Content Sequencing in Language Learning : Does It Make a Difference?

Most of us spend a lot of time thinking about what to learn in a language. Far fewer of us think seriously about the order in which we learn it. But some research suggests that content sequencing in language learning – the structure and progression of input – may play a much bigger role in successful learning than we usually assume.

One particularly fascinating study by Romain, Milin and Divjak (Language Learning, 2024/2025) explored specifically whether the order in which learners encounter grammatical patterns affects how well those patterns are learned. Interesting stuff for someone like me, who regularly dips in and out of grammars with no particular plan. Perhaps I should think again: their answer was a fairly resounding yes, order matters.

Content Sequencing in Language Learning

Content sequencing quite simply refers to how material is organised and introduced over time. There are a few considerations to make here, as both a self-paced learner and a course designer. Do we meet the most regular, high-frequency patterns first? Or should we focus on a mixture of typical and exceptional (irregular) cases from the outset? Should learning pathways build gradually on stable foundations, or can we effectively jump between topics based on our engagement and level of interest without adverse effects?

Well, the study systematised these questions within the frame of EFL teaching, and came up with some pretty clear answers. Learners who were first exposed to clear, reliable and prototypical examples of a structure before encountering messier edge cases developed stronger, more flexible understanding than learners who saw everything mixed together from the start.

In plain terms: learners seem to benefit from building stable generalisations first, before being asked to handle complexity and exceptions. For us language learners on the ground, that means learning and internalising regular paradigms before worrying too much about irregularities.

What’s particularly interesting is that this challenges the popular assumption that more varied input earlier is always better. Instead, it suggests that thoughtful content sequencing in language not only helps us structure our learning more effectively, but also results in deeper, more solid foundations over time.

Why this matters

For individual learners, this is quietly reassuring. If you sometimes feel more comfortable with core patterns than with exceptions, that isn’t failure. It’s our natural mode of learning.

It also suggests that revisiting foundational structures repeatedly, across different contexts, isn’t wasted time. It’s part of how robust knowledge is built. Cover the core well, and you’re setting yourself up for long-term progress.

For anyone building language learning apps, courses or platforms, it’s equally insightful. Many tools prioritise variety, novelty and engagement, which are important, of course. But how many courses truly focus on recycling foundational structures at length, without succumbing to the temptation to list exceptions early on?

A quiet design principle worth taking seriously

None of this means learning must be rigid, linear or joyless. It doesn’t mean that we should ignore irregularity, either. But it does suggest that step-by-step, paradigmatic sequencing isn’t boring, or missing the detail; it’s cognitive kindness. It helps learners build confidence, coherence and flexibility over time.

Perhaps we should spend less time asking how much content we can squeeze into a curriculum or an app, and more time asking whether the order of that content actually supports how learning unfolds.

Because in language learning, progress isn’t just about exposure. It’s about architecture.

Where Have All the Language Learners Gone?

You’ve probably already read the doom-mongering headlines: formal uptake of language learning is in sharp retreat in the UK. It’s an alarming trend, and it couldn’t come at a worse time for a UK (and world) that needs bridges building. As flag-fliers for languages, it’s something that should give all of us in the language community pause for thought.

A report published in 2025 by the Higher Education Policy Institute (HEPI) pulls no punches – modern and classical languages now account for under 3% of all A-level entries, while teacher recruitment for languages remains at just 43% of target. Undergraduate enrolments in modern languages have fallen by around 20% in five years, and many university language departments have quietly closed or contracted. Bear in mind that these trends were already being felt over 20 years ago when I did my teacher training, and you realise that it’s been a slow drip wreaking havoc in plain sight.

The narrative is sobering. Languages were once a staple of post-14 education – a language GCSE was still compulsory when I was taking mine in the early 90s. But thanks to multiple linguaphobic policy shifts, accountability pressures, and chronic underinvestment, they find themselves more and more on the back foot. The hammer blow came early, back in 2004, with the decision in England to make languages optional after age 14. That was a key structural turning point, and the long-term effects (surprise, surprise) are now clearly visible.

