Apples and oranges, generated by Google's new image algorithm Imagén 3

Google’s Imagén 3 : More Reliable Text for Visual Resources

If you use AI imaging for visual teaching resources, but decry its poor text handling, then Google might have cracked it. Their new algorithm for image generation, Imagén 3, is much more reliable at including short texts without errors.

What’s more, the algorithm is included in the free tier of Google’s LLM, Gemini. Ideal for flashcards and classroom posters, you now get quite reliable results when prompting for Latin-alphabet texts on the platform. Image quality seems to have improved too, with a near-photographic finish possible:

A flashcard produced with Google Gemini and Imagén 3.

A flashcard produced with Google Gemini and Imagén 3.

The new setup seems marginally better at consistency of style, too. Here’s a second flashcard, prompting for the same style. Not quite the same font, but close (although in a different colour).

A flashcard created with Google Gemini and Imagén 3.

A flashcard created with Google Gemini and Imagén 3.

It’s also better at real-world details like flags. Prompting in another engine for ‘Greek flag’, for example, usually results in some terrible approximation. Not in Imagén 3 – here are our apples and oranges on a convincing Greek flag background:

Apples and oranges on a square Greek flag, generated by Google's Imagén 3

Apples and oranges on a square Greek flag, generated by Google’s Imagén 3

It’s not perfect, yet. For one thing, it performed terribly with non-Latin alphabets, producing nonsense each time I tested it. And while it’s great with shorter texts, it does tend to break down and produce the tell-tall typos with anything longer than a single, short sentence. Also, if you’re on the free tier, it won’t allow you to create images of human beings just yet.

That said, it’s a big improvement on the free competition like Bing’s Image Creator. Well worth checking out if you have a bunch of flashcards to prepare for a lesson or learning resource!

Greek text on a packet of crisps

Language Lessons from Packaging (And A Little Help from ChatGPT)

If you love scouring the multilingual packaging of household products from discounter stores (a niche hobby, I must admit, even for us linguists), then  there’s a fun way to automate it with LLMs like ChatGPT.

Take the back of this packet of crisps. To many, a useless piece of rubbish. To me (and some of you, I hope!), a treasure of language in use.

Greek text on a packet of crisps - food and household item packaging can be a great source of language in use.

Greek text on a packet of crisps

Normally, I’d idly read through these, looking up any unfamiliar words in a dictionary. But, using an LLM app with an image facility like ChatGPT, you can automate that process. What’s more, you can request all sorts of additional info like dictionary forms, related words, and so on.

From Packaging to Vocab List

Take a snap of your packaging, and try this prompt for starters:

Create a vocabulary list from the key content words on the packaging label. For each word, list:
– its dictionary form
– a new, original sentence illustrating the word in use
– common related words

The results should be an instantly useful vocab list with added content for learning:

Vocabulary list from food packaging by ChatGPT

Vocabulary list compiled by ChatGPT from a food packaging label

I added a note-taking stage to round it off. It always helps me to write down what I’m learning, adding a kinaesthetic element to the visual (and aural, if you’ve had ChatGPT speak its notes out loud). Excuse the scrawl… (As long as your notes are readable by you, they’re just fine!)

Handwritten vocabulary notes derives from crisp packet packaging

Notes on a crisp packet…

It’s a fun workflow that really underscores the fact that there are free language lessons all around us.

Especially in the humblest, and often least glamorous, of places.

ChatGPT takes conversation to the next level with Advanced Voice Mode

ChatGPT Advanced Voice Mode is Finally Here (For Most of Us!)

Finally – and it has taken SO much longer to get it this side of the Pond – Advanced Voice Mode has popped up in my ChatGPT. And it’s a bit of a mind-blower to say the least.

Multilingually speaking, it’s a huge step up for the platform. For a start, its non-English accents are hugely improved – no longer French or German with an American twang. Furthermore, user language detection seems more reliable, too. Open it up, initiate a conversation in your target language, and it’s ready to go without further fiddling.

But it’s the flexibility and emotiveness of those voices which is the real game-changer. There’s real humanity in those voices, now, reminiscent of Hume’s emotionally aware AI voices. As well as emotion, there’s variation in timbre and speed. What that means for learners is that it’s now possible to get it to mimic slow, deliberate speech when you ask that language learning staple “can you repeat that more slowly, please?”. It makes for a much more adaptive digital conversation partner.

