The Use of Mobile Devices for English Language Assessment in Low-Resource Environments: Opportunities and Challenges

Language tests are required for assessing linguistic abilities in different scenarios, but low-resource environments suffer from reduced computational resources and labeled data. This research proposes an efficient language assessment framework based on GPT-5 and FLAVA models, which can help improve such assessments in those environments. GPT-5 provides sophisticated textual analysis, whereas FLAVA contributes multimodal features, making overall assessments possible with different types of inputs. The architecture overcomes computational limitations by employing model distillation and precision reduction methods, promoting scalability and efficiency. Experimental results on the COCA dataset showed excellent performance with 90.6%. The system also improved drastically in terms of latency as well as memory efficiency over baseline models. This method is also noteworthy for the possibilities of integrating state-of-the-art AI models to provide accurate, scalable, and resource-efficient language tests, such that it becomes appropriate for use in schooling and professional applications in low-resource environments. The research concludes by confirming the success of the model in providing efficient and accurate results.