Designing Digital Writing Pedagogy: An SFL-Informed Genre-Based Framework for Higher Education

Digital communication has changed what counts as writing in higher education. Students compose across word processors, collaborative platforms, websites, presentations, infographics, video, and generative artificial intelligence (GenAI) environments, yet writing pedagogy often remains organized around monomodal print genres and sentence-level correctness. This conceptual article develops an integrated framework that connects Systemic Functional Linguistics (SFL), the Genre-Based Approach (GBA), and digital multimodal composing (DMC). Using an integrative, theorybuilding design (Jabareen, 2009; Torraco, 2016) and a transparent literature procedure reported with reference to PRISMA 2020 principles (Page et al., 2021), the article synthesizes foundational works and 2021–2026 research in L2 writing, English for Academic Purposes, multimodality, and GenAI-mediated composition. Five research gaps are identified: limited integration of SFL genre pedagogy and DMC, weak alignment between multimodal tasks and assessment, an under-specified linguistic dimension of digital composing, limited transfer evidence, and misalignment between GenAI-supported composing and conventional assessment. In response, the article proposes an eight-phase SFLInformed Digital Genre Pedagogy framework and a six-dimension assessment architecture. Its novelty lies in aligning field–tenor–mode analysis, genre staging, and multimodal orchestration within one teaching–learning cycle; deriving assessment dimensions from the same phases; treating GenAI output as an object of functional-genre evaluation and verification; and adding a functional-sufficiency principle for metalanguage. Theoretical validity is argued through a traceability matrix and internal-consistency checks, and a staged protocol for empirical validation (expert review with content validity indices, rubric reliability, and argument-based validation) is specified. The framework is offered as a testable design architecture rather than an empirically validated model.