A conceptual article frames AI-driven learning in second-language education through process integration, learner-centered adaptation, data-driven regulation and continuous feedback.
Key findings
- The framework comprises process integration, learner-centered adaptation, data-driven regulation and continuous feedback across pre-class, in-class and post-class activities.
Why this matters globally
The framework shifts discussion from tool selection to whole-cycle learning design and raises questions about data, decision authority and accountability.
Thai researcher contribution
A Dhurakij Pundit University-affiliated researcher contributed to the conceptual framework for AI-supported second-language education.
Limitations to consider
There was no systematic-review protocol, learner sample, comparator or measured outcome. Privacy, bias, hallucination, accessibility, teacher workload and cost remain untested.