Likewise – and rather incredibly – it’s possible to simulate a whole range of regional accents. I asked for Austrian German, and believe me, it is UNCANNILY good. Granted, it did occasionally verge on parody, but as a general impression, it’s shocking how close it gets. It’s a great way to prepare for speaking your target language with real people, who use real, regionally marked speech.

Advanced Voice Mode, together with its recently added ability to remember details from past conversations (previously achievable only via a hack), is turning ChatGPT into a much cannier language learning assistant. It was certainly worth the wait. And for linguaphiles, it’ll be fascinating to see how it continues to develop as an intelligent conversationalist from here.

Mapping out conversational probabilities - it's much easier with flowcharts.

Vocabulary Flowcharts : Preparing for Probabilities with ChatGPT

The challenge in preparing for a speaking task in the wild is that you’re dealing with multiple permutations. You ask your carefully prepared question, and you get any one of a number of likely responses back. That, in turn, informs your next question or reply, and another one-of-many comebacks follows.

It’s probability roulette.

What if you could map all of these conversational pathways out, though? Flowcharts have long been the logician’s tool of choice for visualising processes that involve forking choices. Combined with generative AI’s penchant for assembling real-world language, we have a recipe for much more dynamic language prep resources than a traditional vocab list.

And, thanks to a ready-made flowchart plugin for ChatGPT – courtesy of the charting folks at Whimsical.com – it’s really easy to knock one together.

Vocabulary Flowcharts in Minutes

In your ChatGPT account, you’ll need to locate the Whimsical GPT. Then, it’s just a case of detailing the conversational scenario you want to map out. Here’s an example for ‘opening a bank account in Germany’:

Create a flowchart detailing different conversational choices and paths in German for the scenario “Opening a bank account as a non-resident of Germany planning to work there for six months.” Include pathways for any problems that might occur in the process. Ensure all the text reflects formal, conversational German.

The result should be a fairly detailed ‘probability map’ of conversational turns:

A 'vocabulary flowchart' in German, created by the Whimsical.com GPT on ChatGPT.

A ‘vocabulary flowchart’ in German, created by the Whimsical.com GPT on ChatGPT.

Vocabulary flowcharts are another tool in your AI arsenal for speaking prep. Have you given them a whirl yet? Tell us about your own prep in the comments!

Lots of colourful neon shortcuts on a screen.

Apple Shortcuts for Smart Study Hacks

I’ve been optimising my iPad for study recently, trying to make it a portable, one-stop shop for reading and prep on-the-move. It’s no doubt another flex of my childhood awe for Inspector Gadget’s niece Penny, and her computer-in-a-book that could do everything.

Anyway, it’s led me to discover lots of features I half knew about, but had ignored for the duration of my Apple fanboydom. And one of the biggest revelations of rediscovery has been the Apple Shortcuts app.

In a nutshell, Shortcuts allows you to bundle all sorts of custom process chains into a single, iconisable action. For example, a shortcut could retrieve something, do something with the result, then present it in a certain way. People use them for all sorts of admin tasks – collecting stock prices and collating them in a spreadsheet with a single click, for instance.

The cool thing is that many third-party apps have extensions that you can link into shortcuts. ChatGPT, for example, which I find invaluable as a quick summary or explanatory tool, can be part of an action chain. And you can trigger chains not just with clicks, but from documents, via the share link.

Shortcuts for Smart Study : Brief Description

Using the app, I put together a quick shortcut I called “Brief Description”. It sends the current PDF (from a browser window, or from the Files app), to ChatGPT, prompting it for a one-paragraph summary.

A screenshot from Apple Shortcuts of a 'Brief Explanation' action using ChatGPT.

A screenshot from Apple Shortcuts of a ‘Brief Explanation’ action using ChatGPT.

As you can see, one of the best things about it is how you assemble a shortcut in more or less natural language, as you select items via an intuitive click-and-build interface. There are also plenty of resources for getting started and pushing the boundaries of it (like this very clued-up YouTube channel!).

The result of my shortcut offers a great way to get a paper summary, whatever the language:

Screenshot of an Apple Shortcuts link in the share menu.

Screenshot of an Apple Shortcuts link in the share menu.

