Image upload and analysis is one of the most game-changing recent additions to AI platforms. Combined with a knack for text recognition, it’s possibly one of the most revolutionary for language (and other!) learners, too.
In short, if it’s on a page, you can now get it into AI and do things with it. Because of this. image analysis has huge potential for extending, and breathing new life into printed materials, producing the very best synthesis of old and new tech.
The image upload icon in the Bing app.
At its very simplest, it’s a handy summary and explanation tool. Just upload your page image, and prompt:
Analyse this page from my [LANGUAGE] course book. Summarise it in a few short bullet points I can use for revision.
Useful in its own right. But with some extra prompt magic, you can produce individually tailored support material on the fly – material that will help you to delve really deeply into those language learning texts, making it work for you.
Let’s see what it can do for starters!
Working with Vocab Lists
Vocab in Context
Take the most conventional form of book-based, language learning data. Most course books have vocabulary lists and glossaries of words in the current chapter top. But beyond the dialogues or passages they are attached to, there’s rarely any other in context use of them.
Personally, I find it really helpful to see individual items embedded in sentence examples as an aide memoire. I usually seek them out in mass sentence banks and other manual-search online resources.
Even easier with AI:
Analyse this entire page from my [LANGUAGE] course book, noting all of the vocabulary items internally. Then, create a useful, practical sentence using each and every item. The sentences should relate to real-world contexts where possible. Make sure you include every single entry – don’t leave any out. Constantly double-check that the language is natural-sounding and grammatically correct. Output them in table format listing the word, your sentence and an English translation of that sentence.
ChatGPT Plus analysing a page of Swedish vocabulary.
The trick here is the analyse the entire page instruction. LLM / AI platforms tend to take shortcuts when working with lists, sometimes skipping list items. Adding this stipulation is great at keeping it on track!
Rationalising Vocab Lists
You can also sort such material in an order that works better for you. For instance, I work best with vocab when I classify it first, be that by parts of speech, topic or otherwise. AI makes light work of it:
This is the material I’m currently studying in [LANGUAGE]. First, analyse the entire page, noting all of the vocabulary items listed. Then, rewrite that list, grouping the items by their grammatical part of speech and in alphabetical order. Where the word isn’t in its simple dictionary form, provide that too. Include any entries you couldn’t categorise at the end. Double-check throughout the process that a) you haven’t left out any items, and b) that your categorisation of each item is correct. If you detect errors, start again.
Microsoft Bing analysing a page of Swahili vocabulary to create model sentences for context.
You can also combine this with the AI Anki decks trick to really digitise those paper lists.
AI Translation Exercises
Now, how about some methods for actively working with vocab? Personally, I’m a big fan of the translation method. Now I know this isn’t everybody’s cup of tea (it’s one thing that turns some off Duo) but if it works for you, you can produce a raft of exercises in seconds:
Here’s a page from my [LANGUAGE] course book. Analyse the entire page, noting all vocabulary items internally. Then, create a set of 20 practical, useful sentences using this vocabulary in context. Make them relevant to real-world, current affairs contexts where possible. Present half of these sentences in English and half in the target language for me to translate for practice. Add a key for any extra words you used that aren’t included in the list, as support. Add the translations of all sentences at the end as an answer key.
Bing analysing a page of Hebrew vocabulary to create translation exercises.
You can also extend course book pages with worksheet-style practice exercises. Here’s a prompt that should produce a diverse set of activities in an output perfect for copy-pasting into a note, or PDF, to pore through on the move:
Here’s a page from my [LANGUAGE] course book. First, analyse the entire page, noting all vocabulary items, sentence frames and grammatical structures internally. Then, create a set of worksheet-style activities for me to practise using that material. Vary the activity types, including exercises like gap-fill / cloze, matching and translation. Add an answer key to all exercises at the end.
You might even like to try a more dynamic approach with this paper-to-exercise technique. The following prompt should set up a turn-based game (my favourite kind!) that recycles chapter vocab in live conversation:
Here’s a page from my [LANGUAGE] course book. First, analyse the entire page, noting all vocabulary items, sentence frames and grammatical structures internally. Then, let’s have a conversational, turn-based activity using the material. Present me, turn by turn, with a sentence in the target language using the vocabulary. I have to provide the missing word. Don’t give me any clues or model answers until I’ve made my response each turn!
Admittedly, turn-based language gaming worked better in Bing before recent updates forced it to focus solely on being a fancy search engine. If it does stray, just remind it that you’re playing a vocabulary game!
Choose Your Platform!
All these prompts have one thing in common: they play to the power of AI to take information and display it in different ways. That’s gold for learners, as the human brain learns best when presented with material in multiple, not monotonous formats. For one thing, this helps beat the context trap of repetitious learning. Recycling vocab in as many ways as possible is key to remembering it in unlimited, unpredictable future situations.
Tech-wise, you’ll see that I’ve used Bing in most of these examples. It’s an excellent place to start if you’re new to AI yourself, not leave because it’s accessible, user-friendly and completely free! Additionally, the Bing app allows you to snap a book page easily with your phone camera. And Bing’s internet connectivity out-of-the-box gives it more breadth and up-to-the-minute relevance when creating your materials.
That said, you can use these prompts with any platform that allows image uploads. ChatGPT, for instance, has the added bonus of multiple uploads – ie., pages – so you can process a larger chunk of chapter in one go.
Whichever platform you choose, the most important piece of advice remains the same: don’t just stick with these potted prompts. Instead, experiment constantly to find what works for you, building up your own prompt library to copy-paste. AI can, and should, be an incredibly personalised experience. Good luck making it your own!
Have you used AI image analysis in your learning? Let us know your own tips and tricks in the comments!