A Generative AI-Based Assistant to Evaluate Short and Long Answer Questions

Assessment of long and short answers is a tedious task. The evaluation procedure is usually subjective, resulting in inaccuracies and substantial grading discrepancies. Generative AI-based tools have the potential to significantly lessen the burden on teachers and expedite the evaluation process. Accurate semantic representations of data are one of the challenges while developing generative AI-based tools. This paper proposes a model for automatically evaluating the long-short answer that relies on generative AI-based text embedding and semantic similarity. The answers are graded by measuring the cosine similarity between model answers and students’ responses. The model is evaluated against accuracy and root mean square error (RMSE). The proposed model is flexible enough for fine-tuning with other course-specific data sets.