Speaker
Description
The rapid uptake of generative AI is transforming how engineering students produce multimodal texts - slides, diagrams, and AI-generated images for technical presentations, yet existing assessment rubrics rarely account for work that is partly machine-created. This raises pressing questions of fairness, validity, and academic integrity: how can teachers assess multimodal communicative competence when authorship is shared between student and AI? This study designs and pilot-tests a multimodal assessment rubric for AI-assisted technical presentations in an English for Specific Purposes (ESP) course for engineering students. Drawing on multiliteracies pedagogy and validity theory, the rubric distinguishes students' communicative and design decisions from AI-generated content. It was developed and trialled with a cohort of undergraduate presentations and refined through teacher–student feedback. The study is expected to yield practical, transparent criteria for evaluating AI-mediated multimodal work while safeguarding academic integrity. For TESOL and ESP practitioners, it offers a classroom-ready instrument and design principles for AI-aware assessment, supporting fairer evaluation and stronger learner agency in technical English contexts.
Keywords: multimodal assessment, rubric design, AI-assisted learning, English for Specific Purposes, academic integrity
