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.
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.
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.
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.
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.
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.
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.
At app, we have developed a range of assessments using AI technology.
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.
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.
Read more about the use of AI in our learning and testing here.
Around 20%of the , making it an integral component in global business operations. But the question remains for business leaders and HR professionals: how can language learning, specifically, business English courses, drive your organization forward?Here are 8 ways language training can impact your business.
Stories can make us laugh, cry or tremble with fear. They can teach us valuable life lessons and transport us to other worlds. They've been around since the beginning of language itself, but can they actually help us learn a language?
Stories are one of the most useful toolswhen teaching childrenEnglish. Not only do they help with listening and reading skills, but they can also support speaking and writing skills by providing context, language and structure.
Very young learners may already be familiar with stories – they may hear them in daycare, school or at home with their parents. Therefore, incorporating these into their language classes may help them to feel more comfortable in their surroundings. And if children feel comfortable, they are more likely to be receptive to learning.
Storytelling usually happens as part of a group in the classroom. This means that it becomes a bonding activity for children where they can communicate and subconsciously pick up the key language. While having fun listening and interacting with the story, theysoak up information without even realizing they’re learning.
So, what storytelling activities can we use with younglearners? Let’s find out.
Often, we think of storytelling simply as reading a book aloud to children. Yet, there are other activities you can do. These include:
To get young children interacting with the story, first read out a sentence alone. Then, have the children repeat the line with you as a group. Repeat as many times as necessary, until the children feel confident with the language.
If your learners are happy to, ask them individually to repeat the sentence after you. Make sure each one has a turn and praise them for being brave and trying to use the language.
An activity that works well with children is to act out the story’s characters. For example, there may be animals, fairies, monsters or other exciting characters that they can each act.
Ask them to make the noises of the animals, the wind, or the scenery to create an atmosphere while you read. This gets them interacting with the story and the rest of the group, which will help their communication and listening comprehension skills.
Young learners react particularly well to visual aids and realia. Why not use puppets or dolls to act out the characters, or even ask students to have a go with them? They will engage more with the story and the language.
Children’s story books are usually quite visual with illustrations and pictures. Make the most of these while telling the story. Try asking students questions about the images to get them using the vocabulary.
You could ask them, “what can you see?”, “what’s he wearing?” or “can you find an apple?”. This is another great way to reinforce the vocabulary they’re learning in class.
Use these activities individually orincorporate a mix into your lessons. Either way, storytelling will help your learners with more than just developing their English language skills.
While we often think of storytelling as a pastime for children, it can also be a useful language learning activity for adults.
Stories are part of our daily lives, from news to social media to books and movies. Therefore, they can be extremely beneficial tools for English language learning.
Yet, the way we approach storytelling as a class activity for adults differs to that of young learners. While we typically read fairy tales to young children, we can bring in a much wider range of content for adults, such as:
Storytelling can be a wonderful language learning tool for both children and adults. If you’re looking for a new way to engage, inspire and motivate your learners, why not try it in your next class?
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In this post, we uncover the benefits of language proficiency, particularly in English, backed by relevant statistics and insights from app's recentground-breaking new research.
