How to bring soft skills into the business English classroom

Richard Cleeve
A woman standing at a whiteboard in a office with two others sat down.

Anyone who’s ever taught a business English class knows that their students are busy people. Sometimes they get sidetracked by their other tasks - even during class. This means we have to make the most of the time we have with our learners and focus on what they really need.

How you do this depends on the sector your students work in (or plan to work in), their previous experiences studying English and their own strengths and weaknesses.

Teachers often focus on teaching hard skills, such as writing reports or running meetings. We do this because it can be challenging for many business students to do these things in English and also because hard skills have an immediate and positive impact on their workdays.

But, if there’s one thing that all business people can benefit from, it’s soft skills.

Soft skills are interpersonal or people skills. They include things like active listening, teamwork, decision-making and influencing skills. Mastering these skills will help students progress more rapidly and become more independent learners. However, isolating the specific vocabulary or grammar structures that these skills use is complex and they often get overlooked in language learning classes as a result.ÌýÌý

Bringing soft skills into the Business English Classroom
Play
Privacy and cookies

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

Five essential communication skills for business students

1. Listening actively

People mistakenly think that communication is just about speaking. However, one of the best ways to be a good communicator is to listen to the person you are talking to.ÌýLearners can show interest in what someone is saying by asking clarifying questions and rephrasing what they've said to ensure they've fully understood.

2. Influencing others

In order to manage employees and clients effectively your students will need to be able to influence others. This can be done by building rapport, explaining why they are doing something, asking the right questions and selling themselves in a certain way.

3. Negotiating successfully

The key to closing sales, obtaining a favorable price for a product or service, or maintaining a cohesive team lies in the art of negotiation.ÌýSuccessful negotiators determine their objectives before starting, prepare fully to support their positions and always leave their emotions at home (or in the office).

4. Dealing with different communication styles

In the world of business, students will come across people who communicate differently. The three basic communication styles are aggressive, passive and assertive. They’ll need to know how to deal with these different styles if they want to succeed.

5. Speaking clearly and concisely

Students need to learn to express themselves clearly and convey their message in as few words as possible. It’s easy to ramble when you are nervous, soÌýencourage them to think about what they want to say and, if necessary, make some brief notes beforehand.

How they apply these skills will depend on the issue at stake, the situation and who they're talking to, so they will need to adjust their behaviour accordingly. The more time you spend on this in class, the easier it will become.

If you need any guidance there are courses likeÌýBusiness Partner, which is aÌýbusiness English course that teaches real-world language and business skills which helps to teach the above skills mentioned. The course raises students’ awareness of different communication styles to help avoid misunderstandings and so they can be more perceptive and adapt their own styles according to the audience.

Read our post 'Tips for teaching business English to low-level learners'

More blogs from ÃÛÌÒapp

  • Hands typing at a laptop with symbols

    Can computers really mark exams? Benefits of ELT automated assessments

    By ÃÛÌÒapp 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 VersantÌýtests – 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 assessmentÌýto 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.â€

    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 ÃÛÌÒapp, 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.