The institutional decline is real

There are obvious consequences to this trend. It raises serious questions about equity (access to language learning increasingly correlates with socio-economic background), national linguistic capacity, and the future of research and teacher supply. Organisations such as HEPI, the Russell Group and the Chartered Institute of Linguists have all warned that the decline represents not just a cultural loss, but a strategic one.

On paper, then, language learning appears to be in retreat.

But something else is happening alongside it

And yet, that story doesn’t quite match our lived experience. Spend any time online and you’ll see something different entirely: language-learning YouTube channels with millions of followers; Discord servers full of learners practising Korean at midnight; thriving subreddits, podcasts, apps, blogs, meetups, challenges, and communities devoted to the sheer pleasure of learning languages.

If formal pathways are shrinking, informal ones are flourishing.

More and more people seem to be learning languages not because they are required to for a qualification, but because they want to. Out of curiosity. Cultural interest. Identity. Joy. In other words, language learning is increasingly becoming a hobbyist, self-directed, or even lifestyle pursuit rather than an institutional one.

The rise of hobby learning and polyglot culture

The growth of the so-called “polyglot community” is part of this shift. This isn’t a formally organised movement, but rather a loose ecosystem of learners who share strategies, resources, encouragement, and enthusiasm. Some are very advanced, others are beginners; some focus deeply on one language, others enjoy exploring several. What they tend to share is intrinsic motivation, and a love of signposting cheap (and frequently free!) resources for learners.

This aligns closely with what we already know from decades of research on learner autonomy and motivation: sustained engagement is far more likely when learners feel ownership, agency, and personal meaning in what they are doing. Many hobbyist learners aren’t working towards a certificate; they’re simply working towards connection, enjoyment, identity, or intellectual stimulation.

There isn’t yet a large academic literature specifically on “polyglot culture”, but there is plenty of research on self-directed learning, intrinsic motivation, multi-competence, and identity in language learning that helps explain why these communities can be so powerful.

Loss and possibility, side by side

None of this negates the seriousness of the institutional decline. Formal education provides structure, support, progression, and access. And when those pathways disappear, it is disproportionately students from less advantaged backgrounds who lose out. That matters.

But it also seems clear that the desire to learn languages hasn’t gone away. It has simply shifted location. People are still learning – just not always through schools, universities, or qualifications. They’re learning on buses, in lunch breaks, late at night, through friendships, fandoms, travel, heritage, curiosity.

So perhaps the better question isn’t “Why is language learning dying?”, but rather: why has it migrated?

Because language learners are still very much here. They’re just not always where the education system expects them to be.

The First Communicative Turn: The 1880s Reform Movement And Language Teaching

It is easy to think of communicative language teaching as a late 20th-century invention. Pairwork, role-play, authentic materials and the idea that language exists primarily for communication are often associated with the classroom revolutions of the 1970s and 1980s. But the roots of that shift run much deeper. In fact, many of the arguments we now consider “modern” were already present in late nineteenth-century discourse around education.

That earlier shift was the Reform Movement in modern foreign language teaching – a remarkably modern-seeming turn in thinking around language education. Emerging across Europe in the 1880s, it represented a serious intellectual challenge to the long-dominant Grammar–Translation Method and laid down principles that still feel strikingly familiar today.

The problem with Grammar–Translation

Throughout most of the nineteenth century, language teaching in schools entailed grammatical drills, vocabulary lists and translation exercises. Lessons typically revolved around written texts, often literary, with little attention paid to pronunciation, listening or spontaneous speech. The method had clear roots in classical language education, where the goal was access to texts rather than communicative ability.

Now, as much as this helps learners get to grips with the rules of language – I love the systematicity of those old courses, myself – the problem was that this approach was increasingly out of step with social reality. As travel, trade and international communication expanded, learners wanted usable language, not just intellectual knowledge about language. Students could often analyse complex sentences yet struggled to understand or produce even basic spoken forms. By the 1870s and 1880s, frustration with this mismatch was becoming more openly voiced.

