In the world of global talent, a single test score can be the key that unlocks a new career, an educational opportunity, or a life-changing move. Yet, the path to success is often fraught with stress and uncertainty. We’re joined today by Sofia Khaira, a leading expert in diversity, equity, and inclusion, who specializes in how technology can create more equitable pathways in talent management. She offers a unique perspective on how AI-driven tools are not just changing how we prepare for exams, but also democratizing access for professionals worldwide. Our conversation explores the immense pressure candidates face, how AI provides a crucial support system for subjective skills, and the future of accessible learning in a globally connected workforce.
Your IELTS Pre-Test targets candidates facing strict deadlines. What specific pain points did you observe in their traditional preparation, and how does the AI’s instant feedback feature directly address the stress and uncertainty these learners experience? Please share a typical user scenario.
Absolutely. The pressure on these candidates is immense, and it’s something we see constantly in talent mobility. It’s not just about passing a test; it’s about securing a job offer, meeting a university deadline, or keeping an immigration application alive. The pain point wasn’t just a lack of practice materials, but a crushing lack of timely, reliable feedback. Imagine studying late at night after a long shift or while your children are asleep, pouring your energy into a practice essay, and then having to wait days for a tutor’s notes, if you can even afford one. That waiting period is filled with anxiety. The AI’s instant feedback changes this dynamic entirely. A user can now complete a full speaking test, and within seconds, see an estimated score and identify that they’re consistently drifting off-topic. This immediacy transforms passive waiting into active, targeted learning, which is a game-changer for someone whose future is on the line.
The platform, IELTS Express, uses AI to score all four test components. Could you walk us through how the AI was trained to assess subjective skills like Speaking and Writing in a way that reflects real exam conditions? What were the biggest technical hurdles in achieving this?
Training an AI for subjective skills like speaking and writing is one of the most significant challenges in EdTech today. It’s far more complex than just marking multiple-choice reading or listening questions. The system was meticulously trained on vast datasets to recognize the key markers of proficiency that human examiners look for—things like lexical resource, grammatical accuracy, coherence, and fluency. The goal was to create an assessment that mirrors the structure and timing of the actual exam, conditioning the user to perform under that specific pressure. The biggest hurdle was nuance. Capturing the subtle differences between a coherent argument and one that’s slightly off-topic, or distinguishing between a wide vocabulary and one that’s used imprecisely, required incredibly sophisticated models. The aim isn’t to replace human examiners but to provide a consistent, scalable guide that helps learners see their patterns almost immediately.
Ben Pearce mentioned that learners often feel stuck and need a private space to practice. Beyond being an online tool, what specific design choices were made to create this supportive environment, and what user patterns—like repeating speaking tests multiple times—have you observed since the launch?
Creating a psychologically safe learning environment was a cornerstone of the design. From a diversity and inclusion standpoint, this is critical. Many talented individuals are held back by a fear of making mistakes in front of others. The platform was intentionally built as a private, non-judgmental space. This means no live audience, just the learner and the AI. This empowers them to be vulnerable in their practice. We’ve seen fascinating user patterns emerge from this. For instance, it’s very common for a user to attempt the speaking test three or four times in a row. The first attempt might be hesitant, but with each repetition and a quick review of their feedback, you see their confidence and fluency build. It’s a space where they can fail, learn, and try again without the social pressure, which is something traditional classroom settings or tutoring sessions can’t always provide, especially for introverted or anxious learners.
Given that over 40% of candidates miss their target score by just 0.5, could you share an example of how your tool helps a user identify a consistent weakness, like drifting off-topic in Speaking, and then work to close that critical half-band gap before their official exam?
That 0.5-band gap is heartbreaking because it’s often the result of small, fossilized errors that the candidate themselves can’t see. Let’s take a common scenario: a professional who is perfectly fluent in conversational English but struggles with the formal structure of the IELTS Speaking test. They might consistently fail to fully address the prompt in Part 2 because they get caught up in a personal anecdote. Without targeted feedback, they would never know this is a scoring issue. The AI tool, however, can flag this pattern. After a few practice tests, the user might see feedback indicating their responses lack topical coherence. Suddenly, they have a concrete problem to solve. They can then focus their practice specifically on structuring their answers, ensuring every sentence directly supports the prompt. This focused intervention is exactly what’s needed to bridge that critical half-band gap and turn a near-miss into a success.
What is your forecast for the role of AI in high-stakes exam preparation? How do you see tools like yours evolving beyond practice and feedback, and what impact might this have on accessibility for learners in remote areas or with limited resources in the coming years?
My forecast is that AI will become the great equalizer in global education and talent assessment. Right now, we’re focused on providing accurate practice and instant feedback, but the next evolution is true personalization. I envision AI tutors that don’t just score an essay but generate a customized lesson plan based on it, suggesting specific grammar exercises or vocabulary drills to address the identified weaknesses. This goes beyond simple feedback and becomes adaptive coaching. For learners in smaller towns or developing countries who lack access to elite tutors or expensive prep centers, this will be revolutionary. It democratizes high-quality preparation, ensuring that a candidate’s success is determined by their ability and dedication, not their geographic location or financial resources. This will create a more equitable and diverse pool of global talent for everyone’s benefit.
