Keyword: «learning metrics»

In the context of the digital transformation of education and the mass spread of foreign language learning, it is important to use artificial intelligence technologies in such a way that they enhance rather than replace the activity of teachers and learners. Without a well-designed methodology and clear rules of use, there is a growing risk of reduced learner autonomy, loss of academic integrity, and a widening gap between the theory and practice of language teaching. The aim of the article is to substantiate and describe a model for the effective use of artificial intelligence technologies to enhance classroom-based acquisition of a second foreign language, implemented as an adaptive methodological ecosystem that strengthens learner activity and preserves the primacy of human contribution. The study is based on a theoretical and methodological analysis of works on the theory of second language acquisition, intelligent computer-assisted foreign language learning, formative assessment, and self-regulated learning. These strands are synthesized into practical tools that take into account Russian educational standards and multilevel descriptions of communicative competence. The article proposes an adaptive ecosystem of scaffolding support using artificial intelligence technologies, built around five interrelated components: sequencing of lesson stages, scaffolding support, target skills, safeguards, and self-regulation, as well as a synergy mechanism that structures iterative interaction cycles between the human user and the digital assistant. The model introduces an intervention threshold for artificial intelligence technologies that does not exceed one fifth of the volume of auxiliary materials and presents a ready-to-use toolkit: an alignment map of the digital assistant’s roles across lesson stages, a policy for the use of artificial intelligence with a prompt log, formative assessment rubrics, lesson scenarios, a risk and resilience matrix, an implementation checklist, and monitoring indicators covering vocabulary retention, fluency of oral speech, error frequency, text coherence, teacher checking time, and the footprint of artificial intelligence use. Theoretical significance lies in clarifying the role of artificial intelligence technologies as an instrument of metacognitive support that does not substitute for genuine language use. Practical significance consists in providing a methodological toolkit that helps save teacher time, improve the quality of formative feedback, protect academic integrity, and adapt the model to different disciplines, proficiency levels, and classroom formats.