The potential and limitations of large language models for automatic classification of teachers' motivational messages in educational research

Whereas recent studies have demonstrated the potential of LLMs to offer scalable and time-efficient alternatives for classifying motivational messages in the classroom, several challenges persist. These include concerns related to the quality and quantity of training data, model generalisability, the ability to capture the complexity of classroom interactions, and biases involved in integrating LLMs as a classification method. This comprehensive literature overview provides practical recommendations for the responsible use of LLMs in educational research and school practice.