# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Virtual Tutors and Chatbots
#### 3.2.4 Case Study: Implementation of LOVA3 in Virtual Tutoring Systems  
The implementation of LOVA3 in virtual tutoring systems offers valuable insights into how MLLMs can enhance educational experiences. LOVA3 is an innovative framework designed to equip MLLMs with the abilities to answer, ask, and assess questions in the context of images [51]. This triad of skills aligns with human learning mechanisms and is crucial for understanding the world and acquiring knowledge.  
Technologically, LOVA3 utilizes two supplementary training tasks, GenQA and EvalQA, to foster the skills of asking and assessing questions. GenQA focuses on enabling the model to generate diverse question-answer pairs from a single input image, thereby equipping the MLLM with the capability to ask questions. This ability encourages learners to engage more deeply with information, enhancing problem-solving skills. EvalQA involves tasking the MLLM to predict the correctness of a given visual-question-answer triplet. This assessment capability allows virtual tutors to evaluate student responses, provide feedback, and guide learners toward a deeper understanding of the material.  
The model architecture of LOVA3 integrates a vision encoder with a large language model through a simple MLP adapter, allowing the system to process and generate multimodal content efficiently. Training involves a mixture of tasks, including traditional visual question answering (VQA), GenQA, and EvalQA, enhancing the model's comprehensive understanding and interactive capabilities.  
In virtual tutoring systems, implementing LOVA3 can lead to several benefits. By enabling the virtual tutor to ask questions and assess responses, the interaction becomes more dynamic and engaging, resembling a human tutor's approach. The tutor's ability to generate questions and assess answers encourages students to think critically and engage more deeply with the content. Incorporating visual elements into questioning and assessment aligns with diverse learning styles and can improve comprehension.  
For example, a virtual tutor using LOVA3 can present an image related to a biology lesson and generate questions that prompt the student to identify structures, explain functions, or predict outcomes. The tutor can then assess the student's responses, provide feedback, and adjust subsequent interactions based on the student's understanding.  
A study by [PERSON] et al. [54] demonstrated that training MLLMs using the LOVA3 framework improved performance across various multimodal datasets and benchmarks. The results underscored the critical role of these additional tasks in fostering comprehensive intelligence in MLLMs. The model showed consistent performance gains, highlighting its effectiveness in enhancing multimodal comprehension.  
While LOVA3 enhances virtual tutoring systems significantly, challenges exist. Handling a wide range of topics and queries requires extensive training data. LOVA3 addresses this by compiling a comprehensive set of multimodal foundational tasks, but scaling this to cover all educational content remains a challenge. Ethical considerations are paramount: privacy, data security, and ensuring appropriate responses are essential, especially when dealing with sensitive information. Educational virtual tutors need to handle complex and varied content, and ensuring the accuracy and relevance of generated questions and assessments requires continuous updates and validations of the model.  
The implementation of LOVA3 in virtual tutors showcases the potential for delivering personalized and effective educational experiences. It highlights how advancements in MLLMs can address the complexities of content and provide scalable solutions that adapt to individual learner needs. By equipping virtual tutors with the abilities to answer, ask, and assess, LOVA3 brings AI-driven education closer to the nuanced and interactive nature of human tutoring.