Artificial Intelligence and Smart Pedagogy: Machine Learning in Digital Media for Language Education

This qualitative study explores the experiences, perceptions, and pedagogical practices of language teachers integrating AI-powered language learning tools into their classrooms, aiming to examine pedagogical enhancements and challenges, and their impact on teacher-student interactions and student learning outcomes. The study addresses a significant gap in the literature by investigating the complexities of AI-powered language learning tool integration in real-world classrooms. Utilizing thematic analysis and the Cognitive-Media-Machine Framework as its theoretical foundation, which provides a holistic understanding of the interplay between cognitive processes, media, and machine learning algorithms, the study collects data from 100 language teachers using AI-powered tools in different universities through interview and observation protocols. The findings reveal three nested themes: Pedagogical Enhancements, Technological Challenges, and Student-Centered Learning, highlighting the potential of AI-powered language learning tools to provide personalized feedback and assessment, while underscoring the need for addressing technological challenges and promoting student-centered learning experiences. The study contributes to the existing literature by providing insights into the practical applications and limitations of AI-powered language learning tools, informing language education policy, practice, and research, and highlighting the need for ongoing professional development, technical support, and pedagogical innovation. The research employs a qualitative approach, utilizing thematic analysis to identify emerging themes, and the Cognitive-Media-Machine Framework, which encompasses three key aspects: cognitive processes, media, and machine learning algorithms. The study's key findings emphasize the potential of AI-powered language learning tools to enhance language learning outcomes, while also posing significant technological challenges, and highlighting the importance of promoting student-centered learning experiences. Ultimately, the study addresses a significant gap in the literature, providing valuable insights for language education stakeholders.

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Publication Details

Journal
Metallurgical and Materials Engineering
Published
2025-02-13
DOI
https://doi.org/10.63278/1324
Citations
1
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
1.69
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article

Artificial Intelligence and Smart Pedagogy: Machine Learning in Digital Media for Language Education

Basim Mir, Mahnoor Ghani Sheikh, Ayesha Rashid, Mudasar Jahan et al.
1 citations
Metallurgical and Materials Engineering
Online Learning and Analytics
1.69
article

Artificial Intelligence and Smart Pedagogy: Machine Learning in Digital Media for Language Education

Basim Mir, Mahnoor Ghani Sheikh, Ayesha Rashid, Mudasar Jahan, Nishat Zafar, Tabassum lqbal, Saima Asim
article en
1 citations

Abstract

This qualitative study explores the experiences, perceptions, and pedagogical practices of language teachers integrating AI-powered language learning tools into their classrooms, aiming to examine pedagogical enhancements and challenges, and their impact on teacher-student interactions and student learning outcomes. The study addresses a significant gap in the literature by investigating the complexities of AI-powered language learning tool integration in real-world classrooms. Utilizing thematic analysis and the Cognitive-Media-Machine Framework as its theoretical foundation, which provides a holistic understanding of the interplay between cognitive processes, media, and machine learning algorithms, the study collects data from 100 language teachers using AI-powered tools in different universities through interview and observation protocols. The findings reveal three nested themes: Pedagogical Enhancements, Technological Challenges, and Student-Centered Learning, highlighting the potential of AI-powered language learning tools to provide personalized feedback and assessment, while underscoring the need for addressing technological challenges and promoting student-centered learning experiences. The study contributes to the existing literature by providing insights into the practical applications and limitations of AI-powered language learning tools, informing language education policy, practice, and research, and highlighting the need for ongoing professional development, technical support, and pedagogical innovation. The research employs a qualitative approach, utilizing thematic analysis to identify emerging themes, and the Cognitive-Media-Machine Framework, which encompasses three key aspects: cognitive processes, media, and machine learning algorithms. The study's key findings emphasize the potential of AI-powered language learning tools to enhance language learning outcomes, while also posing significant technological challenges, and highlighting the importance of promoting student-centered learning experiences. Ultimately, the study addresses a significant gap in the literature, providing valuable insights for language education stakeholders.

Metallurgical and Materials EngineeringVol. 31(2)
University of Lahore (PK), Sir Syed University of Engineering and Technology (PK), University of Gujrat (PK)
Openalex Percentile: Top 14%
Online Learning and Analytics
1.69
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