When working through a bunch of resources, it’s great to ascertain in a click or two whether it might be a worthwhile full read, making this a brilliant time-saver. It’s even more powerful when combined with ChatGPT’s new(ish) memory features – my account ‘knows’ what I’ve been working on from recent chats, so is even better able to judge what’s useful.

For sure, I’ll be exploring more of this over the coming weeks, as it’s clear I’ve barely scratched the surface of how useful it could be yet. Have you used Apple Shortcuts to create smart study hacks? Let us know in the comments!

Shelves of helpful robots - a bit like Poe, really!

Which LLM? Poe offers them all (and some!)

One of the most frequent questions when I’ve given AI training to language professionals is “which is your favourite platform?”. It’s a tricky one to answer, not least because we’re currently in the middle of the AI Wars – new, competing models are coming out all the time, and my personal choice of LLM changes with each new release.

That said, I’m a late and recent convert to Poe – an app that gives you them all in one place. The real clincher is the inclusion of brand new models, before they’re widely available elsewhere.

To illustrate just how handy it is, just a couple of weeks ago, Meta dropped Llama 3.1 – the first of their models to really challenge the frontrunners. However, unless you have a computer powerful enough to run it locally, or access to Meta AI (US-only right now), you’ll be waiting a while to try it.

Enter Poe. Within a couple of days, all flavours of Llama 3.1 were available. And the best thing? You can interact with most of them for nothing.

The Poe Currency

Poe works on a currency of Compute Points, which are used to pay for messages to the model. More powerful models guzzle through compute points at a higher rate, and models tend to become cheaper as they get older. Meta’s Llama-3.1-405B-T, for example, costs 335 points per message, while OpenAI’s ChatGPT-4o-Mini comes in at a bargain 15 points for each request.

Users of Poe’s free tier get a pretty generous 3000 Compute Points every day. That’s enough credit to work quite extensively on some of the older models without much limitation at all. But it’s also enough to get some really useful (8-ish-requests daily) use from Llama 3.1. And, thanks to that, I can tell you – Llama 3.1 is great at creating language learning resources!

Saying that, with the right prompt, most of the higher-end models are, these days. Claude-3.5-Sonnet is another favourite – check out my interactive worksheet experiments with it here. And yes, Claude-3.5-Sonnet is available on Poe, at a cost of 200 points per message (and that’s already dropped from its initial cost some weeks back!). Even the image generation model Flux has made its way onto the platform, just days after the hype. And it’s a lot better with text-in-image (handy if you’re creating illustrated language materials).

Poe pulls together all sorts of cloud providers in a marketplace-style setup to offer the latest bots, and it’s a model that works. The latest and greatest will always burn through your stash of Computer Points faster, but there’s still no easier way to be amongst the first to try a new LLM!

AI Parallel Texts for Learning Two Similar Languages

I’ve seen a fair few social media posts recently about linguist Michael Petrunin’s series of Comparative Grammars for polyglots. They seem to have gone down a storm, not least because of the popularity of triangulation as a polyglot strategy.

They’re a great addition to the language learning bookshelf, since there’s still so little formal course material that uses this principle. Of course, you can triangulate by selecting course books in your base language, as many do with Assimil and other series like the Éditions Ellipse.

Parallel Texts à la LLM

But LLMs like ChatGPT, which already do a great job of the parallel text learning style, are pretty handy for creative comparative texts, too. Taking a story format, here’s a sample parallel text prompt for learners of German and Dutch. It treats each sentence as a mini lesson in highlighting differences between the languages.

I’m learning Dutch and German, two closely related languages. To help me learn them in parallel and distinguish them from each other, create a short story for me in Dutch, German and English in parallel text style. Each sentence should be given in Dutch, German and English. Purposefully use grammatical elements which highlight the differences between the languages, which a student of both does need to work hard to distinguish, in order to make the text more effective.

The language level should be lower intermediate, or B1 on the CEFR scale. Make the story engaging, with an interesting twist. Format the text so it is easy to read, grouping the story lines together with each separate sentence on a new line, and the English in italics.

You can tweak the formatting, as well as the premise – specify that the learner already speaks one of the languages more proficiently than the other, for example. You could also offer a scenario for the story to start with, so you don’t end up with “once upon a time” every run. But the result is quite a compact, step-by-step learning resource that builds on a comparative approach.

