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!

An abstract image depicting social learning

Language learning isn’t learning without the social

A fresh-off-the-press npj paper confirms this month what polyglots have been saying all along: other people make language learning better!

If you’ve ever felt that a single good conversation in your target language does more for your progress than hours of app-based drilling, you’re not imagining it. The paper by Zappa, Slater and Rodriguez-Fornells, published in npj Science of Learning (part of the Nature family) makes a strong, evidence-backed case for something many language learners instinctively know: social interaction isn’t a nice add-on to language learning – it’s central to how it works.

The paper revisits long-standing ideas in second language acquisition research and combines them with newer technological perspectives, asking a deceptively simple question: why does interacting with other people help us learn languages so effectively?

Interaction isn’t optional – it’s structural

At the heart of the paper is the instinctive idea that language is fundamentally a social tool. We don’t acquire it just by hearing words or memorising forms, but by using language to do things with other people: asking, clarifying, negotiating meaning, reacting, repairing misunderstandings.

This aligns with what linguists have called the interaction hypothesis: when learners are forced to adjust what they say in order to be understood – and to understand others – their attention is naturally drawn to gaps in their knowledge. Those moments of “oh, that didn’t quite work” are often where real learning happens.

In other words, conversation isn’t just practice. It actively shapes acquisition. And any practical language learning strategy should really aim to build in that social aspect (sorry, shy polyglots – myself included!).

What’s the new take here?

Where this 2025 paper gets particularly interesting is in its discussion of virtual reality (VR) and immersive digital environments. The authors argue that VR offers a powerful research and teaching space because it allows for:

  • social interaction that feels meaningful and contextual
  • repeatable, controlled scenarios (something real life is terrible at)
  • lower-stakes environments where learners may feel less anxious about speaking

Rather than replacing real human interaction, these tools can scaffold it – especially for learners who find face-to-face conversation intimidating (I’m including myself in that cohort!), or for classrooms where access to diverse speakers is limited.

The key takeaway isn’t “everyone must now learn languages in VR”, but that the social dimension of learning can be designed for more intentionally, even when technology is involved.

Why this matters for your language learning

If you’re a learner, this research reinforces a few practical truths:

  • Listening and reading are essential, but they’re not enough on their own
  • Progress accelerates when we have to respond, adapt and negotiate meaning on the fly
  • Low-pressure interaction (with tolerant partners, or mediated by tech) often beats “perfect” study conditions

That might mean language exchanges, group classes, conversation clubs, or even carefully chosen digital environments that encourage spontaneous output rather than scripted responses.

And for teachers?

For teachers, the message is simple: design for interaction, not just exposure. That doesn’t demand flashy tech, but it does mean thinking about how we prompt learners to respond to one another, repair misunderstandings, and co-construct meaning.

Technology, including AI and immersive tools, is most powerful here when it supports those social processes rather than replacing them. Used well, it can widen access to interaction and reduce anxiety, especially for quieter or less confident learners.

A final thought

Language is something we do with people. This paper is a useful reminder that however sophisticated our tools become, the engine of language learning remains deeply human: interaction, responsiveness, and shared meaning.

If your study routine or teaching practice has drifted too far towards passive consumption, this might be the nudge to bring conversation back to the social centre.

The CEFR scale - a ladder to fluency in language learning

CEFR and Interactive Language Learning: Bringing the Threads Together

The Council of Europe’s CEFR (Common European Framework of Reference) scale is something we’re almost instinctively aware of as language learners. A1/2, B1/2 and C1/2 are the shorthand we regularly use for fluency. A recent paper by Gökhan Haldun Demirdöven in Frontiers in Education takes a long-overdue fresh look at the framework in light of new immersive, technology-mediated language learning. How does our common language ladder mesh with recent developments?

A lot of recent work on language learning tech focuses on platforms such as XR, AI-driven environments and conversational agents almost as a pedagogical end in themselves. Instead, this paper asks a more structured question: how might these technologies meaningfully align with what the CEFR already – with well-established practical application – conceptualises as language ability?

In other words, can we design immersive digital environments to support the kinds of communicative action, mediation, and interaction that CEFR descriptors actually describe?

CEFR : More Than Just Assessment

The author takes the 2020 revisions to the CEFR as a starting point, identifying features of the updated framework – particularly its emphasis on mediation, multiple competences, and real-world communicative tasks – and considers how these might map onto immersive learning contexts. Examples include simulated environments for task-based interaction, AI-supported conversational agents, and speech-recognition systems that are sensitive to sociolinguistic variation rather than enforcing a single normative model.

