# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Evolution to Multimodal Large Language Models (MLLMs)  
To address the limitations of traditional LLMs, researchers have developed multimodal large language models (MLLMs), which extend the abilities of LLMs by integrating and processing diverse data modalities alongside text [6; 12; 13]. MLLMs represent a significant advancement in AI, enabling the fusion of textual and non-textual data to create models that can comprehend and generate contextually relevant outputs across different modalities. This integration allows MLLMs to perform complex tasks that mirror human-like understanding and communication, such as interpreting images with accompanying descriptions or generating detailed visual content from textual prompts.  
The development of MLLMs has led to several notable models that demonstrate the seamless integration of multiple modalities. One such model is OpenAI's CLIP (Contrastive Language-Image Pre-training), which learns visual concepts from natural language supervision [14]. CLIP aligns images and textual descriptions in a shared embedding space, allowing it to perform tasks such as image classification, object recognition, and image-text retrieval without explicit task-specific training data. By leveraging a contrastive learning approach, CLIP can understand the relationship between images and text, making it highly versatile in handling multimodal data.  
Similarly, DALL-E and DALL-E 2 are models capable of generating high-quality images from textual prompts by utilizing diffusion models conditioned on text embeddings [15; 16]. These models showcase the ability to merge text and visual understanding seamlessly, creating novel images that correspond to detailed textual descriptions. This capability has profound implications for creative industries, design, and educational content creation, where visualizations generated from textual concepts can enhance comprehension and engagement.  
Another significant development is DeepMind's Flamingo, a visual language model that can perform few-shot learning across multiple modalities [13]. Flamingo integrates vision and language modalities in a single model, enabling it to handle tasks like visual question answering, image captioning, and dialogue with minimal task-specific training. Byleveraging large-scale pre-training and attention mechanisms [17], Flamingo can adapt to new tasks with limited data, highlighting the potential for efficient and scalable deployment of MLLMs in various applications.  
In educational contexts, MLLMs support diverse learning needs by facilitating adaptive and inclusive educational experiences. For example, they can generate interactive learning materials, produce visual explanations for complex topics, and provide accessibility features, such as audio descriptions for visual content. The transition from text-based LLMs to MLLMs thus marks a significant shift in educational technology, offering educators and students new opportunities to interact with AI in more dynamic, immersive, and effective ways [18].  
As MLLMs continue to evolve, they will further redefine the educational landscape, enabling a deeper level of personalization, engagement, and accessibility in learning. This integration of multiple modalities aligns well with the diverse learning styles and needs of today's students, enhancing both teaching and learning experiences.