ChatGPT creating parallel texts in German and Dutch with an English translation.

ChatGPT creating parallel texts in German and Dutch with an English translation.

Variations and Limitations

I also tried prompting for explanatory notes:

Where the languages differ significantly in grammar / syntax, add an explanatory note (in English) to the sentences, giving details.

This was very hit and miss, with quite unhelpful notes in most runs. In fact, this exposes the biggest current limitation of LLMs: they’re excellent content creators, but still far off the mark in terms of logically appraising the language they create.

It is, however, pretty good at embellishing the format of its output. The following variation is especially impressive in an LLM platform that shows a preview of its code:

I’m learning Spanish and Portuguese, two closely related languages. To help me learn them in parallel and distinguish them from each other, create a short story for me in Spanish, Portuguese and English in parallel text style. Each sentence should be given in Spanish, Portuguese and English. Purposefully use grammatical elements which highlight the differences between the languages, which a student of both does need to work hard to distinguish, in order to make the text more effective.

The language level should be lower intermediate, or B1 on the CEFR scale. Make the story engaging, with an interesting twist.

The output should be an attractively formatted HTML page, using a professional layout. Format the sentences so they are easy to read, grouping the story lines together with each separate sentence on a new line, and the English in italics. Hide the English sentences first – include a “toggle translation” button for the user.

Claude by Anthropic creating an HTML-formatted parallel story in Spanish and Portuguese.

Claude by Anthropic creating an HTML-formatted parallel story in Spanish and Portuguese.

It’s another use case that highlights LLMs’ greatest strength: the creation of humanlike texts. For linguists, it matters not a jot how much (or little) deep understanding there is beneath that. With the language quality now almost indistinguishable from real people-speak, AI texts serve as brilliant ‘fake authentic’ language models.

e-Stories as parallel texts are yet another fun, useful flavour of that!

Robots exchanging gifts. We can exchange - and adapt - digital resources now, with Claude's shareable Artifacts.

Sharing Your Language Learning Games with Claude Artifacts

If Claude’s recent improvements weren’t already impressive enough, Anthropic has only gone and done it again – this time, by making Artifacts shareable.

Artifacts are working versions of the programs and content you, the user, prompt for in Claude. For example, they pop up when you ask the AI to write a language practice game in HTML, running the code it writes as a playable activity. Instant language learning games – no coding required.

Now, you can share your working, fully playable creations, with a simple link.

Instant Spanish Quiz with Claude

Take this simple Spanish quiz (very topical given the forthcoming Euros 2024 final!). I prompted for it as follows:

Create an original, self-contained quiz in Spanish for upper beginner / lower intermediate students of the language, on the topic “Spain in the European Football Championships”. It should be completely self-contained in an HTML page. The quiz should be multiple choice, with ten questions each having four alternative answer buttons – only one is right, and there is always one ‘funny’ alternative answer in the mix too.

Every time the quiz is played, the questions and the answers are in a random order. The student can keep trying answers until they get the right one (obviously after clicking an answer button, it should be disabled). Incorrect buttons turn red – correct ones green. Keep score of the player’s accuracy as they work through the questions (number of correct clicks / total clicks).

Make sure it looks attractive, slick and smart too, with CSS styling included in the HTML page.

If you have Artifacts turned on (see here for more). you should see your working game appear in a new pane. But now, you’ll also see a little Publish link in the bottom-right corner. Click this, and you can choose to make your creation public with an access link.

Publishing your working language activities using a share link with Claude Artifacts

Publishing your working language activities using a share link with Claude Artifacts

Remixing Artifacts

But wait – there’s more. When colleagues access your Artifact, they will see a Remix button in that bottom-right corner.

Remixing Artifacts in Claude

Remixing Artifacts in Claude

By hitting that, they can pick up where you left off and tweak your materials with further prompting. For instance, to keep the quiz format but change the language and topic, they could simply ask:

Now create a version of this quiz for French learners on the topic “France at the Olympic Games”.

It makes for an incredibly powerful way to network your learning resources. It’s also perfectly possible to take advantage of all this using only Claude’s free tier, which gives you 10 or so messages every few hours.

More than enough to knock up some learning games.

Have you created anything for colleagues to adapt and share on in Claude? Let us know in the comments!

A cute robot coding at an old-fashioned computer terminal. AI code generation is great for making language games!

