STUDENTS' DIRECTIVE SPEECH ACTS IN ACADEMIC WHATSAPP GROUPS: A GENERATIVE AI-ASSISTED PRAGMATIC LEARNING DESIGN
DOI:
https://doi.org/10.21009/ishel.v2i1.69406Keywords:
directive speech acts, academic WhatsApp groups, digital pragmatics, generative AI, pragmatic learningAbstract
This study examines students' directive speech acts in academic WhatsApp Groups (WAGs) and formulates a generative AI-assisted pragmatic learning design based on authentic digital academic communication. The study employed a descriptive qualitative case-study design. The data comprised 1,956 anonymized messages from 15 academic WAGs, from which 481 candidate directive utterances were identified through rule-based screening and contextual pragmatic reading. The analysis drew on speech act theory, politeness theory, Aziz's Principle of Mutual Consideration, digital pragmatics, data-driven learning, usage-based learning, construction grammar, and generative AI as pragmatic scaffolding. The findings show that students' directives mainly involved requests for participation or attendance permission, clarification or information, task guidance or submission, technical access, and confirmation. Students mitigated directives through honorific address terms, greetings, apologies, permission markers, request markers, reasons, indirect questions, gratitude, and prayer-hand emojis. Potential pragmatic problems appeared in utterances that were too direct, informal, unclear in addressee orientation, potentially perceived as assessment follow-up, or sensitive in personal disclosure. The study proposes a learning design involving exposure to authentic WAG data, noticing of politeness markers, contextual interpretation, AI-assisted revision, response simulation, and metapragmatic reflection. Generative AI is positioned as a reflective scaffold, not as an authority on pragmatic appropriateness.
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