The Reform Movement and the rise of “living language”

Now, the Reform Movement that rallied against this method was not a single organisation. Rather, it was a loose, unaffiliated network of linguists, teachers and educational thinkers across Europe who shared similar concerns. What united them was the conviction that language teaching should centre on modern language in use, rather than the continuity of age-old classroom tradition for its own sake.

Where there’s a particularly pertinent crossover for me, working in dialect research, is with one of its most prominent British figures – one Henry Sweet, a pioneering phonetician and linguist. Sweet argued that language teaching should focus on the present, and informed by scientific linguistic knowledge, particularly phonetics. Learners, he believed, needed systematic exposure to spoken language and accurate pronunciation from the start, rather than being left to infer sounds from spelling.

Other prominent theorists were making similar arguments elsewhere. In Germany, Wilhelm Viëtor famously declared that modern language teaching was in a state of crisis, calling for a radical break with grammar-translation. In France, Paul Passy, one of the founders of the International Phonetic Association, promoted phonetic training and naturalistic exposure to speech. Across these contexts, common principles began to emerge.

Spoken language should be prioritised alongside reading and writing. Pronunciation matters and should form its own, explicit part of the curriculum. Learning should progress from simple, high-frequency language to more complex forms. It was best to learn a language through meaningful, communicative activity, not only through analysis.

These ideas did not overturn educational systems overnight, but they represented a genuine conceptual shift. Practitioners viewed language increasingly as a practical tool, not merely an object of scholarly study.

From the 1880s to the communicative turn of the 1980s

What makes the Reform Movement particularly interesting is how closely its goals align with those of the later communicative turn in language teaching almost a century later.

By the mid-20th century, many school systems had once again become dominated by structural syllabi and form-focused teaching, even where newer methods such as audiolingualism – remember those Linguaphone courses? – had temporarily emphasised speech. Yet the same familiar problem persisted: learners were spending years studying languages without developing functional communicative ability.

In the 1970s and 1980s, applied linguistics began to offer new theoretical tools for articulating what earlier reformers had intuited. The concept of communicative competence, associated with scholars such as Dell Hymes and later Canale and Swain, argued that knowing a language involves far more than grammatical accuracy. It includes the ability to use language appropriately in social contexts, to manage interaction, and to interpret meaning and intention.

This thinking led directly to the growth of Communicative Language Teaching (CLT): classrooms built around tasks, interaction, negotiation of meaning and real-world language use. What changed in the 1980s was not so much the underlying aspiration, but the intellectual and institutional support behind it. Applied linguistics had matured, classroom research had expanded, and globalisation had increased the practical demand for communicative proficiency.

Seen in this light, the communicative turn of the 1980s looks less like a sudden revolution and more like a return to those long-standing questions. Many of the core critiques voiced by communicative theorists echo those of Sweet, Passy and Viëtor – that teachers should privilege real usage, that speech matters, and that learners need opportunities to use language meaningfully.

Why this history still matters

So, there’s nothing new under the sun (or Intet er nytt under solen, as Åse Kleveland famously sang at the 1966 Eurovision Song Contest – honestly, there’s a Eurovision reference for everything!). Understanding this longer history helps to challenge the idea that language teaching progresses in a neat, linear way from “old-fashioned” to “modern”. Instead, the field tends to cycle through recurring tensions: form versus meaning, analysis versus use, system versus communication. The Reform Movement shows that concerns about authenticity, speech and learner experience are not new innovations but part of a conversation stretching back well over a century.

For teachers and learners today, this perspective can be reassuring. Many of the instincts that feel pedagogically sound now were already being articulated in the 1880s. The tools and terminology have changed, but the underlying question remains remarkably consistent: not simply how language is structured, but how it is lived. It’s also a nice reminder of how thoroughly modern the Victorians appear – at times!

