Can computers really mark exams? Benefits of ELT automated assessments

app Languages
Hands typing at a laptop with symbols

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.  aԻ Versanttests – 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 assessmentto 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. 

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    Teaching teenagers can be a rewarding yet challenging experience.

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    Commonly referred to as (or Gen Z), they are the most tech-savvy generation to date; this should be reflected in how we teach them.

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    1. Review target language with your very own quizzes

    Activity requirements: one phone per student (or group of students) and a pre-made quiz.

    is an app designed to help you create quizzes online, which can be a fun, engaging way to challenge your learners in the classroom.

    Quizzes are an ideal activity to use at the start of a class to recycle previous vocabulary or to pre-teach new language.

    For example, you could choose 10 British English phrases or idioms. Write a series of four possible definitions for each word, phrase, or idiom (with only one correct answer).

    In class, ask your students to take out their phones, find the app, and link to the quiz.

    Tell the students that they must vote for the definition they think is correct. They can keep track of their scores directly on the app while competing with their friends.

    Not only will it immediately engage your learners and help them interact with what’s being learned, but they’ll also have a lot of fun.

    2. Become expert translators using Google Translate

    Activity requirements: a phone with the Google Translate app installed and a pen and paper per pair.

    Did you know Google Translate has a feature where you can ? This is a perfect tool for text that you might find on a poster, in a book or uses an alphabet which you are unfamiliar with.

    Bring in samples of different languages to class – the more complex, the better. If you don’t have anything suitable at home, find something online and print it out.

    Get one student to take a photo of the text using the app and with their finger, select the section of the text they’d like to translate from the original language to English. (Note: if you download the Google Translate language file to your phone, it will translate everything automatically).

    Then, have them dictate what it says to their partner, who has to write it down.

    Finally, they work together to improve the English translation. After all, everyone knows Google’s English isn’t perfect (not yet, anyway).

    3. Take photos outside the classroom to use in class

    Activity requirements: a mobile phone with a camera per student.

    Do your students love taking photos? Tap into their love of photography and make activities more exciting by incorporating the photos on their phones into your classes.

    Adapt speaking activities from common exams such as the B2 Cambridge First. Instead of using the pictures in the book, put the students in pairs and get them each to choose a more relevant photo from their phones. You may find they have more to say and will communicate naturally while still practicing the necessary language and skills required in the exam.

    Another idea is to get students to take photos of things they encounter in English in the street. This could be on a sign outside a restaurant or in their favorite clothing shop. Use these photos to start the next class with a discussion about where they saw it, what it means and if the English is correct.

    You could also nominate one student each week to bring a photo they took that weekend. Share it with the class and get everyone to write Instagram captions. You wouldn’t believe how many already do this in English, so why not help them?

    Keeping your teens on task with mobile tech

    If you are worried about the misuse of mobile devices in your class – don’t be! Here are our top three strategies to help avoid students getting distracted:

    • Create a class contract. At the start of the course speak to them about when it is acceptable (e.g. during one of the activities above or to check the meaning of a new word on ) or not (e.g. during an exam or when you are explaining something) to use their phones. Then have someone write down everything you’ve talked about, get the whole class to sign it and stick it to the wall where everyone can see.
    • Give them a tech break. Halfway through the class, allow students 60 seconds to check their notifications to relieve their anxiety.
    • Reward good use. If you see they’ve been using their phones appropriately, reward them by playing their favorite game. The more you punish or tell them not to do something, the more they’ll want to do it.
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    Follow a recipe

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    Read a story

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    Older learners can read independently, but you can also suggest some books to read with parents/caregivers. Reading aloud together is a really positive way for adults and children to spend time together. It positively impacts children’s self-esteem and builds good associations with reading, hopefully encouraging them to become independent readers.

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    Learning how to read a map and follow directions is an excellent cognitive and physical exercise. It helps young learners to solve problems and builds their decision-making and observational skills.

    Do some experiments

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    • Count to ten.
    • Drop your right arm to your side and bend to your right while stretching your left arm over your head.
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    • Role-play - When students talk about food, you could role-play in a restaurant or talk about likes and dislikes.
    • Guessing games - Students must read the animals' descriptions and guess what they are. You can make up your own descriptions.

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    To optimize learning time, consider dividing your class into smaller groups and teaching each one individually for part of the timetabled class time. You may find that you get more done in 15 minutes with eight students than you would be able to get done in 60 minutes with 32 students.

    At the same time, you will be able to focus more easily on individual needs (you’ll be able to see all their video thumbnails on the same preview page). If it is not acceptable in your school to do this, divide the class so you’re not trying to teach everyone the same thing simultaneously.

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    When you’re all online, you can’t use visual clues to quickly judge whether anyone is having difficulties, like you can in the classroom. Ask direct questions to specific students rather than asking if everyone understands, or is OK. During and at the end of class, check and reinforce the achieved objectives.