Language learning assessment has traveled a long path — from rote memorization and standardized testing to communicative tasks, mobile practice, and now AI-driven feedback. What used to be a static snapshot of ability is becoming a continuous, adaptive process woven into daily learning. The promise of AI lies in its ability to personalize practice, preserve languages, and scale access. Yet the risks — bias, inequity, surveillance, and corporate control — remind us that faster or more automated is not always better.
The future of assessment is not about replacing teachers or erasing human judgment. Instead, it is about reimagining the teacher’s role as a mentor, guide, and cultural anchor, while allowing technology to handle some of the repetitive tasks. The challenge is to design systems that keep learners’ voices, contexts, and identities at the center.
If you’d like to explore more, here are a few useful resources beyond what we’ve included elsewhere in the site:
UNESCO (2023). AI and Education: Guidance for Policy-Makers. Practical frameworks and cautions for global adoption. Link
Cambridge English (2024). How AI-powered marking is changing language assessment. Insights from practice. Blog