ARTIFICIAL INTELLIGENCE-DRIVEN FRAMEWORKS FOR FOSTERING ACTIVE PARTICIPATION AND LEARNING IN LANGUAGE CLASSROOMS

  • Polamuri S
  • Manikyamba I
  • Viswanath D
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Abstract

As we near the turn of the millennium and technology permeates every facet of human existence, we take a look at how AI, a recently developed technical marvel, has the ability to transform language schools in ways that have not yet been thoroughly studied.Specifically, the study aims to determine if this method encourages increased student engagement, which in turn improves their learning results. Artificial intelligence (AI), especially in the field of educational technology, is about to use previously unseen mechanisms and ways to overcome longstanding problems in language instruction. In this paper, I explore how AI (artificial intelligence) technologies like adaptive learning platforms, natural language processing tools, and intelligent tutoring systems can bring about large-scale change by developing user-centric, interactive features that are more responsive to individual needs in a personalised context, leading to improved interaction efficiency across a range of language-learning domains. First, the here and now (the most current developments in artificial intelligence and their educational applications, as well as any potential connections to language acquisition) Students spend significantly more time actively engaging with content when intelligent tutoring systems that adapt their response to a student's learning rate provide personalised feedback and advice. More interactive instruction within the learning experience that immediately acts towards educator time constraints is made possible by these conversational tools, which may include chatbots, Automated Essay Scoring systems, and natural language processing applications.

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APA

Polamuri, S. R., Manikyamba, I. L., & Viswanath, D. G. (2024). ARTIFICIAL INTELLIGENCE-DRIVEN FRAMEWORKS FOR FOSTERING ACTIVE PARTICIPATION AND LEARNING IN LANGUAGE CLASSROOMS. International Journal of Interpreting Enigma Engineers, 01(03), 23–32. https://doi.org/10.62674/ijiee.2024.v1i03.004

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