This study examines the integration of artificial intelligence (AI) tools with Nation’s Four Strands Framework in the design of pre-class learning modules for English as a Foreign Language (EFL) flipped classrooms. Focusing on conversational skills development—a domain requiring balanced attention to meaning-focused input/output, language-focused learning, and fluency—the research addresses a critical gap in material design for technology-mediated flipped instruction. Employing a qualitative case study methodology, semi-structured interviews and focus group discussions were conducted with six Indonesian EFL teachers at the tertiary level. Data analysis revealed a four-phase pedagogical model wherein AI tools were systematically deployed to: (1) scaffold meaning-focused input (e.g., AI-generated transcriptions, text adaptation), (2) reinforce language-focused learning (e.g., grammar/vocabulary feedback systems), (3) facilitate meaning-focused output (e.g., conversational AI prompts), and (4) enhance fluency development (e.g., adaptive speaking drills). Key challenges emerged regarding AI’s limitations in contextual appropriateness, feedback reliability, and the risk of pedagogical fragmentation, necessitating teacher mediation to preserve theoretical fidelity. The study contributes to literature on flipped learning by demonstrating how AI can operationalize SLA principles while affirming educators’ central role in curricular decision-making. Practical implications for instructional design and teacher professional development are discussed, alongside recommendations for future research on AI’s efficacy across diverse institutional contexts.
Rahmawati et al. (Fri,) studied this question.