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Here, we look at what some of the strangest English phrases mean – and reveal their origins…

Bite the bullet

Biting a bullet? What a strange thing to do! This phrase means you’re going to force yourself to do something unpleasant or deal with a difficult situation. Historically, it derives from the 19th century when a patient or soldier would clench a bullet between their teeth to cope with the extreme pain of surgery without anesthetic. A similar phrase with a similar meaning, “chew a bullet”, dates to the late 18th century.

Use it: “I don’t really want to exercise today, but I’ll bite the bullet and go for a run.”

Pigs might fly

We all know that pigs can’t fly, so people use this expression to describe something that is almost certain never to happen. It is said that this phrase has been in use since the 1600s, but why pigs? An early version of the succinct “pigs might fly” was “pigs fly with their tails forward”, which is first found in a list of proverbs in the 1616 edition of John Withals’s English-Latin dictionary, A Shorte Dictionarie for Yonge Begynners: “Pigs fly in the ayre with their tayles forward.” Other creatures have been previously cited in similar phrases – “snails may fly”, “cows might fly”, etc, but it is pigs that have stood the test of time as the favored image of an animal that is particularly unsuited to flight! This phrase is also often used as a sarcastic response to mock someone’s credulity.

Use it: “I might clean my bedroom tomorrow.” – “Yes, and pigs might fly.”

Bob’s your uncle

Even if you don’t have an uncle called Bob, you might still hear this idiom! Its origin comes from when Arthur Balfour was unexpectedly promoted to Chief Secretary for Ireland by the Prime Minister of Britain, Lord Salisbury, in 1900. Salisbury was Arthur Balfour’s uncle (possibly his reason for getting the job!) – and his first name was Robert. This phrase is used when something is accomplished or successful – an alternative to “…and that’s that”.

Use it: “You’re looking for the station? Take a left, then the first right and Bob’s your uncle – you’re there!”

Dead ringer

This phrase commonly refers to something that seems to be a copy of something – mainly if someone looks like another person. The often-repeated story about the origin of this phrase is that many years ago, people were sometimes buried alive because they were presumed dead – when actually they were still alive. To prevent deaths by premature burial, a piece of string would supposedly be tied to the finger of someone being buried – and the other end would be attached to a bell above ground. If the person woke up, they would ring the bell – and the “dead” ringer would emerge looking exactly like someone buried only a few hours ago! Other stories point to the practice of replacing slower horses with faster horses – “ringers”. In this case, “dead” means “exact”.

Use it: “That guy over there is a dead ringer for my ex-boyfriend.”

Off the back of a lorry

This is a way of saying that something was acquired that is probably stolen, or someone is selling something that’s stolen or illegitimate. It can also be used humorously to emphasize that something you bought was so cheap that it must have been stolen! “Lorry” is the British version – in the US, things fall off the back of “trucks”. An early printed version of this saying came surprisingly late in The Times in 1968. However, there are many anecdotal reports of the phrase in the UK from much earlier than that, and it is likely to date back to at least World War II. It’s just the sort of language that those who peddled illegal goods during and after WWII would have used.

Use it: “I can’t believe these shoes were so cheap – they must have fallen off the back of a lorry.”

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Automated assessment, including the use of Artificial Intelligence (AI), is one of the latest education tech solutions. It speeds up exam marking times, removes human biases, and is as accurate and at least as reliable as human examiners. As innovations go, this one is a real game-changer for teachers and students. 

However, it has understandably been met with many questions and sometimes skepticism in the ELT community – can computers really mark speaking and writing exams accurately? 

The answer is a resounding yes. Students from all parts of the world already take AI-graded tests.  a�Ի� Versant��tests – for example – provide unbiased, fair and fast automated scoring for speaking and writing exams – irrespective of where the test takers live, or what their accent or gender is. 

This article will explain the main processes involved in AI automated scoring and make the point that AI technologies are built on the foundations of consistent expert human judgments. So, let’s clear up the confusion around automated scoring and AI and look into how it can help teachers and students alike. 

AI versus traditional automated scoring

First of all, let’s distinguish between traditional automated scoring and AI. When we talk about automated scoring, generally, we mean scoring items that are either multiple-choice or cloze items. You may have to reorder sentences, choose from a drop-down list, insert a missing word- that sort of thing. These question types are designed to test particular skills and automated scoring ensures that they can be marked quickly and accurately every time.

While automatically scored items like these can be used to assess receptive skills such as listening and reading comprehension, they cannot mark the productive skills of writing and speaking. Every student's response in writing and speaking items will be different, so how can computers mark them?

This is where AI comes in. 

