Empowering Assessors in Providing Quality Feedback with GenAI Assistance: A Preliminary Exploration

Chen, Zexuan; Cross, Simon and Rienties, Bart (2024). Empowering Assessors in Providing Quality Feedback with GenAI Assistance: A Preliminary Exploration. In: Technology in Education. Digital and Intelligent Education (Lee, Lap-Kei; Poulova, Petra; Chui, Kwok Tai; Černá, Miloslava; Wang, Fu Lee and Cheung, Simon K. S. eds.), Communications in Computer and Information Science (CCIS), Springer, Singapore, pp. 134–148.

DOI: https://doi.org/10.1007/978-981-96-0205-6_10

Abstract

This study examines the impact of GenAI on assessors’ ability to provide feedback during peer assessment. Utilizing a customized peer assessment system, PeerGrader, six undergraduate participants used either a GenAI-facilitated or non-GenAI-facilitated condition to provide feedback on their peers’ English as a Foreign Language (EFL) writings. The assessors were categorized as three types based on their utilization of GenAI tools for feedback refinement: Self-sufficient Masters, Cautious Adopters, and Sustained Users. An analysis of the feedback provided by the six assessors in the three peer assessments revealed a positive influence of GenAI, as it helped assessors focus more on discourse-level aspects, apply more helpful feedback as indicated in more specific praise and criticism, more reasoning and exemplification. Post-course interviews indicated that participants strategically employed GenAI due to language challenges but also highlighted its benefits in assisting with feedback delivery. These findings tentatively suggest that integrating GenAI into peer assessment can positively impact the feedback quality.

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