The evolution of CALL (Computer-Assisted Language Learning) to AI-driven SLA: A Literature-Based Synthesis
DOI:
https://doi.org/10.54536/ajsts.v5i1.6591Keywords:
Adaptive Learning, AI in SLA, Computer-Assisted Language Learning, Educational Technology Evolution, Intelligent Tutoring SystemsAbstract
This literature-based synthesis examines the historical and conceptual shift from Computer-Assisted Language Learning (CALL) to Artificial Intelligence-mediated approaches in Second Language Acquisition (SLA). The study aims to trace the evolution of CALL, analyze how AI transforms language learning processes, and identify the pedagogical and ethical implications of AI enhanced instruction. A structured review of scholarly work from applied linguistics, educational technology, and artificial intelligence was conducted using peer reviewed sources from 1996 to 2024, alongside foundational texts that shaped CALL and SLA theory. The synthesis integrates empirical, theoretical, and technological research, employing thematic and chronological analysis to connect CALL developments with emerging AI practices. Findings reveal that CALL laid essential pedagogical foundations centered on interaction, autonomy, and communicative authenticity, but struggled with adaptivity, contextual feedback, and cultural mediation. AI expands these foundations by enabling personalized learning pathways, intelligent tutoring, automated feedback, and data informed instructional design. However, the analysis also identifies challenges related to algorithmic bias, data privacy, teacher autonomy, learner identity, and risks of dehumanized instruction. The study concludes that AI represents not a replacement of CALL but an expansion of its principles into more adaptive and data driven environments. Future research must address ethical governance, teacher training, and equitable access to ensure that AI supports humanistic, context sensitive, and socially grounded language education.
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