Turning three texts into a task-based lesson with ChatGPT

How ChatGPT helped me turn three business texts into a task-based lesson that my student loved.

A student recently sent me three business documents: two case studies and one press release. They were all in Czech, her first language, and her request was vague: “describe on English and do some quick points to my LinkedIn.” No instructions, no priorities — just the documents.

My aim, as usual, was to make the experience task-based rather than a one-way translation service. The question was: how to use these texts to generate LinkedIn-ready material while also giving her a strong learning experience.

Step 1: What ChatGPT did

I gave the documents to ChatGPT without any translation into English. It read them directly in Czech and produced everything in English: lists of key vocabulary, verb + preposition patterns, noun + preposition structures, common collocations, and stylistic features such as the passive voice and nominalisations. This immediately gave us a map of useful building blocks to shape into a lesson.

Step 2: My ideas unfolded

As I looked at the extracted language, I saw the possibility of creating a set of LinkedIn-style bullet points that captured the key ideas from the three texts. The idea then grew into two layers of activity:

Sorting by project (which bullet belongs to which case study or press release).
Sorting by type: hard facts vs value-add phrases.

That meant the student wasn’t just reading polished text; she was working with raw material, noticing patterns, and making decisions.

Sample of the bullets (half of the full set)

Implemented over 80 digital components in a single solution.
Improved candidate experience and strengthened employer branding.
Delivered a scalable SaaS solution for (number) authorised dealers.
Presented electromobility models and their availability online.
Educated customers about sustainability and green mobility.
Built tools to showcase services and car offers consistently across the dealer network.
Contributed to (company name)’s position in the market.

Step 3: The worksheet

With ChatGPT’s help, I created a one-page worksheet in minutes. It contained:

A list of mixed bullet points from all three documents.
Instructions to sort them by project and by fact/value.
A set of frames for turning facts into stronger LinkedIn statements.

Step 4: From facts to value

The worksheet also included frames that allow facts to be reshaped into value-add statements, such as:

Contributed to + [bigger goal] by + [action]
Focused on + [area] to + [outcome]
Implemented + [change] which + [result]
Strengthened + [area] through + [method]
Delivered + [result] leading to + [impact]

She took these away for homework, with the plan of combining them with the bullet points in the coming weeks. This will help her prepare LinkedIn content while developing a clearer sense of how English expresses impact and achievement in corporate settings.

What the student experienced

She encountered new vocabulary directly relevant to her field.
She saw known words used in ways she had never imagined, and certainly beyond her ability to use on her own.
She realised that pronunciation was a major hurdle with the new words — and that the main suprasegmental challenge for her is linking.

Reflection

ChatGPT enabled me to process three Czech business documents into a set of English bullets and frames almost instantly. My role was to let my ideas unfold as I saw the language it had extracted, shaping it into tasks that engaged the student in sorting, selecting, and adapting.

It was wonderful to be able to tailor-make such a specific lesson for a student who needed to work with particular texts for a very particular purpose. For me to read the documents in Czech (which I could have done) and identify areas of language that could become teaching points while also serving her need to update her LinkedIn presence would have taken me a whole morning. Instead, it took about 30 minutes to produce this focused, customised lesson.

In the future, she will be able to produce LinkedIn content while also developing a clearer grasp of English corporate language. The result of this first attempt was a strong start, and the student was delighted.

Illustrative sentences

29/7/2024

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Illustrative sentences

Language learners benefit greatly from example sentences, since it is an opportunity to learn language from language, my big thing. For this reason, I devoted a considerable amount of my teaching, training and writing to helping students gain the maximum benefit from illustrative sentences.

In the early 2000s, I attended my first Teaching and Language Corpora conference in Bertinoro, a beautiful hilltop town near Bologna, and presented my incipient formula for computationally selecting the most useful sentences from corpora to present to students. I programmed a tool that allocated the frequency of every word in a sentence and average it. Sentence length was also a criterion.

As mentioned in previous posts, the great English lexiocagrapher, Patrick Hanks was my colleague at this time and I asked him what criteria his kind used when selecting sentences to include in their dictionaries. He said there was no list.

I worked on this further and came up with a list of ten criteria that I discussed with Patrick and he added one more. I gave this list to Pavel Rychlý, who was developing Sketch Engine and his team used these criteria as a basis for their GDEX algorithm, i.e. good example sentences. It is now a standard part of SkELL and Sketch Engine. My criteria are listed on this 2006 webpage.

