Advancing Language Assessment with AI and ML- Leaning into AI is Inevitable, but Can Theory Keep Up?

Following the burgeoning growth of artificial intelligence (AI) and machine learning (ML) applications in language assessment in recent years, the meteoric rise of ChatGPT and its sweeping applications in almost every sector have left us in awe, scrambling to catch up by developing theories and best practices. This special issue features studies of recent AI and ML advances and thought pieces and attempts to unify our field with a collection of work towards a common set of tools, frameworks, and practices. In this editorial, I briefly review the five studies and four commentaries and discuss the key validity issues around the AI applications covered. To unpack complex validity issues for lay users, I propose accessible questions to ask when evaluating these applications. I stress the importance of developing best practices guiding ethical and responsible use of AI and improving users' AI literacy skills. In light of users' increasing access to AI tools in real-world communication, I raise the need for redefining the constructs of language tests to be in sync with what is happening in the real world. These new conceptions of language ability are expected to result in significant changes in task design, scoring, and test interpretation and use.

Automated summary

This editorial reviews the recent advances in artificial intelligence and machine learning in language assessment and discusses the validity issues and the importance of ethical use. Accessible questions for evaluating these applications are proposed for lay users. Redefining the concept of language tests is necessary to keep up with the trends in real-world communication.