Predictive Feedback Loops: Harnessing AI for Continuous Assessment and Personalized Growth in English Language Learners

English language learning involves acquiring the ability to understand, speak, read, and write in English. It focuses on developing skills in vocabulary, grammar, pronunciation, and comprehension, often through structured lessons and practice. English proficiency is essential for communication in global settings, education, and professional environments. This research explores the application of predictive feedback loops, leveraging Artificial Intelligence (AI) to revolutionize continuous assessment and personalized growth in English language learners. By employing a Long Short-Term Memory (LSTM) model, the study aims to provide a dynamic framework that monitors and evaluates student progress over time, enabling educators to deliver personalized feedback tailored to individual learning needs. LSTM, known for its ability to process and predict time series data, is uniquely suited for tracking linguistic progress, capturing both short-term achievements and long-term language acquisition patterns. The proposed model continuously analyzes learners' performance in various language tasks, identifying gaps in comprehension, grammar, vocabulary, and fluency. Through real-time assessments, the system offers immediate and actionable feedback, creating a loop of evaluation, adjustment, and improvement. This feedback loop fosters adaptive learning environments, where instruction is tailored to the learner's evolving needs, promoting more efficient language development. The research investigates the model's efficacy in enhancing language proficiency, focusing on its role in improving self-regulation, motivation, and learner autonomy. By providing timely, personalized interventions, the LSTM-based framework supports continuous learner growth, helping educators move beyond traditional, static assessment methods. Ultimately, this AI-driven approach aims to optimize the language learning experience, ensuring sustained progress and meaningful engagement in English language acquisition.

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Published
2024-12-06
DOI
https://doi.org/10.1109/ccis63231.2024.10931931
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
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article
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article

Predictive Feedback Loops: Harnessing AI for Continuous Assessment and Personalized Growth in English Language Learners

P. Nagaraj, Debashree Devi, Antima Chamoli, Padma Lochan Hazarika et al.
Intelligent Tutoring Systems and Adaptive Learning
article

Predictive Feedback Loops: Harnessing AI for Continuous Assessment and Personalized Growth in English Language Learners

P. Nagaraj, Debashree Devi, Antima Chamoli, Padma Lochan Hazarika, Vineeta Kaur Saluja, N. Devadhas Prabu
article en

Abstract

English language learning involves acquiring the ability to understand, speak, read, and write in English. It focuses on developing skills in vocabulary, grammar, pronunciation, and comprehension, often through structured lessons and practice. English proficiency is essential for communication in global settings, education, and professional environments. This research explores the application of predictive feedback loops, leveraging Artificial Intelligence (AI) to revolutionize continuous assessment and personalized growth in English language learners. By employing a Long Short-Term Memory (LSTM) model, the study aims to provide a dynamic framework that monitors and evaluates student progress over time, enabling educators to deliver personalized feedback tailored to individual learning needs. LSTM, known for its ability to process and predict time series data, is uniquely suited for tracking linguistic progress, capturing both short-term achievements and long-term language acquisition patterns. The proposed model continuously analyzes learners' performance in various language tasks, identifying gaps in comprehension, grammar, vocabulary, and fluency. Through real-time assessments, the system offers immediate and actionable feedback, creating a loop of evaluation, adjustment, and improvement. This feedback loop fosters adaptive learning environments, where instruction is tailored to the learner's evolving needs, promoting more efficient language development. The research investigates the model's efficacy in enhancing language proficiency, focusing on its role in improving self-regulation, motivation, and learner autonomy. By providing timely, personalized interventions, the LSTM-based framework supports continuous learner growth, helping educators move beyond traditional, static assessment methods. Ultimately, this AI-driven approach aims to optimize the language learning experience, ensuring sustained progress and meaningful engagement in English language acquisition.

Mangalayatan University (IN), Graphic Era University (IN), Kalasalingam Academy of Research and Education (IN)
Quality Education
Openalex Percentile: Top 74%
Intelligent Tutoring Systems and Adaptive Learning
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