Can computers really mark exams? Benefits of ELT automated assessments

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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.  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. 

app English International Certificate (PEIC)

app English International Certificate (PEIC) also uses automated assessment technology. With a two-hour test available on-demand to take at home or at school (or at a secure test center). Using a combination of advanced speech recognition and exam grading technology and the expertise of professional ELT exam markers worldwide, our patented software can measure English language ability.

Read more about the use of AI in our learning and testing here, or if you're wondering which English test is right for your students make sure to check out our post 'Which exam is right for my students?'.

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    The ethical challenges of AI in education

    By Billie Jago
    Reading time: 5 minutes

    AI is revolutionising every industry, and language learning is no exception. AI tools can provide students with unprecedented access to things like real-time feedback, instant translation and AI-generated texts, to name but a few.

    AI can be highly beneficial to language education by enhancing our students’ process of learning, rather than simply being used by students to ‘demonstrate’ a product of learning. However, this is easier said than done, and given that AI is an innovative tool in the classroom, it is crucial that educators help students to maintain authenticity in their work and prevent AI-assisted ‘cheating’. With this in mind, striking a balance between AI integration and academic integrity is critical.

    How AI impacts language learning

    Generative AI tools such as ChatGPT and Gemini have made it easier than ever for students to refine and develop their writing. However, these tools also raise concerns about whether submitted texts are student-produced, and if so, to what extent. If students rely on text generation tools instead of their own skills, our understanding of our students’ abilities may not reflect their true proficiency.

    Another issue is that if students continue to use AI for a skill they are capable of doing on their own, they’re likely to eventually lose that skill or become significantly worse at it.

    These points create a significant ethical dilemma:

    • How does AI support learning, or does it (have the potential to) replace the learning process?
    • How can educators differentiate between genuine student ability and AI-assisted responses?

    AI-integration strategies

    There are many ways in which educators can integrate AI responsibly, while encouraging our learners to do so too.

    1.Redesign tasks to make them more ‘AI-resistant’

    No task can be completely ‘AI-resistant’, but there are ways in which teachers can adapt coursebook tasks or take inspiration from activities in order to make them less susceptible to being completed using AI.

    For example:

    • Adapt writing tasks to be hyperlocal or context-specific. Generative AI is less likely to be able to generate texts that are context-bound. Focus on local issues and developments, as well as school or classroom-related topics. A great example is having students write a report on current facilities in their classroom and suggestions for improving the learning environment.
    • Focus on the process of writing rather than the final product. Have students use mind maps to make plans for their writing, have them highlight notes from this that they use in their text and then reflect on the steps they took once they’ve written their piece.
    • Use multimodal learning. Begin a writing task with a class survey, debate or discussion, then have students write up their findings into a report, essay, article or other task type.
    • Design tasks with skill-building at the core. Have students use their critical thinking skills to analyse what AI produces, creatively adapt its output and problem solve by fact-checking AI-generated text.

    2.Use AI so that students understand you know how to use it

    Depending on the policies in your institution, if you can use AI in the classroom with your students, they will see that you know about different AI tools and their output. A useful idea is to generate a text as a class, and have students critically analyse the AI-generated text. What do they think was done well? What could be improved? What would they have done differently?

    You can also discuss the ethical implications of AI in education (and other industries) with your students, to understand their view on it and better see in what situations they might see AI as a help or a hindrance.

    3.Use the GSE Learning Objectives to build confidence in language abilities

    Sometimes, students might turn to AI if they don’t know where to start with a task or lack confidence in their language abilities. With this in mind, it’s important to help your students understand where their language abilities are and what they’re working towards, with tangible evidence of learning. This is where the GSE Learning Objectives can help.

    The Global Scale of English (GSE) provides detailed, skill-specific objectives at every proficiency level, from 10 to 90. These can be used to break down complex skills into achievable steps, allowing students to see exactly what they need to do to improve their language abilities at a granular level.

    • Start by sharing the GSE Learning Objectives with students at the start of class to ensure they know what the expectations and language goals are for the lesson. At the end of the lesson, you can then have students reflect on their learning and find evidence of their achievement through their in-class work and what they’ve produced or demonstrated.
    • Set short-term GSE Learning Objectives for the four key skills – speaking, listening, reading and writing. That way, students will know what they’re working towards and have a clear idea of their language progression.
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    Teaching engaging exam classes for teenagers

    By Billie Jago
    Reading time: 4 minutes

    Teachers all over the world know just how challenging it can be to catch their students’ interest and keep them engaged - and it’s true whether you’re teaching online or in a real-world classroom.

    Students have different learning motivations; some may be working towards their exam because they want to, and some because they have to, and the repetitiveness of going over exam tasks can often lead to boredom and a lack of interest in the lesson.

    So, what can we do to increase students’ motivation and add variation to our classes to maintain interest?

    Engage students by adding differentiation to task types

    We first need to consider the four main skills and consider how to differentiate how we deliver exam tasks and how we have students complete them.

