English for employability: What will jobs be like in the future

Nicola Pope
People sat in chairs doing various things like working on a laptop; sat in one of those seats is a cartoon robot

What do driverless car engineers, telemedicine physicians and podcast producers have in common? About 10 years ago . They are representative of a new technology-driven marketplace, which is evolving faster than employers, governments and education institutions can keep up.

As new jobs appear, others fall by the wayside. Today, it’s estimated that up to with currently available technology. Routine jobs like data entry specialists, proofreaders, and even market research analysts are especially at risk of becoming redundant within the next 5 to 10 years. Globally, that means between 400 and 800 million workers could be displaced by automation technology by 2030, according to McKinsey.

Moreover, will need to work in areas that do not exist in the current market. The question is, what can we do to prepare learners for a future when we have no idea what jobs they’ll be doing? Mike Mayor and Tim Goodier discuss this uncertain future and explain why English for employability is such a hot topic right now.

A rising level of English and employer expectations

Mike Mayor, Director of the Global Scale of English at app, explains that while he believes employability has always been a factor in English language education, it has become more important and more of a focus for students looking to enter the workforce.

“Expectations of employers have risen as proficiency in English language, in general, has risen around the world,” he says. “They’re now looking for more precise skills.”

Tim Goodier, Head of Academic Development at Eurocentres, agrees. He explains that English language education is primarily about improving communication and soft skills – which is key for the jobs of 2030 and beyond.

“There’s a convergence of skills training for the workplace and language skills training,” Tim says. “The Common European Framework of Reference (CEFR) has recognized and, in many ways, given a roadmap for looking into how to develop soft skills and skills for employability by fleshing out its existing scheme – especially to look at things like mediation skills.”

How the Global Scale of English and CEFR have surfaced employability skills

TheGlobal Scale of English (GSE) is recognizing this increasing prominence of English for employability. Mike explains that it’s doing this “by taking the common European framework and extending it out into language descriptors which are specific for the workplace.”

In developing a set of learning objectives for professional learners, Mike and his team have given teachers more can-do statements. “They are able to create curricula and lessons around specific business skills,” he says.

Tim comments that one of the most interesting things about the GSE is that it links can-do statements to key professions, which he explains “is another extension of what these can-do statements can be used for – and viewing competencies as unlocking opportunity.”

Showing how these skills and competencies relate to the real world of work can be a strong motivating factor for learners.

He says that teachers need to visualize what success will look like in communication “and then from there develop activities in the classroom that are authentic.” At the same time, he says that activities should be personalized by “using the learners’ own interests and adapting the course as much as possible to their future goals.”

Preparing students for the future workplace

Speaking on the role of publishing in English for employability, Mike says:

“I would say as course book creators we actually incorporate a lot of these skills into our materials, but… I think we could do to push it a little further.”

In Mike’s view, educators need to do more than teach the skills, they need to raise awareness of their context. In other words why these skills are important and how they will help them in authentic situations both in and out of the work environment.

Beyond teaching the language itself, he says publishers should be helping teachers ask:

  • Are the students participating fairly in group discussions?
  • Are the students actively listening?
  • Are they interrupting politely?

These skills “don’t come naturally, and so just to begin raising awareness would be an added value,” he says.

Future skills: careers in 2030

In the same way we didn’t know that driverless cars would become a reality 10 years ago, we cannot say with absolute certainty which professions will arise and which will disappear. However, using tools like the GSE teacher toolkit, we can help our students develop the language and soft skills they need to navigate an ever-shifting job market. The future is an exciting place, let’s help our learners prepare themselves!

Watch the full interview with Mike and Tim below:

English language skills development for employability
Play
Privacy and cookies

By watching, you agree app can share your viewership data for marketing and analytics for one year, revocable by deleting your cookies.

Learn more about the impact of English and employability

More blogs from app

  • Hands typing at a laptop with symbols

    Can computers really mark exams? Benefits of ELT automated assessments

    By app Languages

    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.  and 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, including  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.