# A Survey on MLLMs in Education: Application and Future Directions
## 4 Discussion and Future Directions
### Limitations of Using MLLMs in Educational Applications  
Despite the significant benefits, the deployment of MLLMs in education is accompanied by several limitations that warrant careful consideration. One of the primary challenges is the technical and resource constraints associated with implementing these advanced models. MLLMs require substantial computational power and specialized technical expertise to develop, deploy, and maintain. Educational institutions, especially those in developing regions or with limited funding, may find it prohibitive to invest in the necessary infrastructure. This disparity can exacerbate existing inequalities in educational access and quality [108].  
Data privacy and security concerns also pose significant obstacles. MLLMs often rely on the collection and analysis of extensive personal data to function effectively. In educational settings, this includes sensitive information about students' learning behaviors, performance, and potentially personal identifiers. Ensuring compliance with data protection regulations such as the General Data Protection Regulation (GDPR) in Europe or the Family Educational Rights and Privacy Act (FERPA) in the United States is essential. However, many institutions may lack the robust data governance frameworks required to safeguard this information adequately [109].  
Bias and fairness issues inherent in MLLMs are another critical limitation [110; 111; 112]. These models learn patterns from vast datasets, which may contain historical biases and stereotypes. Consequently, MLLMs can inadvertently perpetuate or even amplify these biases in their outputs, affecting the fairness and equity of educational content and assessments. For example, language models might generate content that is culturally insensitive or skewed towards certain demographics, disadvantaging minority groups. Addressing these biases is essential to prevent the reinforcement of inequalities within the educational system [77].  
Moreover, the use of MLLM applications separately, as standalone solutions, leads to fragmentation and inefficiencies. Isolated applications may not integrate seamlessly with existing educational technologies or with each other, resulting in a disjointed learning experience for students. This lack of interoperability hampers the comprehensive understanding of a learner's progress, as data and insights are siloed within individual applications. Educators may struggle to aggregate information from disparate sources to inform instructional decisions effectively [113]. Consequently, the potential of MLLMs to enhance education is not fully realized when applications operate in isolation.  
Over-reliance on technology and reduced human interaction present additional concerns. While MLLMs can augment educational processes, there is a risk that excessive dependence on AI could diminish the role of educators and the value of human relationships in learning. Social interaction, mentorship, and the development of soft skills are critical components of education that AI cannot fully replicate. Students may miss out on the nuances of human communication, empathy, and collaborative learning experiences, which are essential for personal and professional development.  
Finally, ethical considerations regarding the transparency and accountability of AI decision-making processes are paramount. MLLMs operate as complex black-box models,making it challenging to interpret how they arrive at specific outputs or recommendations. This opacity can hinder trust and acceptance among educators and learners, who may be wary of relying on systems that lack explainability. Ensuring that MLLMs provide interpretable and justifiable outputs is crucial for their successful integration into educational settings [114].