Diffuse squares

SingaKids: A Glimpse of Where Multimodal AI Tutoring May Be Headed

A recent pre-print on SingaKids, a multilingual multimodal tutoring system for young learners, offers an interesting look at how AI-supported language learning is evolving. You can read the paper here: SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning.

Designed for early primary classrooms, SingaKids is an AI-based system that uses picture-description tasks as the basis for spoken interaction. It combines dense image captioning, multilingual speech recognition, a dialogue model tuned with pedagogical scaffolding, and child-friendly text-to-speech. The system works in English, Mandarin, Malay, and Tamil, with extra attention paid to the lower-resource languages to improve recognition and generation quality.

Flexible Scaffolding

Something that stood out to me in particular was the system’s focus on scaffolding rather than straightforward correction. That approach is flexible; depending on a child’s response, the system shifts between prompts, hints, explanations, and more structured guidance. Higher-performing learners are pushed towards fuller reasoning; less confident learners get clearer cues and more supportive turns. It’s a step away from the rigid “question–answer–score” pattern and closer to the texture of real classroom dialogue.

Although the work is aimed at children, several ideas have wider implications for the rest of us. Picture-guided dialogue isn’t new in ‘grown-up’ resources – think Rosetta Stone, for instance. But it could easily support adult learners practising free production in AI tools, too. Improved multilingual ASR – especially for hesitant, accented, or code-switched speech – would benefit almost every speaking-practice tool. And the flexible scaffolding approach hints at future e-tutors that adapt to the learner’s behaviour dynamically, rather than funnelling everyone down the same path.

The project sits firmly in the research space, but it points towards what the next generation of tools may look like: multimodal, context-aware systems that don’t just respond to learners but actively guide, prompt, and adjust. For anyone keeping an eye on developments in educational AI, it’s a nice indication of the direction of travel (and I’m probably a wee bit envious of those kids getting a chance to try it first!).

AI Role-Plays that Actually Move the Needle

Papers on AI in education are two a penny at the moment, but there’s a particularly nice one that appeared recently in Frontiers in Education (30 Sept 2025). It takes a fresh look at AI-generated, scenario-based conversation practice for university EFL learners – one of perhaps the most obvious and widespread use cases for AI in language learning, but given a smart, systematic treatment by a team of scholars from Saudi Arabia, China and Pakistan.

The gist is simple: build realistic speaking scenarios with AI, let students interact in them over a term, and see what happens. Over 18 weeks with 130 first-years split into control vs. AI-scenario groups, the AI cohort came out ahead on pronunciation, accuracy and conversational flow. They also reported higher interest and better teacher–student interaction to boot.

The catch? Emotional thinness in AI dialogue, patchy content quality if you don’t curate, and a risk of learner over-dependence on the tech. 

So, what can we pinch for our own learning? Well, the paper itself is full of useful nuggets and worth a careful read. But here are some key takeaways for avoiding “AI for AI’s sake” based on the team’s findings.

1) Make your speaking tasks scenario-first, not tool-first.

Before opening any chatbot, sketch a brief: Where am I? Who am I? What’s my goal? What counts as success? That mirrors the paper’s “input → interaction → output” design and stops generative models meandering (always an occupational hazard worth mitigating against).

2) Bake in “flow nudges”.

The study’s gains in conversational flow suggest prompts that push you to repair, clarify and keep turns moving. Add rules to your prompt like: “If I give a short answer, ask a natural follow-up; if I stall, offer two options.” That keeps the exchange discursive rather than Q&A-ish. 

3) Add in a feedback micro-loop.

The report notes improvements in pronunciation, which is fine if you’re using AI in voice mode. If not, replicate that with a regular mini-feedback cycle that gives short explanations for tricky words of phrases.

4) Curate, don’t just generate.

A recurring warning was inconsistent or culturally off-kilter content when left unchecked. Make sure to describe your scenario frames in terms of function, time and place (e.g., returning a faulty purchase in Athens; arranging a GP appointment in Lille). 

