Integrating Generative Ai into Task-Based Language Teaching: Enhancing Learners’ Speaking Fluency and Digital Literacy - Muhammad Idul (Balla' Caradde Education, 2026)
As generative artificial intelligence (GenAI) rapidly reshapes higher education, evidence on its pedagogically effective integration into English as a Foreign Language (EFL) instruction remains limited. This quasi-experimental study examined the effects of embedding GenAI tools within a Task-Based Language Teaching (TBLT) framework on university students’ speaking proficiency and digital literacy. Sixty undergraduate EFL students were assigned to either a GenAI-enhanced TBLT group or a traditional TBLT group using a pre-test–post-test control group design. Oral proficiency was assessed using IELTS speaking rubrics, while interviews and reflective journals explored students’ learning experiences. Results showed that the experimental group achieved significantly greater gains in speaking fluency, lexical resource, and grammatical accuracy (p < 0.05). Qualitative findings further revealed that GenAI-supported feedback reduced speaking anxiety and promoted learner autonomy and collaboration. This study contributes empirical evidence for AI-enhanced TBLT, offering practical guidance for integrating GenAI to advance communicative competence and digital literacy in EFL contexts.