One enlightening conclusion is that the CEFR is not simply an assessment tool; it is also a design framework. New language learning technologies are useful as long as they support CEFR-aligned communicative action, rather than as standalone innovations. This is a helpful course correction in a space where language-learning technologies can sometimes prioritise novelty over pedagogical coherence. For me, as a language software developer, it’s certainly something that strikes home – language professionals should avoid developing new language tech in a vacuum, and instead build on previous work with a proven track record.

For teachers, curriculum designers, and language-learning app developers, it’s a really timely reminder that innovation in language learning does not have to sit outside existing standards. Frameworks like the CEFR can play an active role in shaping how new technologies are pedagogically grounded.

Christmas 2025

Christmas Gifts for Language Lovers : 2025 Edition!

It’s that time of year again: the seasonal round-up of gifts for the language-obsessed. Whether you’re shopping for a budding bilingual, a seasoned polyglot, or the friend making language learning resolutions at last – here’s this year’s curated list of things to delight and support.

As you’d expect, AI is everywhere in the ‘edu-gift’ shopping lists lately. Which is why I’ll start with some old-school resources – no gimmicks, no experimental tech, just solid trad resources you can hold in your hands. That’s not to say there aren’t some techie gifts worth a look, so we’ll round off with one fun wee gadget!

📚 Journals & Notebooks

Sometimes the simplest tools are the best — especially for vocab. There’s something refreshing (and frankly more memorable) about writing things down compared to tapping buttons in an app. And with ‘functional stationery’ as strong as ever, there are a few lovely language-specific picks:

FLUENTISH: Language Learning Planner & Journal

Fluentish is a neat, structured journal for vocabulary, grammar notes, weekly goals and reflections. It’s light-touch enough not to overwhelm, but organised enough to help learners spot progress. Great for anyone juggling multiple languages or trying to build a routine.

Goldlist-Friendly Vocabulary Notebooks (various languages)

If you’ve ever flirted with the Goldlist Method — that gentle, handwriting-based approach to deep vocabulary learning — then a good layout makes all the difference. There are plenty of notebooks set up with the familiar 3-column pattern the technique requires.  If it’s for a gift, you can get some wonderfully themed ones like this Korean ‘build your own dictionary’ notepad.

Great stocking fillers, particularly lovely for learners who like to keep their notebooks tidy and colour-coded.

🎲 Card & Board Games

Language learning shouldn’t always be earnest. A few games – many of them handily portable – genuinely pull their pedagogical weight while being light-heartedly frivolous:

TOP TRUMPS: LOL SURPRISE (FRENCH EDITION)

It’s Top Trumps, but in French and with cute animé style characters — which somehow makes it both sillier and more useful. Great for kids or nostalgic adults, and brilliant for casual vocab exposure without even trying.

TOP TRUMPS: NARUTO (GERMAN EDITION)

Now this one is actual animé – but in German. The text on each card adds a surprising amount of reading practice (and speaking, if you insist on your fellow players sticking to the target language). You learn without noticing — the best kind.

KLOO SPANISH BOARD/CARD GAME

OK, this one is a bit less portable. But KLOO games are designed explicitly for language learners as resources to learn from. You build Spanish sentences as you play, picking up grammar patterns and vocabulary naturally. Ideal for families, classrooms or anyone who’d rather learn Spanish through play than through verb tables. (Personally I like both ways.)

🤖 One Fun Gadget

The gadget market is heaving with “AI translators”, many of which are… optimistic in their promises. After wading through the noise, there’s one that’s consistently reliable and genuinely useful for language learners and travellers alike:

POCKETALK S2 GLOBAL TRANSLATOR DEVICE

Supports a huge range of languages, handles two-way voice translation, and even does camera/text translation. Fabulous for travel, reading menus abroad, and giving you that extra bit of confidence in multilingual situations. It won’t quite teach you a language (wouldn’t that be lovely), but for linguistically-minded gadget fiends it’s a fun distraction.

I will add that with consumer electronics like this, you do have to do a bit of homework – there are a lot of cheap, plasticky versions about – so always browse, check the reviews and compare before buying.

Final Christmas Thoughts

This year’s list is a fun blend of handwritten learning and functional stationery, games that trick you into practising, and a single smart gadget that (almost) earns its keep. Whether you’re buying for a learner or quietly treating yourself, these picks all support real, meaningful progress — the kind that lasts longer than Boxing Day.

Wishing all Polyglossic visitors a wonderful language-learning Christmas!

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!).

Macmillan’s “Mastering” Series: Language Learning Stalwarts of the 80s and 90s

You might remember that I was reminiscing about the forgotten Made Simple series the other week. It led me to recall another language learning series of old, and one I often mix up with Made Simple, I must admit. It’s Macmillan’s Mastering … series, another once much more familiar name in the self-paced and further ed market.

