A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

Abstract

Asynchronous discussions are a popular feature in online higher education as they enable instructor-student and student–student interactions at the users’ own time and pace. AI-driven discussion platforms are designed to relieve instructors of automatable tasks, e.g., low-stakes grading and post moderation. Our study investigated the validity of an AI-generated score compared to human-driven methods of evaluating student effort and the impact of instructor interaction on students’ discussion post quality. A series of within-subjects MANOVAs was conducted on 14,599 discussion posts among over 800 students across four classes to measure post ‘curiosity score’ (i.e., an AI-generated metric of post quality) and word count. After checking assumptions, one MANOVA was run for each type of instructor interaction: private coaching, public praising, and public featuring. Instructor coaching appears to impact curiosity scores and word count, with later posts being an average of 40 words longer and scoring an average of 15 points higher than the original post that received instructor coaching. AI-driven tools appear to free up time for more creative human interventions, particularly among instructors teaching high-enrollment classes, where a traditional discussion forum is less scalable.