Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment

Understand

The advancement of Artificial Intelligence (AI) has created opportunities for e-learning, particularly in automated assessment systems that reduce educators' workload and provide timely feedback to students.

• However, developing effective AI-based assessment tools remains challenging due to the substantial resources required for collecting and annotating real student data.
• This study investigates the potential and gap of simulative data to address this limitation.
• Through a two-phase experimental study, we examined the effectiveness and gap of Large Language Model generated synthetic data in training educational assessment systems.