It’s not surprising that they sometimes merge into Made Simple in my language book memory. The book format itself was similar – a somewhat taller paperback, with that thick, off-white paper that feels really satisfying to turn (and crease, for fellow page corner turner-downers like me). Even the covers features that black, yellow and red of their Made Simple cousins. Like those books, these feel like grown-up courses, and, were likewise staples at night classes in the 80s and 90s.

The Mastering library overlapped with Made Simple in topics, but with a couple of important differences. For one thing, the series contained an intermediate stage for the mainstream languages; Mastering German 2, for example, is a very decent second-tier course that picks up where the first leaves off.

Impression of Mastering French I (Macmillan)

Mastering … take a slightly broader path with its titles, too. Mastering Arabic, for example, is one of the few really accessible, off-the-shelf courses in the language from the time. Perhaps that’s the reason it’s one of the titles that’s still very much with us; now acquired by Bloomsbury, Mastering Arabic continues as a respected and well-used course book today.

That’s not to say the old ones aren’t worth a look, too. And you can often pick them up on eBay for just a couple of pounds – you know we like a bargain at Polyglossic!

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! 

Perplexity Tasks for Language Learners

AI techniques to support language learning are pretty well-known now. From structured conversation partners to resource creators, LLM platforms have been embraced by the polyglot community.

Like many of us, I dip in and out of them almost unthinkingly now. Often, I’ll snap in a page from a chapter I’m working on with my Greek teacher, and it’ll help me prepare ahead of a lesson. Sometimes, I’ll get it to reel off a list of useful phrases on a topic I’m studying. LLMs can make great worksheet creators, too. In many ways, it’s simply a very interactive reference tool, giving (mostly) reliable answers but with a big nod to context.

I’d been pretty dogged in my choice of platform, sticking for the most part with ChatGPT Plus. Claude and Gemini were also in the mix, alongside some fun running local models. But for the most part, I thought my tool choices were pretty settled.

But then I gave Perplexity a whirl.

Perplexity – Task Master

Perplexity isn’t an LLM in the sense that ChatGPT, Gemini and Claude are. It uses LLM technology. But it’s actually more of an intelligent, context-sensitive search tool, that uses natural language APIs to turbo-boost its web-hunting activities.

I’d clearly not found that prospect very exciting, as I’d not gone near it until now. But thanks to a bundled free upgrade, I got to try the premium tier of late. And one particular feature stands out as potentially transformative for my learning habits: Perplexity Tasks.

Tasks are scheduled searches you set up with natural language instructions. And those instructions can be as rich as your usual LLM prompts in terms of requested formatting and such like, so in essence, you can build regular bulletins with up-to-date information in any language you like. Take one of mine, that runs daily:

Search the global news for the biggest world news story of the day. Summarise it in French, German, Modern Greek, Polish, Scottish Gaelic and Swahili at a level appropriate for an intermediate learner, ensuring that the translation is of the highest, native speaker standard quality, idiomatic and natural-sounding. Summaries should be 3-4 sentences long. Highlight key words in bold.

Accompany each summary text with a glossary / vocabulary list detailing all the key / difficult words from it in dictionary format (listing word class, irregular parts if applicable etc.). Hyperlink glossary items to Wiktionary entries where available with further information on them (use the English version en.wiktionary.com).

Lay it all out neatly to make it easy on the eye. Use plenty of emojis for impact too. Make this a fabulous resource for polyglot language learning! 🌍

Now, every morning, I get a wee news digest emailed straight to my inbox in multiple languages. It’s learner-friendly, includes vocab support, and gives me something to talk about in my language meets and lessons. I’ve done the same for academic paper searches in linguistics, and stories on dialect appearing in news outlets.

It feels like a proper game changer!

Tasking on Other Platforms

Now, you don’t need Perplexity to do this – it’s just one of the most user-friendly ways I’ve found to do it. If you have ChatGPT,  check out Scheduled Tasks. In Gemini, Scheduled Actions will do the trick for Pro members. Copilot is in on the game too. Others will no doubt follow suit shortly – clearly, task scheduling is becoming one of those features AI platforms are expected to have.

What I like about Perplexity, though, is that its whole raison d’être is the search – it feels particularly suited to web-based tasks like news digests. It’s also quite nice to keep the separation between my everyday LLM ramblings, and my more structured, scheduled items (use it for a few weeks and you’ll have clogged your timeline up with dozens of chats!).

If you’ve been looking for a way to make AI genuinely work for your learning rather than distract from it, try setting up a task or two – you might just find it becomes part of your morning ritual as well.