You Nudge a Language Model Off the Standard Path?

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One of my quiet worries about the AI revolution in language is standardisation. When a model trained on billions of documents produces text, it tends to sand everything down to some kind of weighted average – grammatically impeccable, register-appropriate, but utterly devoid of local colour. And where’s the fun in that? You know my thoughts on dialect and language learning.

But here’s the thing: despite the odds, the standard grey slop isn’t a mathematical inevitability. Large Language Models are trained on billions of words. And that material will include plenty of non-standard language, too, simply by dint of the extraordinarily broad sweep of the training brush. Dialect literature, forum posts, subtitles, transcripts – there’s room for non-standard, more colloquial language in every context of the model’s background.

Which raises an interesting question: can we nudge AI output away from the standard, and towards something more authentically regional?

I’ve been playing around with exactly this, and the short answer is: yes – and it’s surprisingly decent at it.

Stepping off the well-trodden path

I’ve said from the start that AI is built for language learning content creation. There aren’t many fields where content accuracy doesn’t matter too much – as long as the language is correct and authentic-sounding. One of my go-to moves is pushing the LLM for a more colloquial register. Standard written language is fine for reading comprehension, but if you’re teaching people how to actually speak, you want contractions, ellipsis, informal vocabulary – the stuff that sounds like a real person. AI handles this pretty well once you prompt for it explicitly.

Create a model German dialogue on the topic “Reality TV”. The language should be contemporary, colloquial and natural-sounding German.

Claude did a pretty good job of this:

  • Lena: Ey, hast du letzte Woche „Love Island” geschaut? Ich konnte gar nicht aufhören – ich war bis halb zwei wach.
  • Kai: Natürlich hab ich das geschaut – wär ich ja bescheuert, das zu verpassen. Diese Tiara und Marco… ich meine, komm schon. Die spielen da nur rum.
  • Lena: Genau! Marco tut total so, als wäre er an ihr interessiert, aber das ist doch alles für die Kamera. Ich find das so zum Fremdschämen.
  • Kai: Ja, aber genau deswegen schaust du’s doch! Gib’s zu. Du liebst den Drama.
  • Lena: Okay, okay, ich geb’s zu. Aber ich find’s trotzdem fragwürdig, wie die Leute da so bloßgestellt werden. Manche wirken echt überfordert.
  • Kai: Absolut. Ich glaub, vielen ist nicht bewusst, wie sehr die Produktion das alles lenkt. Die schneiden das so zusammen, dass du denkst, die Person ist voll die Zicke – aber vielleicht war das gar nicht so gemeint.
  • Lena: Stimmt schon. Und dann wird die Person im Netz fertiggemacht. Ich hab mal gelesen, dass einige danach richtig psychische Probleme hatten.
  • Kai: Das ist so traurig. Und trotzdem melden sich jedes Jahr tausende Leute an. Ich versteh’s irgendwie – du willst berühmt werden, schnelles Geld, Follower… Der Lockruf ist halt stark.
  • Lena: Stimmt. Und seien wir ehrlich – wir beide gucken’s ja auch. Also sind wir Teil des Problems, oder?
  • Kai: Pff… ich würd sagen: wir sind Teil des Unterhaltsystems. Klingt besser. Jetzt mach die nächste Folge an.

Some great non-standard, everyday features in there. Colloquial contractions like -e dropping on verbs, decent use of particles like halt – not a bad job at all. Claude even threw in a bunch of usage notes without asking, too.

But colloquial is still, broadly, floating just beneath a standard umbrella. What if we go further?

Adding Local Flavour

When I’m probing the edges of what AI can do, I like to start with varieties I can actually judge. Austria is my German-speaking region of choice in this regard, being my year abroad destination of old – I still have a huge soft spot for Kärnterisch. So here’s the tweak:

Create a model German dialogue on the topic “Reality TV” between two young Austrian speakers from Kärnten. The language should be contemporary, colloquial and natural-sounding Austrian German.

Here’s what came back:

  • Magdi: Oida, hast du gestern „Love Island” g’schaut? I kann nimma! Die Lea hat den Stefan komplett ausg’spielt, echt a Wahnsinn.
  • Tobi: Na freilich, i schau des jo seit Wochn! Aber geh, die Lea, die tuat doch nur so. Die weiß ganz genau, wos sie macht — des is alles g’spield.
  • Magdi: Ja eh, ober trotzdem — der Stefan is so a Trottel! Wie kann man so blöd sein, gell? Der glaubt ihr jo alles.
  • Tobi: Najo, i mein, de san jo auch so a bissi animiert zum Streiten, oder? Die Produzenten woin das jo so. Ohne Zoff läuft des ned.
  • Magdi: Des stimmt scho, ober i find des trotzdem scheißn. Die tean so, ois ob’s echt wär, und dann geht’s nur ums Follower kriegen. Danoch san alle auf Instagram mit Werbung zuagekleistert.
  • Tobi: Haha, stimmt! Drei Wochen nach der Sendung verkaufen’s alle irgendwelche Protein-Shakes. Gott sei Dank ned mia. [lacht]
  • Magdi: Ober weißt wos? I schau’s trotzdem weiter. I bin schon voll drin, i muss wissen, ob der Kevin die Kathi kriegt oder ned.
  • Tobi: Na sag i dir: die kriegen sich ned. Die Kathi is viel zu schlau für ihn. Die geht da raus, macht a paar Storys, und dann war’s des.
  • Magdi: Oida, du bist so a Zyniker! Vielleicht is es jo echt?
  • Tobi: Magdi. Des is Reality TV. Des is so echt wie mei Oma beim Wrestling. [grinst]
  • Magdi: Haha, okay okay. Ober schauen tust’s trotzdem, gell?
  • Tobi: … Ja. Natürli. Um neun bin i dabei. Sag’s ned weiter.

