# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Based Insight and Predictive Analytics for Educators
#### 3.5.4 Case Study: Early Warning Systems Using MLLMs  
Early warning systems (EWSs) aim to identify students who are at risk of academic failure or dropping out, allowing educators to intervene proactively [82]. Traditional EWSs rely on structured data such as grades, attendance, and demographic information. Integrating MLLMs enables the analysis of unstructured and multimodal data, providing a more comprehensive risk assessment.  
In this context, data are gathered from various sources, including textual data like discussion posts, emails, and assignment submissions; visual data such as video recordings of classes or online interactions; and audio data from recordings of student speeches or discussions. NLP models like BERT [19] are utilized to analyze textual data for sentiment, topic modeling, and engagement indicators. Computer vision models, such as OpenFace [80], are employed for facial expression recognition to assess emotions and engagement levels. Speech recognition models like Wav2 Vec 2.0 [65] transcribe and analyze audio data for speech patterns and indicators of confusion or hesitation.  
Features extracted by MLLMs are combined with machine learning algorithms--such as random forests or neural networks--to predict at-risk students. For example, in university settings, institutions implement EWSs to monitor student engagement in online courses, using data from learning management systems (LMSs), discussion forums, and assignment submissions [87]. In K-12 education, schools use multimodal data, including classroom observations and student interactions, to identify students needing additional support [88]. Massive open online courses (MOOCs) analyze participant data to predict dropout rates and tailor interventions [89].  
A study by [PERSON] et al. (2019) explored the use of multimodal data and MLLMs to enhance early warning systems in online education [90]. The findings indicated that incorporating multimodal data significantly improved the accuracy of predicting at-risk students compared to models using only traditional data. Key predictive features included textual engagement, where the frequency and sentiment of discussion posts correlated with student success; emotional indicators, where facial expression analysis provided insights into student frustration or confusion during video interactions; and speech patterns, where hesitation and speech rate in audio submissions were linked to comprehension difficulties. Early identification enabled timely interventions such as personalized support, tutoring, or counseling.  
However, challenges were identified, including data privacy and ethics, as collecting and analyzing sensitive data requires strict adherence to privacy regulations and ethical considerations. Technical complexity is also a concern, as implementing MLLMs and processing multimodal data demand significant computational resources and expertise.  
Interpretability is another challenge, as complex models may lack transparency, making it difficult for educators to understand the basis of predictions.  
The case study demonstrates the potential of MLLMs in enhancing early warning systems through the analysis of multimodal data. By providing more accurate and comprehensive risk assessments, educators can intervene effectively to support at-risk students. Addressing challenges related to privacy, technical implementation, and model interpretability is essential for practical deployment.