Teacher Readiness for AI-Assisted Formative Assessment: Repurposing Telegram in Low-Resource ESL Contexts

This paper introduces a conceptual framework for understanding how English as a Second Language (ESL) teachers develop readiness to use Telegram for AI-assisted formative assessment in low-resource contexts. It draws on Self-Determination Theory (SDT), Technological Pedagogical Content Knowledge (TPACK), Sociocultural Theory (SCT), and Metacognition to create an integrated model that moves beyond single-lens explanations of digital readiness. The framework presents two new constructs: Multi-Use Educational Repurposing (MUER) and AI-Assisted Formative Repurposing (AAFR). MUER explains how teachers creatively adapt Telegram’s features, such as polls, channels, and pinned posts, to support continuous formative assessment. AAFR focuses on the use of automation and bots to provide scalable and efficient feedback in low-bandwidth environments. The model conceptualises readiness as a dynamic and reflective process shaped by motivation, knowledge, collaboration, and metacognitive regulation. It contributes both theoretical insight and practical guidance for teacher professional development, particularly in resource-constrained ESL settings. The study positions teachers as active designers who repurpose everyday communication platforms for equitable and sustainable formative assessment.