If you’ve spent any time in Austria, this is probably just as joyful to you as it is to me. It’s added in so much southern colour, from the tag particle ‘gell’ to the reduced i from ich and past participles in g’. There’s even some fun wordplay in there (as real as my gran at wrestling!), so it’s not only dialect, but character. It’s laid it on thick!

There’s an element of pastiche to this, for sure. Nobody will claim this is indistinguishable from an authentic speaker of Klagenfurt. But it’s genuinely not bad, especially if you’re after a solid introduction to local speech features.

And then there’s Trøndersk…

That’s all very well for ‘mainstream’ foreign languages like German. To push the envelope a little more, it’s worth playing around with some lesser-learnt languages – Norwegian is always a good choice.

For my Norwegian test, I went with Trøndersk, the dialect cluster associated with the Trondheim region and much of central Norway. It’s one of the most immediately recognisable Norwegian dialects, with distinctive pitch accent patterns, characteristic -a verb endings, and a few phonological quirks that mark it out clearly from the Oslo Bokmål you’ll meet in textbooks.

How good a job would Claude do of this challenge?

  • Marte: Oi, har du sett at det e ny sesong av Farmen nå? Æ begynte å se på den i går kveld og klarte itj å slutt.
  • Jonas: Åja? Æ har itte sett mye reality-greier på ei stund, men Farmen e jo noe anna. Det e liksom ordentlig underholdning, da.
  • Marte: Ja, det e noko med den serien! Og den nye dama fra Steinkjer – ho e jo helt vill. Ho klaga på absolutt alt og alle og gjor ingenting sjæl.
  • Jonas: Haha, klassisk. Det e alltid nåkka en sånn type på realitygreier. Produsenten e nødt til å ha med dem, skjønner du? Ellers e det jo kjedelig.
  • Marte: Jaja, men det e jo litt ekkelt å innrømme at man liker det, liksom. Æ satt der og ropte på skjermen som et idiot.
  • Jonas: Haha! Ka sa du da?
  • Marte: Noe sånt som «koffor gjør’u det, din tulling!» Men mora mi satt rett ved sia og hun så bare på mæ som om æ var gal.
  • Jonas: Uff da, haha. Ka syns ho om programmet da?
  • Marte: Ho e heilt avhengig sjæl! Det e liksom heime hos oss at hele familien samles rundt skjermen på tirsdager. Det har vorre sånn siden æ va lita.
  • Jonas: Okei men ka med all den andre reality-søpla da? Eks på stranda og sånt? Det e jo bare tull, syns æ.
  • Marte: Ja nei, det gidder itj æ. Det e jo bare folk som vil bli kjendis og som itte har nåkka å by på. Farmen e annleis fordi det faktisk krev noe.
  • Jonas: Sant nok. Kameraten min i Ålesund e helt hekta på Paradise Hotel derimot. Han ser på det heile tida.
  • Marte: Seriøst? Koss klare han det? Æ holdt ut to episoder og ga opp. Alle folkan der oppe e jo helt… æ veit itte. Tomme, liksom.
  • Jonas: Haha, han sier det e «sosialt eksperiment». Æ tror bare han har lyst på drama heime fra sofaen sjæl.
  • Marte: Hahaha! Ja det e nok det. Uansett — skal du se Farmen med mæ på fredag? Vi kan bestille pizza og le av alle.
  • Jonas: Ja, det høres bra ut! Men du bestemme pizza-topping. Sist gang valde du masse squash og det va itte greit.
  • Marte: Oi, squash e jo godt da! Men okei, du får velge. Bare itte ananas.
  • Jonas: Deal. Vi ses fredag!

As far as I can tell – and I’m not so well placed here, I must admit – it’s done a solid job. The k- question words (rather than hv-), the reduced æ for jeg… Even the cultural references, selecting two actual Norwegian shows for the exchange. I’d trust this as a bit of pre-Trondheim trip prep!

That said, if you speak a Trøndersk dialect, what’s the verdict? Does this read as a reasonable attempt, or is it giving you second-hand embarrassment? I’d genuinely love to know. (Drop a comment below, or find me wherever I am on the internet this week.)