5) Add a human(-like) layer to keep things warm

Students benefitted from richer teacher–student interaction around the AI tasks. Translate that to solo study by doing a quick human check: post one 60-second recap to a study buddy, social feed or tutor each week. This ‘social accountability’ step compensates for the AI’s limited emotional range. Try recording the dialogue afterwards as a voice note, too, for some added spoken practice.

6) Watch the dependence trap.

The authors flag tech over-reliance. Give yourself “AI-off Fridays”: repeat a scenario from memory with real materials (voice notes, a friend, or even talking to your phone camera), then compare to your AI-assisted version for gaps. 

AI in Practice

Bringing all that together, here’s a ready-to-use mini-format you can try for a 15-minutes role-play practice that isn’t crow-baring AI in for no real gain:

  • Minute 0–2: Choose a vetted scenario card (place, role, goal, 3 key phrases).

  • 2–3: Prime the bot with constraints (stay in A2/B1, insist on follow-ups, correct only one thing per turn).

  • 3–10: Converse. Every third turn, ask for a meaning / explanatory nudge on one tricky word or structure.

  • 10–12: Bot summary with 3 personalised upgrade lines you could have said.

  • 12–15: Record a no-AI voice note version. Park it for a weekly human warm-layer check.

Pastable Prompt

You are a language conversation partner tasked with improving the language skills of me, the user.
We’ll do a short scenario-based speaking practice in French.
Follow these rules carefully:
1. Keep the level at A2–B1 CEFR.
2. Always stay in character and make the conversation feel natural – imagine we’re really there.
3. Insist on follow-up questions whenever my answers are too short or unnatural.
4. Correct only one thing per turn, briefly and gently, then move on.
5. Every third turn, give me a short “💡 Language note” explaining a tricky word or structure that came up.
6. After about 20 lines or so of dialogue (ideally when the conversation draws to a natural close), give a performance summary, including what I did well, some ‘upgraded’ versions of my sentences showing how I could sound more natural or advanced, and 2-3 new phrases worth learning from this conversation.
7. Keep the tone friendly, realistic, and mildly humorous if it fits the setting. When ready, start the conversation by greeting me in the target language and setting the scene.

The bottom line is that AI role-plays can be genuinely useful when we design around them: scenario first, small feedback loops, and human warmth stitched back in. Treat the model like a scene partner with good timing but flat affect, and you’ll harvest the fluency gains without outsourcing your judgement.

The paper’s results are encouraging; its realistic caveats are a gift that ground us back in practical realism. As always, build guardrails into your AI usage first of all, to ensure that you get the most from – and enjoy – the chat! 

ChatGPT French travel poster

A Second Shot at Perfect Posters – ChatGPT’s Image Tweaker

The big ChatGPT news in recent weeks is about images, rather than words. The AI frontrunner has added a facility to selectively re-prompt for parts of an image, allowing us to tweak sections that don’t live up to prompt expectations.

In essence, this new facility gives us a second shot at saving otherwise perfect output from minor issues. And for language learning content, like posters and flashcards, the biggest ‘minor’ issue – the poor spellings that crop up in AI image generation – makes the difference between useful and useless material.

Rescuing ChatGPT Posters

Take this example. It’s a simple brief – a stylish, 1950s style travel poster for France. Here’s the prompt I used to generate it:

Create a vibrant, stylish 1950s style travel poster featuring Paris and the slogan “La France”.

I wanted the text “La France” at the top, but, as you can see, we’ve got a rogue M in there instead of an N.

ChatGPT generated image of a French travel poster

To target that, I tap the image in the ChatGPT app. It calls up the image in edit mode, where I can highlight the areas that need attention:

ChatGPT image editing window

Then, I press Next, and can re-prompt for that part of the image. I simply restate the slogan instructions:

The slogan should read “La France”.

The result – a correct spelling, this time!

ChatGPT French travel poster

It can take a few goes. Dodgy spelling hasn’t been fixed; we’ve just been given a way to try again without scrapping the entire image. Certain details also won’t be retained between versions, such as the font, in this example. Others may be added, like the highly stylised merging of the L and F in the slogan (a feature, rather than a bug, I think!).

