Ashton Dawes
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Presentation Does It Sound Right? Podcast Audio Production Inspires Self-Evaluation more
This presentation examines how audio editing in L2 podcast production sparks language acquisition beyond traditional speaking practice. Drawing on open-ended reflections from 103 university students in an EMI media studies course, we demonstrate how technical editing decisions become opportunities for language hypothesis testing. Students answered four reflection prompts after producing podcast episodes. Using inductive thematic analysis (Braun & Clarke, 2006), three teacher-researchers, working in pairs, independently coded responses by question. Based on preliminary codes, we collaboratively developed a codebook with defined categories (Nowell et al., 2017) and maintained reflexive audit trails. Average interrater agreement was 74.6%, with discrepancies resolved through discussion. Emerging themes showed relationships between editing difficulties, collaboration, and perceived utility for English learning. As students determined whether recordings “sounded right,” they engaged in critical listening and gained awareness of how to improve their English. Technical challenges increased motivation and investment through authentic purpose, editorial autonomy, and the desire to produce polished work. Findings echo Kuhn (2015) on collaboration and Azizi et al. (2022) on podcasting’s value for linguistic output. Podcast editing fosters metacognitive awareness and supports language refinement, offering a meaningful context for L2 development. Implications for integrating technical skills into language learning curricula will be discussed.
Workshop AI as a Feedback Companion: Learning to Seek, Analyse, and Use AI for Feedback in Writing more
Feedback is essential for language acquisition and writing development, helping students assess performance, identify weaknesses, and refine their skills (Papi et al., 2024; Hyland, 2019). This has led to a push for self-assessment (Lee, 2016), and recognition of the necessity of timely feedback (Fu & Li, 2022). Providing only personalized, self-sought feedback can be challenging for teachers—a challenge that Generative AI can help address. This interactive workshop explores how to integrate AI-driven feedback into L2 writing classes by guiding students in developing effective feedback-seeking behaviors. While AI tools provide accessible feedback, students need support in asking effective questions, evaluating AI responses, and refining their writing based on relevant feedback. Participants will experience activities designed to establish expectations about AI’s role as a feedback companion and examine how students interact with AI versus peers and instructors. The discussion will also explore how this approach can be adapted to different learning contexts. Through discussion and suggested instructional materials, this session highlights the importance of teaching students how to be feedback-seekers in the era of Generative AI. Participants will leave with concrete strategies for helping students engage with AI feedback effectively and become more independent writers.