Cognitive-Psycholinguistic and Sociocultural Approaches to Second Language Acquisition in AI-Enhanced and Computer-Assisted Language Learning Environments: A Critical Comparison of Two Empirical Studies

This paper critically compares the cognitive-psycholinguistic and sociocultural approaches in second language acquisition (SLA) by analysing two empirical studies which investigate second language (L2) learner development in AI-enhanced and computer-assisted language (CALL) learning environments. As digital technologies increasingly reshape the language learning landscape, it is vital to understand how different theoretical paradigms engage with these tools. While the cognitive-psycholinguistic and sociocultural approaches have often been positioned in opposition to one another, this paper argues for their complementarity, focusing on the cognitive-interactionist model and sociocultural theory (SCT) to highlight their convergences. Specifically, it examines how each framework conceptualises three core dimensions related to interaction in SLA: the role of others, the role of the learner, and the research site. The analysis reveals that the cognitive-interactionist model emphasises input, processing, and output, whereas SCT underscores social collaboration and co-constructing L2 learning knowledge. The findings suggest that both perspectives are needed to capture a more comprehensive account of the cognitive and social complexities involved in SLA, particularly in digital contexts. The paper concludes by advocating a transdisciplinary approach to L2 research that integrates theoretical insights from different perspectives to better understand and address the evolving role of technologies in language learning.

Publication Details

Journal
Apollo (University of Cambridge)
Published
2025-11-13
DOI
https://doi.org/10.17863/cam.123116
Primary Topic
EFL/ESL Teaching and Learning
Type
article
Field-Weighted Citation Impact
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article

Cognitive-Psycholinguistic and Sociocultural Approaches to Second Language Acquisition in AI-Enhanced and Computer-Assisted Language Learning Environments: A Critical Comparison of Two Empirical Studies

Apollo (University of Cambridge)
EFL/ESL Teaching and Learning
article

Cognitive-Psycholinguistic and Sociocultural Approaches to Second Language Acquisition in AI-Enhanced and Computer-Assisted Language Learning Environments: A Critical Comparison of Two Empirical Studies

article en

Abstract

This paper critically compares the cognitive-psycholinguistic and sociocultural approaches in second language acquisition (SLA) by analysing two empirical studies which investigate second language (L2) learner development in AI-enhanced and computer-assisted language (CALL) learning environments. As digital technologies increasingly reshape the language learning landscape, it is vital to understand how different theoretical paradigms engage with these tools. While the cognitive-psycholinguistic and sociocultural approaches have often been positioned in opposition to one another, this paper argues for their complementarity, focusing on the cognitive-interactionist model and sociocultural theory (SCT) to highlight their convergences. Specifically, it examines how each framework conceptualises three core dimensions related to interaction in SLA: the role of others, the role of the learner, and the research site. The analysis reveals that the cognitive-interactionist model emphasises input, processing, and output, whereas SCT underscores social collaboration and co-constructing L2 learning knowledge. The findings suggest that both perspectives are needed to capture a more comprehensive account of the cognitive and social complexities involved in SLA, particularly in digital contexts. The paper concludes by advocating a transdisciplinary approach to L2 research that integrates theoretical insights from different perspectives to better understand and address the evolving role of technologies in language learning.

Apollo (University of Cambridge)
Quality Education
Openalex Percentile: Top 31%
EFL/ESL Teaching and Learning
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