But the overall result is good enough that our lovely 1950s style poster wasn’t a total write-off.

Another case of AI being highly imperfect on its own, but a great tool when enhanced by us human users. It still won’t replace us – just yet!

Image tweaking is currently only available in the ChatGPT app (iOS / Android).

An illustration of a cute robot looking at a watch, surrounded by clocks, illustrating AI time-out

Avoiding Time-Out with Longer AI Content

If you’re using AI platforms to create longer language learning content, you’ll have hit the time-out problem at some point.

The issue is that large language models like ChatGPT and Bard use a lot of computing power at scale. To keep things to a sensible minimum, output limits are in place. And although they’re often generous, even on free platforms, they can fall short for many kinds of language learning content.

Multi-part worksheets and graded reader style stories are a case in point. They can stretch to several pages of print, far beyond most platform cut-offs. Some platforms (Microsoft Copilot, for instance) will just stop mid-sentence before a task is complete. Others may display a generation error. Very few will happily continue generating a lengthy text to the end.

You can get round it in many cases by simply stating “continue“. But that’s frustrating at best. And at worst, it doesn’t work at all; it may ignore the last cut-off sentence, or lose its thread entirely. I’ve had times when a quirky Bing insists it’s finished, and refuses, like a surly tot, to pick up where it left off.

Avoiding Time-Out with Sectioning

Fortunately, there’s a pretty easy fix. Simply specify in your prompt that the output should be section by section. For example, take this prompt, reproducing the popular graded reader style of language learning text but without the length limits:

You are a language tutor and content creator, who writes completely original and exciting graded reader stories for learners of all levels. Your stories are expertly crafted to include high-frequency vocabulary and structures that the learner can incorporate into their own repertoire.

As the stories can be quite long, you output them one chapter at a time, prompting me to continue with the next chapter each time. Each 500-word chapter is followed by a short glossary of key vocabulary, and a short comprehension quiz. Each story should have five or six chapters, and have a well-rounded conclusion. The stories should include plenty of dialogue as well as prose, to model spoken language.

With that in mind, write me a story for French beginner learners (A1 on the CEFR scale) set in a dystopian future.

By sectioning, you avoid time-out. Now, you can produce some really substantial learning texts without having to prod and poke your AI to distraction!

There may even be an added benefit. I’ve noticed that the quality of texts output by section may even be slightly higher than with all-at-once content. Perhaps this is connected to recent findings that instructing AI to thing step by step, and break things down, improves results.

If there is a downside, it’s simply that sectioned output with take up more conversational turns. Instead of one reply ‘turn’, you’re getting lots of them. This eats into your per-conversation or per-hour allocation on ChatGPT Plus and Bing, for example. But the quality boost is worth it, I think.

Has the section by section trick improved your language learning content? Let us know your experiences in the comments!

ChatGPT releases custom GPT models

ChatGPT, Your Way : Custom GPTs In The Wild!

This week saw one of the biggest recent developments in consumer AI. ChatGPT released GPTs – customisable AI bots – into the wild for Plus members, and the community has gone wild.

In a nutshell, GPTs are AI bots with custom behaviour that you define. And you define that behaviour using natural language, just like how you talk to regular ChatGPT.

Crucially, GPTs are shareable. So you can come up with a killer app idea, set it up in seconds, then share your creation with the world. Already, linguists and language lovers are sharing their creations on the socials.

ChatGPT for Worksheet Creation

Obviously, I couldn’t wait to get playing when the GPT creation tool went live this week. I’ve long been a cheerleader for topic-based units for independent study, especially when preparing for spoken lessons. So the first thing I coded up was a foreign language worksheet creator!

It’s the kind of thing I’ve been writing and sharing prompts about for a while, now. The big game-changer, of course, is that now, all that functionality is packaged up into a single, one-click module. Open it, tell it your language, topic and level, and watch it go. This will produce a range of resources and activities for independent learning, including a vocabulary list, reading comprehensions, and cloze quizzes.