We hear a lot about how AI is increasingly being used in areas where there is a need to deal with large amounts of unstructured data, effectively and 100% accurately – like in medical diagnostics, for example. In language testing, AI uses specialized computer software to grade written and oral tests. 

How AI is used to score speaking exams

The first step is to build an acoustic model for each language that can recognize speech and convert it into waveforms and text. While this technology used to be very unusual, most of our smartphones can do this now. 

These acoustic models are then trained to score every single prompt or item on a test. We do this by using human expert raters to score the items first, using double marking. They score hundreds of oral responses for each item, and these ‘Standards’ are then used to train the engine. 

Next, we validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. If this doesn’t happen for any item, we remove it, as it must match the standard set by human markers. We expect a correlation of between .95-.99. That means that tests will be marked between 95-99% exactly the same as human-marked samples. 

This is incredibly high compared to the reliability of human-marked speaking tests. In essence, we use a group of highly expert human raters to train the AI engine, and then their standard is replicated time after time.  

How AI is used to score writing exams

Our AI writing scoring uses a technology called . LSA is a natural language processing technique that can analyze and score writing, based on the meaning behind words – and not just their superficial characteristics. 

Similarly to our speech recognition acoustic models, we first establish a language-specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. 

Once the language model has been established, we train the engine to score every written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. 

The benchmark is always the expert human scores. If our AI system doesn’t closely match the scores given by human markers, we remove the item, as it is essential to match the standard set by human markers.

AI’s ability to mark multiple traits 

One of the challenges human markers face in scoring speaking and written items is assessing many traits on a single item. For example, when assessing and scoring speaking, they may need to give separate scores for content, fluency and pronunciation. 

In written responses, markers may need to score a piece of writing for vocabulary, style and grammar. Effectively, they may need to mark every single item at least three times, maybe more. However, once we have trained the AI systems on every trait score in speaking and writing, they can then mark items on any number of traits instantaneously – and without error. 

AI’s lack of bias

A fundamental premise for any test is that no advantage or disadvantage should be given to any candidate. In other words, there should be no positive or negative bias. This can be very difficult to achieve in human-marked speaking and written assessments. In fact, candidates often feel they may have received a different score if someone else had heard them or read their work.

Our AI systems eradicate the issue of bias. This is done by ensuring our speaking and writing AI systems are trained on an extensive range of human accents and writing types. 

We don’t want perfect native-speaking accents or writing styles to train our engines. We use representative non-native samples from across the world. When we initially set up our AI systems for speaking and writing scoring, we trialed our items and trained our engines using millions of student responses. We continue to do this now as new items are developed.

The benefits of AI automated assessment

There is nothing wrong with hand-marking homework tests and exams. In fact, it is essential for teachers to get to know their students and provide personal feedback and advice. However, manually correcting hundreds of tests, daily or weekly, can be repetitive, time-consuming, not always reliable and takes time away from working alongside students in the classroom. The use of AI in formative and summative assessments can increase assessed practice time for students and reduce the marking load for teachers.

Language learning takes time, lots of time to progress to high levels of proficiency. The blended use of AI can:

• address the increasing importance of formative assessment��to drive personalized learning and diagnostic assessment feedback 

• allow students to practice and get instant feedback inside and outside of allocated teaching time

• address the issue of teacher workload

• create a virtuous combination between humans and machines, taking advantage of what humans do best and what machines do best. 

• provide fair, fast and unbiased summative assessment scores in high-stakes testing.

We hope this article has answered a few burning questions about how AI is used to assess speaking and writing in our language tests. An interesting quote from Fei-Fei Li, Chief scientist at Google and Stanford Professor describes AI like this:

“I often tell my students not to be misled by the name ‘artificial intelligence’ — there is nothing artificial about it; A.I. is made by humans, intended to behave [like] humans and, ultimately, to impact human lives and human society.”

AI in formative and summative assessments will never replace the role of teachers. AI will support teachers, provide endless opportunities for students to improve, and provide a solution to slow, unreliable and often unfair high-stakes assessments.

Examples of AI assessments in ELT

At ����app, we have developed a range of assessments using AI technology.

Versant

The Versant tests are a great tool to help establish language proficiency benchmarks in any school, organization or business. They are specifically designed for placement tests to determine the appropriate level for the learner.

PTE Academic

The  is aimed at those who need to prove their level of English for a university place, a job or a visa. It uses AI to score tests and results are available within five days. 

English Benchmark

English Benchmark is also scored using the same automated assessment technology. This test, which is taken on a tablet, is aimed at young learners and takes the form of a fun, game-like test. Covering the skills of speaking, listening, reading and writing, it measures the student’s ability and suggests follow-up activities and next teaching steps.