What first drew me to linguistics was realising that language is far more than the words we use to communicate. Something as ordinary as having a conversation can be looked at through so many different lenses. How we produce and understand sounds, how language reflects the people and communities who use it and how our brains process it. Studying linguistics at undergraduate level opened up all of these different ways of thinking about language. My MSc gave me the chance to take one of these questions much further – how technology is drastically changing the way we learn languages.
My MSc in Applied Linguistics and Second Language Acquisition at the University of Oxford allowed me to explore this question at a time when ChatGPT was quickly becoming part of conversations about education and language learning. There was a huge amount of discussion about what AI might do for language learning, but much less evidence about what it was actually doing. That gap became the starting point for my dissertation.
My MSc project was a systematic review investigating the experimental evidence on the effects of ChatGPT on foreign language learning outcomes in instructed contexts. I searched thousands of research records and eventually brought together 73 experimental and quasi-experimental studies. The studies explored a range of ways learners were using ChatGPT, from getting feedback on their writing and revising their work to practising language and working through course content.
One of the things that surprised me was just how heavily the research focused on writing. This made me think more carefully about what it actually means to ‘learn with AI’. Is ChatGPT itself creating language learning, or is it changing the opportunities learners have for feedback, interaction and revision? The answer depends heavily on how the technology is being used and what kind of learning is being measured. That became one of the most interesting outcomes from my project. Rather than just asking whether a technology ‘works’, further research needs to ask how, for whom and under what conditions it works. These questions are relevant even beyond AI and are part of what makes the empirical study of language so interesting.
Receiving the Philological Society bursary greatly supported me throughout this process. It helped make it possible to dedicate time to my independent research while developing my skills in research and statistics. It was also particularly meaningful to receive support from a society with such a long history of promoting the scientific study of language. Although my project focused on a very contemporary technology, the underlying aim felt much more familiar – using careful research to understand language and how it is learned and used.
As I finish my MSc, the next step is taking these interests into a new area of linguistic research. I am about to start a role at Pearson as a specialist researcher in language assessment, working within the assessment research and validity team. The role will involve looking at questions around language proficiency, measurement and the evidence needed to support claims about what a language test measures.
My MSc has also reinforced an interest in how linguistics can respond to changing technologies and educational practices without losing sight of the importance of good empirical evidence. That is something I hope to take with me into my new role, while continuing to develop as a researcher. The Philological Society bursary has been a valuable part of that journey. It has supported a year of exploring important questions, and it feels fitting that the next stage will continue that journey into the research and measurement of language.
