How AI is Changing the Landscape of Language Learning and Assessment

Early mobile apps mostly checked right/wrong on short items and resurfaced mistakes later. Feedback was simple, timing was fixed, and speech checks were rough. AI promises to shift the model from “practice then test” to “practice as test.” It pinpoints the exact issue (a sound, an ending, a word choice), offers an immediate retry, and adapts the next prompt to your latest attempt. That makes each minute of practice do more work because the sheer amount of data AI can provide feedback on in any given scenario. AI can also summarize patterns so you know what to review, not just that you got things wrong. With clear rubrics and humans working to improve these AI systems in the background, users can expect faster course corrections, steadier habits, and better transfer to real tasks as AI is integrated into language learning.

In the past mobile apps have followed a “tap and drill” routine: you tap through multiple-choice, match-pairs, or short fill-ins, then the app drills similar items until you answer correctly. The feedback is binary, context is thin, and guidance beyond repetition is limited. By contrast, AI supplies a quick hint or example, highlights exactly what to change, and lets you try again immediately. It then sequences the next item based on that attempt and brings back only the weak spots later. Because fixes are specific and timed to the moment of need, a few minutes of practice go further than the old tap-and-drill cycle.

Below we will look into the promise of AI along with some of the good and bad of how this emergent technology is being deployed to support language learning, acquisition, and preservation.