Genuinely useful for self-study!

Foreign Language Worksheet Creator GPT in ChatGPT

Foreign Language Worksheet Creator GPT in ChatGPT

It’s already been a learning experience, for all of us tinkerers. For one thing, I found out not to overload it by trying to do too much at once, or turning on all its capabilities (browsing, code interpretation and image creation). I ended up with a uselessly slow initial version that I can no longer even reopen to edit.

Ah well – these things make us!

Old English Monkeys

When you do get a working version, however, you can boggle at the versatility of it. That’s thanks to the billions of training points backing up the platform. I asked it to create an Old English worksheet on the topic “Monkeys”, in the style of a Modern Languages worksheet, as a cheeky wee test. Admittedly, ChatGPT did say that it would be a challenging task. After all, just how many Old English documents do researchers train their LLMs on? But the results were really not bad at all…

An Old English worksheet in ChatGPT

An Old English worksheet in ChatGPT

 

I expect many of us are playing these games, pushing the new tech to see how far it can go. At the very least, we can all revisit those isolated prompt ideas we’ve been collecting over the past months, and turn them into shareable GPTs – for work and for fun.

Have you had chance to play yet? Share your proud creations with us in the comments!

A digital brain, complete with memory - ChatGPT take note!

Your ChatGPT Teacher – With Persistent Memory!

The interactivity of AI models like ChatGPT and Bing make them the perfect medium for exchange-based language learning. But for one thing: their lack of persistent memory.

The standard setup, to now, has been for a ‘black box’ style conversation on AI platforms. You initiate a session with your instructions, you chat, and it’s over. You can revisit the conversation in your history, but as far as AI is concerned, it’s lost in the mists of time.

It’s something that throws a mini spanner in the works of using AI for language (or any kind of) learning. Teaching and learning are cumulative; human teachers keep records of what their students have studied, and build on previous progress.

DIY ChatGPT Memory

There seems to be little movement in the direction of AI with memory amongst the big platforms, although OpenAI’s recent announcement of memory storage for developer use might lead to third-party applications that ‘remember’. But in the meantime, users within the AI community, ever adept at finding workarounds and pushing the tech, have begun formulating their interim alternatives.

One clever way around it I recently spotted takes advantage of two elements of ChatGPT Plus: custom instructions and file upload/analysis. In a nutshell, an external text file serves as ChatGPT’s ‘memory’, storing summarised past conversations between student and AI teacher. We let ChatGPT know in the custom instructions that we’ll be uploading a history of our previous conversations at the beginning of a learning session. We also specify that it analyse this file in order to pick up where we left off. At the end of each session, we prompt it to add a round-up of the present conversation to that summary, and give the file back to us for safekeeping.

Custom Instructions

Here’s how I’ve worked the persistent memory trick into my own custom instructions:

If I upload a file ‘memory.txt’, this will be a summary of our previous conversations with you as my language teacher; you will use this to pick up where we left off and continue teaching me. When prompted by me at the end of our session, update the file with a summary of the present conversation and provide me with a link to download it for safekeeping. This summary should include a condensed glossary of any foreign language terms we’ve covered.

Wording it as such makes memory mode optional; ‘teacher remembering’ only kicks in if you upload memory.txt. This way, you can otherwise continue using regular, non-teach ChatGPT without any fuss.

The only thing that remains is to create a blank text file called memory.txt to start it all off. Remember to start a new chat before giving it a whirl too, so your new custom instructions take. As you use the technique in your everyday learning chats, you’ll see memory.txt blossom with summary detail. As an offline record of your learning, it even becomes a useful resource in its own right apart from ChatGPT.

Just make sure you keep it safe – that’s your teacher’s brain you have right there!

A page of conversation summaries - my ChatGPT 'memory' file in action.

My ChatGPT ‘memory’ file in action.

Let us know your experiences if you give this technique a go! And if you’re stuck for lesson ideas, why not check out my book, AI for Language Learners?

