# A Survey on MLLMs in Education: Application and Future Directions
## 5 Conclusions
### Final Thoughts  
The integration of MLLMs into educational settings marks a substantial step forward in enhancing the learning experience. However, there are several challenges that need to be addressed to ensure their responsible use. Ethical considerations, such as the potential for bias in AI-generated content, privacy concerns related to data collection, and the need for transparency in AI decision-making processes, are critical issues that must be continually evaluated [77; 125]. Furthermore, it is essential to consider the implications of these technologies on educational equity, ensuring that all students have equitable access to the benefits of MLLMs, regardless of socioeconomic status or geographic location [73].  
Another major ethical issue is algorithmic bias. MLLMs, like all AI systems, are only as unbiased as the data on which they are trained. If the training data reflect existing biases, these can be perpetuated or even exacerbated by the AI system, leading to unfair educational outcomes for certain groups of students. It is crucial that educational institutions and AI developers work together to ensure that the datasets used to train MLLMs are diverse and representative [77].  
Further research and development are crucial to fully realize the potential of MLLMs in education. This includes not only advancing the technical capabilities of these models but also ensuring that their deployment in educational environments is guided by ethical principles and a commitment to equity. Collaboration among educators, technologists, policymakers, and ethicists will be vital in shaping the future of education in the age of AI. Developing comprehensive frameworks for the ethical evaluation of AI tools in education can help address these challenges and ensure that the integration of MLLMs benefits all learners [62].  
Moreover, continuous audits and assessments of AI systems are necessary to identify and mitigate biases, ensuring that these systems remain fair, transparent, and effective. The development of MLLMs should also be inclusive, with diverse AI development teams representing different perspectives and experiences to help identify potential biases early in the development process [126].  
To move forward, stakeholders must focus on creating inclusive, adaptable, and responsible educational environments that leverage the power of MLLMs while safeguarding against their potential risks. This proactive approach will help maximize the benefits of AI-driven education, preparing students for a rapidly evolving digital world and ensuring that the future of learning is bright, equitable, and effective.  
Formal analysis, W.X.; investigation, W.X., T.Z., B.L., J.W.; resources, W.X., J.W.; writing--original draft preparation, W.X.; writing--review and editing, W.X., T.Z., B.L.; visualization, W.X.; supervision, T.Z., B.L.; project administration, J.W.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ARC Linkage Project (LP220200808) and ARC Discovery Project (DP230100246) from the Australian Research Council, Australia.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.