# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Examples of Prominent MLLMs
#### 2.4.2 Proprietary MLLMs  
**GPT-4o (Multimodal Version)**: GPT-4o, developed by OpenAI, integrates visual and textual data for advanced multimodal reasoning. Its applications in education are vast, ranging from interactive learning environments to visual question answering and conversational image interpretation. One of its primary advantages is its exceptional performance and reliability [31]. The model's robust capabilities ensure accurate and consistent results across a wide range of tasks, making it a reliable tool for educators. Moreover, the professional support and regular updates provided by OpenAI enhance its usability and stability in educational settings, ensuring that institutions can deploy the model with confidence.  
GPT-4o's proprietary nature introduces challenges. The high licensing fees can be prohibitive for many educational institutions, particularly those operating on limited budgets. Additionally, its closed design restricts customization, preventing institutions from tailoring the model to specific educational contexts or integrating it with existing systems beyond the predefined parameters.  
Potential applications: GPT-4o can be used to create advanced virtual tutors capable of visual and textual reasoning. For example, it could support biology lessons with real-time microscopic image analysis or assist in art classes with detailed image composition feedback.  
**Kosmos-G**: Developed by Microsoft, Kosmos-G offers enhanced multimodal capabilities, including advanced visual and text comprehension. It is well suited for collaborative and visual learning tools, integrating seamlessly with Microsoft's ecosystem, such as Teams and Office 365, to provide a unified platform for education [32]. This integration simplifies its adoption and enhances user experience, particularly for institutions already utilizing Microsoft products.  
Nevertheless, Kosmos-G's advantages are accompanied by significant limitations. The model's cost can be a major barrier for adoption, as it requires both licensing fees and infrastructure investment. Moreover, its reliance on Microsoft's ecosystem may limit its flexibility, making it less suitable for institutions that use diverse or non-Microsoft technologies.  
Potential applications: Kosmos-G can enhance educational platforms like Teams by integrating multimodal features, such as document summarization and diagram understanding, making collaborative projects more efficient.  
**MM1**: MM1 is a family of multimodal large language models (MLLMs) developed by Apple, designed to achieve state-of-the-art performance in multimodal tasks by carefully analyzing architectural components and data choices [33]. MM1 includes both dense models with up to 30 billion parameters and mixture-of-experts (MoE) models with up to 64 billion parameters. Through comprehensive ablation studies, MM1 identifies crucial design principles for integrating visual and textual data, such as the importance of image resolution, visual encoder capacity, and the mix of pre-training data. The models exhibit enhanced capabilities like in-context learning, multi-image reasoning, and few-shot chain-of-thought prompting. MM1 demonstrates competitive performance across a range of established multimodal benchmarks, highlighting the effectiveness of large-scale multimodal pre-training and offering valuable insights for future MLLM development.  
MM1's proprietary status limits its accessibility. The high costs associated with its deployment, both in licensing and required infrastructure, restrict its use to well-funded institutions. Additionally, its closed system design precludes customization, which may limit its adaptability for diverse educational needs.  
Potential applications: MM1 can support advanced assessments, such as AI-driven exam proctoring or complex data interpretation tasks in STEM fields, making it a valuable tool for high-level education.