So, it’s a good thing that corpora can select illustrative sentences, but can students? And should they? In short, yes and yes. But then what? How does a learner know what they can learn from an illustrative sentence apart from it being a targeted piece of input which they might soak in, as they do from any input they are exposed to. The answer lies in knowing the properties of the target word that are necessary to shift it from active to passive use.

I am a strong advocate of the Collins COBUILD Advanced Learner’s Dictionary because it even presents its definitions in full sentences. Full sentence definitions are goldmines. From the sentence defintion, you can easily extract concept checking questions (CCQs). For example,

Collins: A wildcard is a symbol such as * or ? which is used in some computing commands or searches in order to represent any character or range of characters.

What is a wildcard? A symbol
What are some examples of wildcard? * or ?
Where is it used? In computing commands
What is its function? to represent any character …

Collins: An aphorism is a short witty sentence which expresses a general truth or comment.

What is an aphorism? a short witty sentence.
What does it do? it expresses a general truth or comment.

These sentences typically start with a hypernym, here symbol, which immediately limits what it is and is not. Their definitions progress with the target word’s features, functions, etc. Each of these is encapsulated in a phrase or clause in the sentence definition. They are the properties of the word. The Collins then provides example sentences in which the abstract properties are made concrete. If students know what they can learn from full sentence definitions, they can see how the meanings of words manifest in authentic sentences.

I’m writing a student workbook at the moment which will probably be called Discovering Phrasal Verbs, in which students are repeatedly tasked with finding example sentences in corpora. The book explains the importance of the semantics of the phrasal verb particles (prepositions and adverbs) and the importance of the subjects and objects of the verbs. These properties are the most important contributors to the meanings of the otherwise opaque, or at best translucent, phrasal verbs.

When you search corpora for a phrasal verb, the sheer volume of data can be overwhelming. Fortunately, SkELL uses GDEX, so the 40 sentences it presents are manageable. The other tool I recommend is CorpusMate because it is very fast, it enables searches with wildcards, and the cotext is colour-coded using the same colours for parts of speech as VersaText. The wildcard searches are necessary when the phrasal verb is separable, e.g. tear .* away, keep .* .* away.

AI is another source of illustrative sentences. In ChatGPT's own words, "The sentences generated by AI are original constructs, created using the language patterns learned during training." They are by definition inauthentic sentences, which means they were not motivated by any communicative impetus, hence they lack real-world contexts. These sentences often resemble those made up by textbook authors and test creators. It is reasonable to ask if the trade-off between authentic and inauthentic example sentences in terms of learnability is worth it. Do students really benefit more from authentic than inauthentic sentences?

Like all good questions in ELT, the answer starts with, it depends. My it depends revolves around what the students are tasked with. If the textbook provides made-up example sentences without any task other than perhaps read, read aloud, translate or memorise some sentences, the students will function at the bottom of Bloom's Taxonomy. Garbage in, garbage out.

But if the tasks involve higher order thinking skills in which the students skim and scan multiple examples of authentic language in search of specific properties to which they have been alerted, they develop a better understanding of the properties of the target word, and ultimately a more sophisticated understanding of language per se emerges. Like all good citizen-scientists, students engaged in “extreme noticing” need systems to record their findings that will in turn deepen their conceptual grasp of the target language and prepare them to use it confidently.

It is well-known that guided discovery is not for everyone. I was a school music teacher in my 20s and one would occasionally hear, Never try to teach a pig to sing: it wastes your time and annoys the pig. This is yet another aphorism attributed to Mark Twain, but who knows?

Guided discovery demands a strong rationale, clear instructions, the right tools and an understanding that the students are going to benefit from the multiple affordances of the tasks. It is important that students are made aware of the multiplicity of these learning experiences in the process of acquiring words and their properties. No reflection, no connection.

The sentence is a suitable unit of language to observe the cotext of a word, i.e., its collocations, colligations, its subjects and objects and other properties depending on the part of speech. When you see the word in multiple sentences, as concordances provide, you can discern typical properties. This process of pattern recognition is akin to first language acquisition (FLA), but in SLA, our guided discovery tasks bring it to the surface, making awareness conscious. Given the best scaffolding, students can learn a great deal from illustrative sentences.

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