    Speaking - A communicative, freer practice activity to encourage peer feedback.

    Put students into pairs and assign them as A and B. Set up the classroom so pairs of chairs are facing each other - if you’re teaching online, put students in individual breakaway rooms.

    Hand out (or digitally distribute) the first part of a speaking exam, which is often about ‘getting to know you’. Have student A’s act as the examiner and B’s as the candidate.

    Set a visible timer according to the exam timings and have students work their way through the questions, simulating a real-life exam. Have ‘the examiners’ think of something their partner does well and something they think they could improve. You can even distribute the marking scheme and allow them to use this as a basis for their peer feedback. Once time is up, ask student B’s to move to the next ‘examiner’ for the next part of the speaking test. Continue this way, then ask students to switch roles.

    Note: If you teach online and your teaching platforms allow it, you can record the conversations and have students review their own performances. However, for privacy reasons, do not save these videos.

    Listening – A student-centered, online activity to practice listening for detail or summarising.

    Ask pairs of students to set up individual online conference call accounts on a platform like Teams or Zoom.

    Have pairs call each other without the video on and tell each other a story or a description of something that has happened for their partner to listen to. This could be a show they’ve watched, an album they’ve listened to, or a holiday they’ve been on, for example. Ask students to write a summary of what their partner has said, or get them to write specific information (numbers, or correctly spelt words) such as character or song names or stats, for example. Begin the next class by sharing what students heard. Students can also record the conversations without video for further review and reflection afterwards.

    Writing –A story-writing group activity to encourage peer learning.

    Give each student a piece of paper and have them draw a face at the top of the page. Ask them to give a name to the face, then write five adjectives about their appearance and five about their personality. You could also have them write five adjectives to describe where the story is set (place).

    Give the story’s opening sentence to the class, e.g. It was a cold, dark night and… then ask students to write their character’s name + was, and then have them finish the sentence. Pass the stories around the class so that each student can add a sentence each time, using the vocabulary at the top of the page to help them.

    Reading –A timed, keyword-based activity to help students with gist.

    Distribute a copy of a text to students. Ask them to scan the text to find specific words that you give them, related to the topic. For example, if the text is about the world of work, ask students to find as many jobs or workplace words as they can in the set amount of time. Have students raise their hands or stand up when they have their answers, award points, and have a whole class discussion on where the words are and how they relate to the comprehension questions or the understanding of the text as a whole.

    All 4 skills –A dynamic activity to get students moving.

    Set up a circuit-style activity with different ‘stations’ around the classroom, for example:

    • Listening
    • Reading
    • Writing (1 paragraph)
    • Use of English (or grammar/vocabulary).

    Set a timer for students to attempt one part from this exam paper, then have them move round to the next station. This activity can be used to introduce students to certain exam tasks, or a way to challenge students once they’ve built their confidence in certain areas.

  • A teachet stood in front of a class in front of a board, smiling at his students.

    How to assess your learners using the GSE Assessment Frameworks

    By Billie Jago
    Reading time: 4 minutes

    With language learning, assessing both the quality and the quantity of language use is crucial for accurate proficiency evaluation. While evaluating quantity (for example the number of words written or the duration of spoken production) can provide insights into a learner's fluency and engagement in a task, it doesn’t show a full picture of a learner’s language competence. For this, they would also need to be evaluated on the quality of what they produce (such as the appropriateness, accuracy and complexity of language use). The quality also considers factors such as grammatical accuracy, lexical choice, coherence and the ability to convey meaning effectively.

    In order to measure the quality of different language skills, you can use the Global Scale of English (GSE) assessment frameworks.

    Developed in collaboration with assessment experts, the GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help you assess the proficiency of your learners.

    There are two GSE Assessment Frameworks: one for adults and one for young learners.

    What are the GSE Assessment Frameworks?

    • The GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help teachers assess their learners’ proficiency of all four skills (speaking, listening, reading and writing).
    • The GSE Learning Objectives focus on the things a learner can do, while the GSE Assessment Frameworks focus on how well a learner can do these things.
    • It can help provide you with examples of what proficiencies your learners should be demonstrating.
    • It can help teachers pinpoint students' specific areas of strength and weakness more accurately, facilitating targeted instruction and personalized learning plans.
    • It can also help to motivate your learners, as their progress is evidenced and they can see a clear path for improvement.

    An example of the GSE Assessment Frameworks

    This example is from the Adult Assessment Framework for speaking.

    As you can see, there are sub-skills within speaking (andfor the other three main overarching skills – writing, listening and reading). Within speaking, these areproductionandfluency, spoken interaction, language range andaccuracy.

    The GSE range (and corresponding CEFR level) is shown at the top of each column, and there are descriptors that students should ideally demonstrate at that level.

    However, it is important to note that students may sit across different ranges, depending on the sub-skill. For example, your student may show evidence of GSE 43-50 production and fluency and spoken interaction, but they may need to improve their language range and accuracy, and therefore sit in a range of GSE 36-42 for these sub-skills.