The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented
impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as
to their capabilities and potential uses, and shining a light on natural language processing research which
had not previously received so much attention. The developments offer great promise for education
technology, and in this paper we look specifically at the potential for incorporating large language
models in AI-driven language teaching and assessment systems. We consider several research areas
– content creation and calibration, assessment and feedback – and also discuss the risks and ethical
considerations surrounding generative AI in education technology for language learners. Overall we
find that larger language models offer improvements over previous models in text generation, opening
up routes toward content generation which had not previously been plausible. For text generation they
must be prompted carefully and their outputs may need to be reshaped before they are ready for use. For
automated grading and grammatical error correction, tasks whose progress is checked on well-known
benchmarks, early investigations indicate that large language models on their own do not improve on
state-of-the-art results according to standard evaluation metrics. For grading it appears that linguistic
features established in the literature should still be used for best performance, and for error correction it
may be that the models can offer alternative feedback styles which are not measured sensitively with
existing methods. In all cases, there is work to be done to experiment with the inclusion of large language
models in education technology for language learners, in order to properly understand and report on
their capacities and limitations, and to ensure that foreseeable risks such as misinformation and harmful
bias are mitigated.