A neon globe surrounded by books - the AI future is here.

AI for Language Teachers – the Essential Bookshelf

Clearly, emerging Artificial Intelligence platforms have colossal potential to transform education. Indeed, they are already doing so, proving to innovative disruptors that teachers and students are still grappling to understand. Given the pace of change, where can educators find solid training on practical, classroom-ready AI techniques?

Thankfully, a raft of publications has sprung up with teaching practice at its core. Many of the best titles are from author-educators who have self-published from personal experience. Self-publishing, of course, is a quick, reactive way to get books out there, so it’s unsurprising that there are so many gems that don’t originate with big publishing houses.

It must be said that the majority of current titles are US-centric – again, unsurprising, given that largely US-based AI companies have generally release the leading-edge innovations in the US first. That said, the following picks are all notable for a universal approach, with a generality that should make them useful whatever the setting.

Without further ado, here is the language teacher’s essential AI bookshelf!

The Essential AI Bookshelf

THE AI CLASSROOM

Amazon product image - the AI Classroom The AI Classroom With five-star reviews almost across the board, the authors of The AI Classroom were quick off the mark; the book has become an early leader for practical teaching ideas utilising artificial intelligence. It contains a broad range of ready-to-use prompts, perhaps the most reliable hallmark of the best AI guides for teachers and learners on the market at the moment. What is particularly insightful is the discussion of school policy as an important consideration – an indispensable consideration, particularly for department heads and administrators.

THE AI INFUSED CLASSROOM

Amazon product image - The AI-Infused Classroom

The AI Infused Classroom by Holly Clark is a practical and visionary guide for educators who want to use emerging LLM tools to transform teaching and learning. Clark, a seasoned teacher and edtech expert, is author of The Infused Classroom series, which explores how to amplify student voices with technology. This book builds on those ideas, demonstrating how to leverage AI as a catalyst for innovation, creativity, and deep learning. The book adopts a refreshingly student-centred approach to classroom AI, and is a source of invaluable best practice for teachers of languages and otherwise.

AI FOR LEARNING

Amazon product image - AI for Learning Part of the AI for Everything series, AI for Learning is a book that explores how the medium can, and should, positively impact human learning in various contexts. The authors offer a clear and engaging introduction to the concepts, applications, and implications of AI for learning. The book serves as both an explanatory introduction and practical guide, covering topics from core concepts of AI to how it can develop critical thinking and digital citizenship skills, and prepare learners for the future of work and learning. The book also addresses the ethical and social issues that arise from using AI for learning, such as privacy, bias, accountability, and trust.

80 WAYS TO USE CHATGPT IN THE CLASSROOM

Amazon product image - 80 Ways to Use ChatGPT in the Classroom You can’t beat a good old ‘X ways to do…’ guidebook, and this volume boasts an impressive 80 of them! 80 Ways to Use ChatGPT in the classroom gets straight down to brass tacks with organised, practical prompt examples. A particular strength of this book is a welcome nod to balance throughout, with ample discussion of the issues as well as the well-fanfared benefits. As one of the earliest of these guides to appear, the focus is ChatGPT. However, as with all of these books, the knowledge is easily transferrable to other platforms.

AI FOR LANGUAGE LEARNERS

The cover of AI for Language Learners by Rich West-Soley As the only title to focus specifically on languages – and the one I penned myself – I could hardly leave out AI for Language Learners! Written to be accessible to individual learners as well as classroom teachers, it’s packed full of practical prompt ideas. These cover language reference, practice activities and resource creation. What’s more, the book includes access to a website with copy-paste prompt for those with the paperback. That is definitely a boon to those those typing fingers! The book was a labour of love over summer 2023, and is the product some very enthusiastic experimentation to support my own polyglot learning. I hope you have as much fun trying the prompts as I did writing them.

Brave New World

As AI comes to land firmly in classrooms over the coming months, we’ll undoubtedly be seeing title after title appear. Are there any favourite titles of yours that we’ve missed? Let us know in the comments!