# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Grading and Assessment Tools
#### 3.6.4 Case Study: Reducing the Cost of Short-Answer Scoring with MLLMs  
Automated short-answer scoring (SAS) is the task of automatically evaluating brief student responses to prompts based on predefined rubrics and reference answers [20; 41; 103]. SAS is particularly challenging due to the variability in acceptable answers and the need for models to understand nuanced student inputs. A significant barrier to the widespread adoption of SAS systems is the cost associated with preparing training data for each new prompt, as rubrics and reference answers differ between prompts [23].  
A recent study by [PERSON] et al. (2024) addressed this challenge by proposing a two-phase approach to reduce the cost of training SAS models [104]. The approach involves pre-fine-tuning a language model on existing rubrics and answers with gold score signals from annotated prompts and then fine-tuning it on a new prompt with limited data. By utilizing key phrases--representative expressions that answers should contain to increase scores--the model learns the relationship between key phrases and student answers across different prompts.  
In the pre-fine-tuning phase, the model is trained on cross-prompt data, enabling it to learn general scoring principles shared across prompts. Specifically, the model uses the key phrases from rubrics to understand what constitutes a high-quality answer in a general sense. During the fine-tuning phase, the model is adapted to a new prompt using limited data, benefiting from the knowledge acquired during pre-fine-tuning without requiring access to the proprietary cross-prompt data.  
The study utilized BERT [19] as the base language model and conducted experiments on a dataset enriched with a large number of prompts, rubrics, and answers. The findings indicated that fine-tuning on existing cross-prompt data with key phrases significantly improves scoring accuracy, especially when the training data for the new prompt is limited. The model demonstrated improved generalizability and reduced the amount of in-prompt data required for effective scoring.  
Key insights included the importance of designing the model to learn the general properties of the scoring task and leveraging key phrases to align with the rubrics. By focusing on the relationship between key phrases and student answers, the model effectively captured the essential elements required for high-quality responses, enabling it to generalize across different prompts.  
Challenges identified in the study included ensuring data accessibility, as cross-prompt data might be proprietary and not readily available for all educators. The proposed two-phase approach addresses this by allowing the pre-fine-tuned model's parameters to be shared without exposing the underlying data, thus maintaining data privacy while still benefiting from the knowledge gained during pre-fine-tuning. Additionally, the study emphasized the need for the model to effectively learn from diverse prompts to generalizewell to new prompts, suggesting that increasing the diversity of prompts during pre-fine-tuning enhances the model's performance on unseen prompts.  
The incorporation of MLLMs in SAS demonstrates significant potential for enhancing the efficiency and scalability of short-answer grading. By reducing the data requirements and leveraging cross-prompt learning, educators can adopt automated scoring systems with lower costs and improved performance. Addressing challenges related to data accessibility, model generalizability, and alignment with scoring rubrics is essential for the broader adoption of such systems.