When did you become involved in Linguaskill?
I was involved at the initial conceptual design stage, dating back to 2002. More recently Linguaskill was re-invented, putting automatic assessment at the heart of the product.
Describe your role and involvement in Linguaskill.
I was involved at the design stage for the technical model, creating the Computer Adaptive Test (CAT) for Reading and Listening. I was also responsible for preparing the automated assessment engine for marking Linguaskill’s Writing and Speaking components.
What were the needs you were looking to solve with Linguaskill?
A valid, reliable, quick and cost-effective test of proficiency that could be delivered online to candidates with immediate reporting of results, all at an affordable price.
In your opinion, what makes Linguaskill different from other tests?
There are two main features. Firstly, its adaptiveness, which means that each candidate receives a different set of test items, personalised to their true ability level. Secondly, using AI technology to deliver immediate marking of the Writing and Speaking tests.
What have you learned while developing Linguaskill?
It is important to keep the customer at the heart of planning any new test. We learned how to use sophisticated technology to improve candidates’ experience of taking the test, while ensuring that results are reported to stakeholders in a fast and efficient way. Everyone in the team has been working towards that goal of meeting customer needs and making the product simple to use.
Now that Linguaskill is in the market, what are you most satisfied with in terms of the product and market adoption?
The ease with which candidates can sit the test, and the simplicity to run the test locally are the most satisfying features we managed to achieve in Linguaskill. We’ve had lots of positive feedback around these areas, which is extremely rewarding for us.
How do you see Linguaskill developing over the next 2–3 years?
The Linguaskill family tests will grow rapidly in terms of its use by multiple stakeholders and will become, I hope, the default benchmarking proficiency test of choice for various test purposes.
What are you working on now?
I’m continuing to work on the improvement of the automarker for writing and speaking, introducing new diagnostic features to provide efficient feedback to candidates to improve their English.
How do you see computer-based testing changing in the future, with the increasing use of AI?
I believe AI will become part and parcel of any computer-based test in the future. We are happy that we are leading in this area.
Are there other key trends that you see impacting language learning and testing over the next five years?
It is very clear that AI will have a big impact on language learning and testing moving forward. The Cambridge Learning Oriented Assessment (LOA) approach, which has AI at its heart, will significantly change language learning and testing by bringing these two areas closer, helping drive more effective, individual learning.