We find ourselves at a pivotal moment where the role of generative AI and other technological tools is reshaping the way we teach and learn languages. The question that lies before us is not whether these innovations will shape the future, but rather how we, as educators, will harness their potential to create meaningful and effective language instruction.
The title of our conference, No Fate: The Future is Not Set, underscores our belief that the future is not predetermined. We hope that our conference will serve both as a platform for collaboration and a catalyst for change as it is via the collective effort of educators, researchers, and innovators that the trajectory of language teaching and learning will be determined. By fostering collaboration, sharing insights, and pushing the boundaries of what is possible, we can shape the future of language education.
How does generative artificial intelligence (AI) compare to advanced learners in different output tasks? This research explores the performance of generative AIs and advanced language learners in prompted essay writing and translation tasks. The integration of generative AI in language teaching has presented both opportunities and challenges. While scholars acknowledge the benefits of incorporating generative AI in language instruction (Baidoo-Anu & Ansah, 2023; Kohnke et al., 2023), concerns persist regarding potential adverse effects, such as overreliance on these tools and decreased human interaction (Zunaidah et al., 2023). Our research involves a comparative analysis of the writing and translation tasks undertaken by advanced Chinese learners and generative AI. When presented with prompts for essay writing or engaged in conversations, generative AI excelled in generating error-free sentences utilizing its extensive linguistic database. The outputs of advanced language learners also display few errors, thanks to the circumventing strategy of employing alternative expressions when faced with unfamiliar ideas. However, we discovered translation tasks pose more linguistic challenges. Generative AI and advanced learners both often produce awkward or inappropriate translations for target language audiences, failing to convey nuanced tones, styles, registers, long-distance references, and the intended functions, even when explicitly prompted to do so. Recognizing the limitations of generative AI and areas where advanced language learners need improvement, we propose a novel approach where human instructors, language learners, and generative AI are engaged in multiple rounds of writing/translation revision (self and peer revisions) activities. Instructors may utilize generative AI to generate high-quality translated texts of large quantities on a wide range of topics. Advanced learners are then tasked with enhancing and revising the texts and receive explicit instructions and feedback from human instructors on discourse and functional issues. Using this approach, our presentation will share instructional design ideas, classroom activities, and actual student assignments.
