Teaching employability skills: Q&A

Ken Beatty
Teacher sat in front of a classroom pointing at a student with their hand up

Preparing students for the modern world of work can be challenging; it's constantly adapting and changing which can be hard to keep track of. Today's post is a Q&A on the teaching of employability skills and the essential skills and qualities students need to thrive in the 21st century. offers insights to answer common questions and help you improve your language teaching skills.

1. How can we make students see the value of employability skills when they prioritize traditional language skills? Any tips to bridge this gap?

My advice is to push the issue back to the students by putting them in groups and asking each group to identify a different job/career that involves people working with others. Then ask "What would happen if this job was competitiveinstead of collaborative?" You may have to re-explain the concepts, but let students write a little story about a situation in which the workers suddenly all turn competitive.

For example, It was chaos in the women's soccer/football game. All the members of the blue team suddenly became competitive. Each one still wanted to win, but each decided that she would shoot a ball into the other team's net. This included the goalie, who ran up to the front of the field and purposely shoved and tripped members of her team ….

Or for more traditional jobs, In the middle of the operation the nurse pushed the doctor out of the way and picked up the instrument. The patient also wasn’t completely asleep, and he tried to do the operation himself, then …

It's all absurd, of course, but it can lead into other tasks asking students why collaboration is so important in each job. Then, turning it back to language, what kinds of language does each profession require to collaborate? For soccer/football players, this includes shouted requests and commands:Pass the ball to me! Shoot!as well as hand and body gestures. Similarly, doctors require professional jargon:Pass me the scalpel, please.Rather thanGive me the pointy knife thing!

2. Considering all the impact of tech, is there a clear future for employability for teachers?

One hundred years ago, in 1923, Thomas Edison predicted that motion pictures would replace teachers and books. Since then, similar predictions have been made for radio, TV and computers. It hasn't happened, and one of the reasons is that we crave the human touch in our teaching and learning. I recently read. "When it comes to getting knowledge to stick, there may be no substitute for human relationships. … I've been to former students' weddings and baby showers and funerals of their parents," says Millard, the high school English teacher in Michigan. "I've hugged my students. I've high-fived my students. I've cried with my students. A computer will never do that. Ever, ever.”(Waxman, 2023, para. 21-22)

But, that doesn’t mean teachers should stop learning about new technologies. We need to keep finding ways for them to help us and our language learners in the classroom. It can seem overwhelming, though, which is why I recommend shifting responsibility to students: “Do any of you know about ChatGPT? Yes? How do you think you could use it to help you learn?”

3. How can we deal with collaboration in a competitive world?

Although the world is in many ways competitive, there are countless examples of how students will do better by collaborating. Most of our students today won't be working in environments where they are competing against their co-workers. Instead, they'll be in teams and need critical thinking and negotiation skills to help them do so.

One way forward is to ensure that your classroom features more collaborative activities. Get students working in pairs and groups on all their assignments, but also create a buddy system so students always have someone else to ask for help. For example, if they're having to read a text and come across difficulties, it's often easier for them to text or call a friend than to wait until the next class. After a few collaborative activities, discuss collaboration versus competition with students and ask them which they prefer. Also, ask them for examples of what their friends and family members do regarding collaborating and competing.

As always, it's better to lead students to understand a new idea than to tell them.

If you want to learn more, make sure to check out Ken's webinar here. If you'd like to learn more about teaching future skills to students check out21st-century skills and the English language classroom.

References

Waxman, O.B. (2023, August 8).The creative ways teachers are using ChatGPT in the classroom.Time.time.com/6300950/ai-schools-chatgpt-teachers/

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    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.  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.