# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Powered Learning Management Systems (LMSs)
#### Case Study: Integration of MLLMs in Coursera  
Coursera is one of the world's leading online learning platforms, partnering with universities and organizations to offer courses, specializations, certificates, and degree programs [72]. The platform serves millions of learners globally, providing access to a wide range of educational content. Coursera has integrated AI technologies to enhance its platform, aiming to improve learner engagement, personalize learning experiences, and increase the effectiveness of online education [68].  
Coursera leverages various AI technologies, potentially including MLLMs, to enhance its platform. Natural language processing (NLP) is used for processing course content, generating summaries, and facilitating search and recommendation systems [68]. Machine learning algorithms analyze learner data to personalize course recommendations and adapt content delivery based on individual performance and preferences [73]. AI algorithms provide instant feedback on assignments, particularly in programming and technical courses [74]. While the specific use of MLLMs is not publicly detailed, Coursera handles various content types, including video lectures, readings, quizzes, and interactive assignments.  
Coursera uses AI to recommend courses and content to learners based on their interests, past activity, and learning goals [64]. AI-powered translation services enable course content to be accessible in multiple languages, enhancing inclusivity [75]. For certain subjects like computer science, AI algorithms grade assignments and provide feedback, improving efficiency and scalability [74]. Additionally, AI analyzes learner progress to suggest skill improvements and potential career paths [68].  
Coursera's AI-driven recommendation system increases learner engagement by suggesting relevant courses and content [64]. AI enables Coursera to manage a vast number of learners and courses, providing consistent quality of education at scale [68]. Personalized learning paths and instant feedback contribute to better learner performance and satisfaction [73]. Language translation and multimodal content delivery make education accessible to a global audience with diverse needs [75].  
Challenges include data privacy and security: handling sensitive learner data requires robust security measures and compliance with regulations such as GDPR [76]. Ensuring that AI algorithms do not perpetuate biases is critical, and models must be trained on diverse datasets to mitigate this risk [77]. Learners and educators may require understanding of how AI makes recommendations or grades assessments, highlighting the need for transparency and explainability [78].  
While specific details on Coursera's use of MLLMs are not publicly disclosed, the potential integration of MLLMs could enhance several aspects of the platform. MLLMs can analyze multimodal learner data to provide more accurate and personalized content recommendations. Incorporating models like GPT-4 [9] could enable conversational interfaces for learner support and engagement. MLLMs could assist in generating and curating content across text, images, and videos, enriching the learning materials available. Moreover, multimodal translation and content adaptation can make courses more accessible to learners with disabilities or those speaking different languages.  
Coursera's integration of AI technologies demonstrates the significant impact of AI-powered LMS platforms in scaling education and personalizing learning experiences. While explicit use of MLLMs is not detailed, the potential for incorporating such technologies offers avenues for further enhancing the platform's capabilities. Addressing challenges related to data privacy, bias, and transparency is essential for the ethical and effective implementation of AI in education.