AI for Language Games : We’re All Developers, Now!

I’ve been developing games for language learners for over two decades now. Learning those programming skills was a labour of love, started when I was still a classroom teacher. Honing my own coding skills took years of practice. But now, thanks to ever-improving generative AI models, you can skip that step.

All it takes to create interactive language games is a good set of prompts!

Generative AI is an uncanny fit for language learners and teachers, who quickly realised how useful it could be for authentic(ish) text creation. But it doesn’t have to be static. By specifying the kind of features you want in a prompt, you can come up with great self-contained digital ‘worksheets’ with self-marking activities.

It’s possible to go well beyond this – into actual interactive gaming. Today’s generation of AI platforms are capable of taking your brief, then coding it up efficiently and intelligently as a working game, without any further input from you.

Now, if you can imagine it, you can make it.

Language Tetris

Let’s take a classic gaming example: a version of Tetris with a language learning twist. Blocks fall from the top of the screen, labelled with a word in either German or English. Students must manoeuvre the blocks with the arrow keys in order to land German-English equivalents together, whereupon they pop and disappear from the stack. The game speeds up as students progress; the aim is to prevent the blocks from stacking to the top for as long as possible.

It’s fun, and fantastic for improving recall with a set of vocab items.

An AI-generated interactive language learning game. Blocks fall from the top of the screen - the student must match them and avoid them piling to the top.

An AI-generated interactive language learning game. Blocks fall from the top of the screen – the student must match them and avoid them piling to the top. (Pictured is a ‘boom’ block that gives the students a lifeline!)

You can play a working version of it here, and you’ll see what I mean: it’s fun, it gets quite fast and furious, and it does a great job of drilling words. It may not look particularly pretty in its current state, but it’s completely playable; with a bit of visual sprucing, it wouldn’t look out of place on any language learning website.

It’s the kind of thing that would make a nice intermediate coding challenge for someone learning web app development. Maybe a weekend project, or something to do across a series of evenings.

But it took just a few minutes with ChatGPT.

Prompting for Language Games

Here’s the prompt I used for the initial version (and you’ll see some similarities with the interactive worksheet prompt, too):

Let’s create a language learning game in JavaScript, completely self-contained on a single HTML page. It will be like Tetris, adapted for language learning as follows:
– blocks will have either a German or an English word on them from a pot of ten vocabulary items on the topic ‘Clothing’
– blocks will descend from the top of the screen
– as they fall, students use the left, right and down arrows to manoeuvre the blocks before they land
– if a matching German block and English block touch (above, below or to the side), they go POP and disappear – and the game speeds up slightly
– ensure that ‘gravity’ is respected, so if there are blocks above one that ‘pops’, they fall into place accordingly
– the aim is to avoid the blocks stacking up to the top
– every few blocks (maybe every 5-10 at random), a ‘boom’ block falls that ‘pops’ every block it touches on landing (just to clear the space and make it a bit easier)

If you pop this into Claude Sonnet 3.5 right now – as long as you have Artifacts turned on – you should be able to play what comes up straight away. If you’re using ChatGPT or another platform, there’s an extra (easy-ish) step to do before you can play: you just need to save your code in some text editor as an HTML file.

Code output from a prompt in ChatGPT

Code output from a prompt in ChatGPT

Live Preview – Without Claude’s Artifacts

In fact, one free editor for coding – Phoenix Code – also shows you a live preview of the page working as you paste the code in, Claude Artifacts-style. If you really get into language games generation with AI, it’s well worth a download.

Using Phoenix to save and preview AI-generated language games

Using Phoenix Code to save and preview AI-generated language games

One important caveat: your game may well not be perfect on the first go. It might have a bug or two – AI might have missed the point occasionally. My initial version of Language Tetris, for example, allowed students to move the block across existing columns, unlike in the classic game.

But by prompting and re-prompting, requesting tweaks and changes as you go along, you can produce some amazing results. Change the styling, add features, include fiendish rules of play.

The sky’s the limit.

Pulling It All Together

Once you’re done with one game, it can serve as a template for others. It’s usually clear in the code where to change the vocabulary items to something else. Just change, Save As… a new filename, and build up a library of topical games.

A snippet of code produced by ChatGPT

It’s usually clear from a glance at the code where you can change the vocabulary.

