Ensuring the Privacy of Student Data in Special Education with Generative AI
Protecting the data of students with disabilities is a critical aspect of special education. All students have a right to privacy, and safeguarding their personal and sensitive information is essential to prevent discrimination, stigmatization, and potential misuse of their Personal Identifiable Information (PII). In more severe cases, inadequate data protection can lead to identity theft, underscoring the urgency of maintaining secure access and data privacy standards in educational settings.
Legal standards such as the Family Educational Rights and Privacy Act (FERPA) and the Individuals with Disabilities Education Act (IDEA) have been established to regulate the sharing and protection of student data in schools. These regulations dictate that schools must limit access to students’ PII to authorized personnel only, emphasizing the need for stringent data privacy measures in educational institutions.
Generative Artificial Intelligence (GenAI) tools have shown promise in revolutionizing the creation of Individualized Education Programs (IEPs) for students with disabilities. By analyzing vast amounts of data, these tools can provide personalized recommendations for learning experiences tailored to each student’s unique needs. However, the implementation of GenAI in special education raises concerns about data privacy, especially when it comes to the detailed information required for developing IEPs.
Adam Garry, a seasoned professional in the field of education strategy, emphasizes the importance of data privacy in the context of GenAI tools. As the former Senior Director of Education Strategy for Dell Technologies and current President of StrategicEDU Consulting, Garry has extensive experience supporting districts in integrating GenAI into their educational practices. CoSN, a leading organization in educational technology, approached Garry to discuss the significance of data privacy in crafting IEPs with GenAI while maintaining the confidentiality of student information.
To address the data privacy challenges associated with GenAI tools in special education, Garry proposes a three-level solution that balances personalization with security considerations. Each level offers varying degrees of customization and poses different risks and rewards, allowing educators to make informed decisions about the implementation of GenAI in their school districts.
General Level: Utilizing Large Language Models
At a general level, educators can leverage Large Language Models (LLMs) such as Google’s Gemini or Microsoft’s Copilot to create personalized content for students. These tools are specifically designed for educational purposes and prioritize data privacy by ensuring compliance with student data protection regulations. Microsoft and Google have implemented measures to safeguard user and organizational data, ensuring that chat prompts and responses are not saved, and students’ information is not retained or used to train the AI models.
While LLMs offer robust data protection features, there are potential limitations in terms of functionality compared to other tools. For instance, LLMs may not have the capability to “learn” from previous answers due to the non-retention of data, which could impact the tool’s adaptability and personalization potential.
Small Language Models
Educators can also consider building Small Language Models (SLMs) using technology from Microsoft or Google to create tailored tools for specific tasks such as developing IEPs. SLMs are simpler, resource-efficient text processors that can be deployed on everyday devices like smartphones, offering a more focused approach to personalized learning experiences.
By customizing SLMs to target specific tasks, educators can maintain privacy protections while enhancing personalization for individual student needs. However, SLMs may have a more limited knowledge base compared to LLMs, which could affect the tool’s overall effectiveness in generating personalized recommendations.
The Open-Source Model
For school districts seeking a high level of customization and control over their GenAI tools, the open-source model presents a viable option. Open-source models make the underlying code and data publicly available for modification and distribution, allowing districts to tailor the tool to their specific needs and integrate it with existing systems.
While open-source models offer unparalleled flexibility and customization opportunities, they require significant technical expertise and resources to set up and maintain. Handling sensitive student data in this context poses security risks that must be carefully managed, and ensuring compliance with privacy regulations and local policies can be complex and challenging without formal customer support.
In conclusion, the integration of Generative AI tools in special education holds immense potential for enhancing personalized learning experiences and developing tailored IEPs for students with disabilities. However, it is crucial for educators and school districts to prioritize data privacy and security when implementing these tools. By selecting the appropriate AI model and establishing robust data protection measures, educators can leverage GenAI to support diverse student needs in a secure and inclusive educational environment.
