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.
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Are you studying abroad (or soon to be), learning a new language and need some inspiration on ways to practice your language skills in the real world? Don't worry. It can be daunting to know how to begin, especially in an unfamiliar place, However, with a little bit of effort and following these useful tips, you will be able to improve your language skills and make the most of your study abroad experience.
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As a language teacher, your goal is not just to impart knowledge but to guide your students on a transformative journey toward fluency. Assessing and tracking learning progress is a dynamic process that empowers both educators and learners, rather than being just a routine task.
In today's language learning blog post, we will explore the significance of assessment in language teaching and provide valuable insights on how to track and assess your students' linguistic development.
Regular assessments enable teachers to tailor instruction to meet individual student needs. Identifying strengths and weaknesses helps educators adapt teaching methods, promoting a more personalized and effective learning experience.
Assessment results can be very useful in motivating students. Even small progress should be acknowledged as it can boost their confidence and encourage a positive attitude towards learning. It is important to share success stories, celebrate achievements and foster a culture of continuous improvement within your language classroom.
Assessment feedback can help students improve their skills by giving them a clear idea of their strengths and weaknesses. Teachers can use this feedback to encourage students to take responsibility for their learning journey and foster a growth mindset that is resilient even in the face of linguistic difficulties.
Embrace a variety of assessment methods to capture the multifaceted nature of language learning. Beyond traditional exams, integrate speaking assessments, project-based evaluations and collaborative activities. This diversity ensures a comprehensive understanding of your students' language proficiency.
Example: Consider assigning projects that involve researching, creating presentations and demonstrating creative expression (like plays or videos) in the target language. Assessing various aspects such as language skills, creativity and critical thinking. Design projects around your class's interests and motivations.
Integrate formative assessments into your teaching strategy. These ongoing evaluations, such as quizzes, class discussions and short writing assignments, provide real-time feedback. For instance, if you notice that your students are struggling with a particular concept, you can use formative assessments to evaluate the effectiveness of your teaching approach and make necessary adjustments.
Example: Conduct regular quizzes, polls or short assessments during class to evaluate students' understanding. Use quick checks to gauge student understanding to adjust teaching methods accordingly. This will help you tailor your teaching methods in real time to ensure effective lesson delivery.
Encourage students to maintain language portfolios. These portfolios can include samples of their written work, recorded conversations and reflections on their language learning journey. Portfolio assessments offer a holistic view of progress and provide students with a tangible record of their achievements.
Example: Conduct periodic portfolio reviews to discuss progress and set goals. Encourage frequent reflection to show learners how far they've come.
Empower students to self-assess. Encourage reflection on their language skills, setting goals and evaluating their own progress. Self-assessment also fosters a sense of responsibility and independence in the learning process. When students take ownership of their progress, they become more invested in their education and are more likely to achieve their goals.
Example: Provide your language students self-assessment checklists or rubrics for them to evaluate their proficiency and set personal goals.
Use language learning platforms' analytics and progress reports for data-driven decision-making. It's great to help save time and provide reliable and up-to-date reports.
Example: Using online platforms for assignments, quizzes and collaborative projects with built-in tracking features. Our learning platforms, app English Connect (PEC)and MyEnglishLab (MEL), can help you keep track of your students' progress.
Cultural projects are a great way to engage students in the broader context of the language they are learning. These projects could involve researching cultural practices, traditions or historical events related to the language.
Students learn how to navigate cultural nuances, understand diverse perspectives and effectively communicate in different cultural contexts by participating in cultural projects. Such projects help students form a personal connection with the language and bridge the gap between theory and real-world application, making language learning more meaningful.
Example: Assign projects that explore certain cultural aspects of the target language, encouraging a deeper understanding of context. These can be evaluated on how well it's presented, its clarity, and how factually accurate it is.
Peer review is a valuable practice that promotes a sense of community within the language learning classroom. It involves students working together and offering constructive feedback to each other, which leads to the development of their language skills. It creates a collaborative learning environment where students actively participate in the improvement of their peers, learning from one another's strengths and weaknesses.
Students often put more effort into assignments when they know peers will review their work. This increased accountability can lead to higher-quality work and a greater commitment to language learning.
Example: Implement peer review sessions where students provide feedback on each other's written or spoken assignments. Encourage constructive criticism to enhance collaboration and learning. To accommodate shy students, this process can be anonymous.
Assessing and tracking language learning progress is integral to effective language teaching, requiring continuous interaction between educators and students.
By utilizing diverse assessment methods and fostering a culture of constant improvement, language teachers play a vital role in guiding their students toward linguistic fluency. Helping language students celebrate their successes and overcome challenges helps them to be not only proficient speakers but also lifelong language enthusiasts.
Are you an English teacher preparing for assessments? Check out our postMotivating your students through assessment.
As well as our learning platforms, PECand MEL, we offer various English assessments and courses to help track your learner's progress and to certify their English level, so make sure to explore our range to find the best solution for your students.
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Welcome to the beginning of a new year, filled with countless possibilities and exciting opportunities for learning a new language. If you're like most people, you probably have some language learning resolutions and goals you'd like to achieve this year.
While setting these goals is easy, sticking to them can appear daunting. But don't worry, we’re here to help you not only set those goals but also achieve them like a pro. Below are some useful tips to help turn your language aspirations into reality this year and to keep you motivated towards your goals.
