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.
In this workshop, we will use the consensus-forming 'Nominal Group Technique' to identify and classify our concerns about AI use in academic writing. As a group, I will guide us through the Nominal Group protocol to synthesise these views into an actionable rubric and compare this with the results of a similar exercise conducted at a UK-based university. If participants consent, data from the workshop will be included in a manuscript analysing the UK data.
Rubrics typically define the lowest levels of performance by what is missing, e.g. ‘does not show critical evaluation’ or ‘does not cite sources accurately’. This tradition is based on the concept of a minimally competent candidate, where a ‘cut score’ is set based on what lecturers would deem minimally acceptable at pass, merit, or distinction level. However, this misses an opportunity to explain to students why AI submissions could meet surface requirements but still be poor quality when judged as the work of a scholar in formation. In this workshop, we will use the nominal group technique to define what might be a critical deficiency that, even when other proficiency areas are evidenced, would warrant a low or failing grade. I will then guide us through an appropriate theoretical framework to synthesise the group views into rubric descriptors intended to help generate dialogic feedback around AI use. By the end of the workshop, participants will have expressed and heard a range of views around assessing academic writing that may have used AI and will have at least two example rubrics to make changes to their assessment and feedback practices.
