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The study investigates the impact of different elements in LLM-generated feedback on learning outcomes and learner acceptance, focusing on the interaction with learners' Big Five personality traits. GPT-5 was used to generate feedback for multiple-choice biology questions, varying six feedback elements. Results from a learning experiment with 321 high school students reveal that while certain feedback elements consistently improve learning outcomes, subjective preferences for feedback vary significantly based on personality traits.
LLM-generated feedback isn't one-size-fits-all: personality dictates which elements resonate with learners, impacting acceptance and potentially learning outcomes.
Large language models (LLMs) show promise for automatically generating feedback in education settings. However, it remains unclear how specific feedback elements, such as tone and information coverage, contribute to learning outcomes and learner acceptance, particularly across learners with different personality traits. In this study, we define six feedback elements and generate feedback for multiple-choice biology questions using GPT-5. We conduct a learning experiment with 321 first-year high school students and evaluate feedback effectiveness using two learning outcomes measures and subjective evaluations across six criteria. We further analyze differences in how feedback acceptance varies across learners based on Big Five personality traits. Our results show that effective feedback elements share common patterns supporting learning outcomes, while learners'subjective preferences differ across personality-based clusters. These findings highlight the importance of selecting and adapting feedback elements according to learners'personality traits when we design LLM-generated feedback, and provide practical implications for personalized feedback design in education.
