@article{Liu_2026, title={From Language Training to Interdisciplinary Learning: A Case Study of Task Reconstruction in Vocational Undergraduate College English}, volume={5}, url={http://dx.doi.org/10.22161/ijtle.5.4.1}, DOI={10.22161/ijtle.5.4.1}, abstractNote={<jats:p>This paper reports a qualitative practice-based case study of task reconstruction in a vocational undergraduate College English course in China. Situated in the emerging policy context of Chinese vocational undergraduate education, the study addresses a pedagogical problem that is increasingly visible in public English courses: students are expected to develop communicative competence, examination readiness, humanistic understanding, professional expression, and interdisciplinary reasoning, yet classroom practice often remains constrained by decontextualised language exercises and test-oriented expectations. Using the unit Bamboo Rhythm: Where Ancient Roots Meet Modern Roads as the central case, the teacher reconstructed a reading lesson into an interdisciplinary learning chain connecting bamboo culture, material properties, engineering processes, sustainable design, knowledge management, peer assessment, and project-based output. Data were drawn from lesson plans, revised teaching slides, classroom interaction records, student notes, knowledge-management sheets, student artefacts, peer-evaluation materials, and reflective teaching documents. The materials were analysed thematically to examine how classroom tasks moved students from familiar experience to disciplinary reasoning, from vocabulary learning to knowledge organisation, and from teacher-led assessment to participatory evaluation. The paper argues that vocational undergraduate College English can function not merely as language training, but as a pedagogical space where language, vocational knowledge, cultural interpretation, and learner agency are integrated through carefully scaffolded tasks.</jats:p>}, number={4}, journal={International Journal of Teaching, Learning and Education}, publisher={AI Publications}, author={Liu, Xipeng}, year={2026}, pages={1–10} }