In terms of styling, these games do tend to be graphically quite simple. That said, you can easily prompt for more visually appealing elements. And why not use a AI image generation tool like Bing to make some more attractive graphics to integrate into your creations?

Finally, you might be wondering if educational games developers like me are feeling a bit… well, put out by all this. My answer is a resounding not at all! If anything, AI code generation is a brilliant proof-of-concept, prototyping tool to try out new gaming ideas before setting fingers to keyboard. It’s incredibly useful to test if something will really work before pouring hours into coding it.

And of course, you can pick up with your own skills where AI leaves off, to create something even more special with that irreplaceable human touch.

Have you been using AI as your own coding assistant? Let us know in the comments what you’ve been creating together!

A language learning topic menu created by Claude AI.

Claude Artifacts for Visually Inspired Language Learning Content

If you create language learning content – for yourself, or for your students – then you need to check out the latest update to Claude AI.

Along with a competition-beating new model release, Anthropic have added a new feature called Artifacts to the web interface. Artifacts detects when there is self-contained content it can display – like webpage code, or worksheet text – and it pops that into a new window, running any interactive elements on the fly. In a nutshell, you can see what you create as you create it.

This makes it incredibly easy to wrap your learning content up in dynamic formats like HTML and JavaScript, then visually preview and tweak it to perfection before publishing online. This favours interactive elements like inline games, which can be impressively slick when authored by Claude’s Sonnet 3.5; it turns out that model update is a real platform-beater when it comes to coding.

Using Claude’s new Artifacts Feature

You can give Artifacts a whirl for free, as Claude’s basic tier includes a limited number of interactions with its top model every few hours. That’s more than enough to generate some practical, useful material to use straight away.

First of all, you’ll need to ensure that the feature is enabled. After logging into Claude, locate the little scientific flask icon by the text input and click it.

Claude - locating the experimental features toggle

Claude – locating the experimental features toggle

A window should pop up with the option to enable Artifacts if it’s not already on.

Claude - enabling Artifacts.

Claude – enabling Artifacts.

Now it’s on, you just need a prompt that will generate some ‘Artifactable’ content. Try the prompt below for an interactive HTML worksheet with a reading passage and quiz:

Interactive HTML Worksheet Prompt

Create an original interactive workbook for students of French, as a self-contained, accessible HTML page. The target language level should be A2 on the CEFR scale. The topic of the worksheet is “Summer Holidays“. The objective is to equip students with the vocabulary and structures to chat to native speakers about the topic.

The worksheet format is as follows:

– An engaging introductory text (250 words) using clear and idiomatic language
– A comprehensive glossary of key words and phrases from the text in table format
– A gap-fill exercise recycling the vocabulary and phrases – a gapped phrase for each question with four alternative answer buttons for students to select. If they select the correct one, it turns green and the gap in the sentence is filled in. If they choose incorrectly, the button turns red. Students may keep trying until they get the correct answer.

Ensure the language is native-speaker quality and error-free. Adopt an attractive colour scheme and visual style for the HTML page.

With Artifacts enabled, Claude should spool out the worksheet in its own window. You will be able to test the interactive elements in situ – and then ask Claude to tweak and update as required! Ask it to add scoring, make it drag-and-drop – it’s malleable ad infinitum.

An interactive worksheet created by Claude.ai, displaying in the new Artifacts window

An interactive worksheet created by Claude, displaying in the new Artifacts window

Once created, you can switch to the Artifacts Code tab, then copy-paste your page markup into a text editor to save as an .html file. Then, it’s just a case of finding a place to upload it to.

Pulling It Together

After you’re done with the worksheets, you can even ask Claude to build a menu system to pull them all together:

Now create a fun, graphical, colourful Duolingo-style topic menu which I can use to link to this worksheet and others I will create. Use big, bold illustrations. Again, ensure that it is a completely self-contained HTML file.

Here’s the result I got from running that – again, instantly viewable and tweakable:

A language website menu created by Claude.ai, displayed in Claude's Artifacts feature.

A language website menu created by Claude, displayed in the Artifacts feature.

You’ve now got the pieces to start to stitch together into something much bigger than a single worksheet.

Instant website – without writing a line of code!

Have you had chance to play with Claude’s new Artifacts feature yet? Let us know in the comments what you’ve been creating!