AI and Dialect for Language Learning

AI platforms clearly have some representation of dialect. Awareness is the wrong word for it – anthropomorphising LLMs and all that – but you know what I mean. The patterns are in the training data; the question is whether a prompt can reliably surface them, or whether the model’s default pull towards the standard is too strong.

The two languages I explored here seem to work well. The pull to parody is always something to watch, particularly when written material in a dialect tends to be humorous, rather than in-community. And rarer or more exclusively spoken varieties will no doubt be much more troublesome. But the more linguistic scaffolding you give the model – the more you treat prompting as a kind of sociolinguistic briefing – the better the output is likely to get.

That standard grey slop, it seems, has more texture than it first appears – you just have to know where to poke.

Correcting Mistakes: Should Teachers Do It Immediately or Later?

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A small moment in a lesson recently got me thinking about the age-old question of correcting mistakes “live” in the classroom.

A student was responding to a question, and made a tiny vocab error – nothing dramatic, just the kind of slip that happens when you’re concentrating on your idea rather than the grammar. In other words, a big win for communicative flow, but at the expense of pronouncing a word completely correctly.

My instinct as a teacher was to jump in immediately and correct it.

But I stopped myself.

The student finished the thought. The rest of the class responded. And the conversation continued. I made a mental note of the slip, and later looped back briefly to the concept to model it correctly without calling anyone’s pronunciation out.

That said, I couldn’t help wondering afterwards: had that actually been the better choice?

Immediate versus Delayed Feedback

Language teachers have debated this question for decades. Should we correct errors immediately, as they happen? Or is it sometimes better to wait, allowing learners to finish speaking before stepping in?

Recent research has begun revisiting this question, and the answer appears to be – perhaps frustratingly – “it depends.” But the details are fascinating, and they tell us something important about how language learning actually works.

In second language acquisition research, this question is known as the timing of corrective feedback. It refers to when a teacher responds to a learner’s mistake – either instantly or after some delay.

Immediate feedback happens during the interaction itself:

Learner: Yesterday I go to the shop.

Teacher: Went — yesterday I went to the shop.

Delayed feedback happens after the task or conversation is complete:

“Earlier you said I go yesterday — remember we need went for past tense.”

It can be modelled without calling out the error, too:

Teacher: You said earlier that you went to the shop? Which shop?

The study by Li, Ou and Lee confirms earlier findings that both approaches have their place. Perhaps intuitively, results show that both immediate and delayed feedback improved motivation and learning outcomes compared to giving no feedback at all.

But where do their differences lie?

What each type seems to do best

Studies like this repeatedly suggest the two approaches may support different cognitive processes.

Immediate feedback

  • helps learners notice errors right away

  • supports rapid correction during conversation

  • often leads to faster improvements in accuracy

Delayed feedback

  • encourages reflection on language forms

  • allows learners to focus on meaning first

  • may trigger deeper discussion about grammar

Some studies even find that delayed feedback leads to more discussion about linguistic forms, while immediate feedback can produce greater improvements in accuracy over time.

In other words: one supports fluency and awareness, the other precision and correction. It’s all back to that communicative flow that I didn’t want to interrupt earlier.

Teacher Fading

This debate connects neatly to another concept that has recently attracted attention in language pedagogy: teacher fading.

Fading is the idea that teachers gradually withdraw support as learners become more capable, transferring responsibility for learning to the students themselves, a technique that is gaining traction as studies like this one explore how it affects the classroom dynamic.

In practice, teacher fading means:

  • correcting less frequently

  • letting learners negotiate meaning themselves

  • allowing conversations to run without interruption

In other words, teachers deliberately step back.

Seen through this lens, delayed feedback is not just a technique – it is part of a broader teaching philosophy. Instead of jumping in every time an error occurs, the teacher allows communication to unfold, intervening later only when it is useful.

The Balancing Act

The effect, I think, is a better balance between teacher guidance and student practice – taking down the guard rails just enough for them to try out their ‘talking hats’ without fear of being slapped down.

It’s this balancing act that’s always existed in language (and other) classrooms. If correcting language mistakes happens too frequently, it can impede communication and raise anxiety. If it doesn’t happen enough, learners may simply repeat the same errors.

Modern pedagogy increasingly suggests that the most effective classrooms combine:

  • moments of focused correction

  • periods of uninterrupted communication

  • gradual reduction of teacher intervention

In other words: a mixture of immediate feedback, delayed feedback, and teacher fading.

Putting it into practice

Mistakes do not need to be corrected instantly for us to learn from them. In fact, sometimes the best thing a teacher can do is let the conversation continue. Learning is not just about eliminating errors as quickly as possible. It is about building the ability to communicate – and that sometimes requires a little space for imperfect language to unfold.

And occasionally, the most effective teaching move is surprisingly simple. In the (doctored) words of Ronan Keating:

You say it best when you say nothing at all (at least for a little while!).