# A Survey on MLLMs in Education: Application and Future Directions
## 4 Discussion and Future Directions
### Future Directions: Towards a Unified AI-Powered Educational Ecosystem  
The limitations associated with standalone MLLM applications underscore the need for a cohesive and integrated approach. The future of educational technology lies in developing a unified AI-powered educational ecosystem that brings together various LLM and MLLM agents in a coordinated and interoperable framework. Such an ecosystem would address inefficiencies by enabling seamless communication and data exchange between AI agents, educational platforms, and stakeholders. Creating this unified ecosystem involves establishing common standards and protocols for data interoperability. By adopting open architectures and APIs, different AI agents and educational technologies can interact effectively, sharing insights and providing a holistic view of learner progress. This interoperability facilitates personalized learning pathways that adapt based on comprehensive data from multiple sources, enhancing the effectiveness of educational interventions. MLLM agents, which integrate language understanding with multimodal data processing--such as text, images, audio, and video--play a critical role in enriching educational content and interactions. For instance, models like PaLM-E [115] and LLaVA [30] demonstrate the potential of MLLMs in understanding and generating multimodal content. These agents can interpret visual inputs and generate coherent textual explanations, enabling more interactive and engaging learning experiences. LLM agents, built upon advanced language models like GPT-4 [9], serve as conversational interfaces that guide learners through educational materials, answer complex queries, and provide real-time feedback. They enhance learner autonomy by supporting self-directed learning and offering personalized recommendations based on individual progress and preferences. Integrating retrieval-augmented generation (RAG) technology further enhances the capabilities of these AI agents within the educational ecosystem. RAG models combine the strengths of large language models with external knowledge bases, enabling the AI to access and retrieve relevant information from vast datasets in real time [116]. This integration ensures that the AI agents provide up-to-date and accurate information, which is particularly crucial in rapidly evolving fields of study. In the unified ecosystem (see Figure 4), MLLM and LLM agents equipped with RAG capabilities collaborate to deliver adaptive learning pathways. For example, when a learner poses a question that requires current data or specialized knowledge, the RAG-enabled agent can retrieve information from trusted educational databases, scholarly articles, or institutional repositories [117]. This approach not only enhances the depth and accuracy of responses but also allows for personalized content generation that aligns with the learner's curriculum and learning objectives. Furthermore, these AI agents enable global connectivity among learners and educators, fostering cross-cultural exchanges and collaborative projects. Platforms incorporating MLLM, LLM, and RAG technologies can facilitate mentorship opportunities, virtual study groups, and peer feedback mechanisms, promoting peer-to-peer learning on a global scale. Advancements in cloud computing and edge technologies offer scalable and accessible infrastructure for deploying MLLMs [54]. Utilizing cloud-based services allows institutions to leverage powerful AI capabilities without the need for extensive on-premises hardware. This democratization of technology can reduce barriers to adoption, enabling institutions of varying sizes and resources to participate in the unified ecosystem. Partnerships with technology providers and investment in shared resources can further enhance accessibility [118]. Embedding ethical AI practices into the design and implementation of the unified ecosystem is imperative. This includes ensuring transparency in AI algorithms, implementing measures to detect and mitigate biases, and upholding stringent data privacy standards.