Data Privacy in Special Education: A Three-Level Solution for Generative AI
Protecting Student Data
Protecting the data of students with disabilities is crucial for several reasons. Firstly, all students have a right to privacy, and their personal and sensitive information must be kept confidential to protect them from unwanted exposure of their Personal Identifiable Information (PII) and its potential misuse. Ensuring the protection of this information helps prevent discrimination and stigmatization, and in more critical cases, identity theft. To ensure data privacy, legal standards such as FERPA and IDEA have been designed, which require schools to limit the access to the students’ PII.
Generative AI in Special Education
Special education professionals have started to notice the potential of Generative AI to create Individualized Education Programs (IEPs), as it could help provide recommendations of personalized learning experiences by analyzing vast amounts of data, and tailor educational paths to each student’s unique needs. However, there is a critical concern: IEPs require detailed information about students’ disabilities, learning needs, medical history, and academic performance. Because many AI tools and platforms used in education are developed by third-party vendors, sharing student data through these tools requires trusting that vendors will handle the data responsibly and securely. Any lapse in their data protection practices can result in unauthorized access or exposure.
A Three-Level Solution for Generative AI
Adam suggests a three-level solution for the safe implementation of Generative AI in school districts. The levels are organized in terms of how much personalization of the tool is possible. For each level, he mentions that it is necessary to ponder their risks and rewards.
General Level: Utilizing a Large Language Model (LLM)
- Reward: Microsoft and Google ensure their tools comply with student data protection regulations. These tools protect user and organizational data while chat prompts and responses are not saved. Additionally, these companies ensure that students’ information is not retained or used to train the AI models.
- Risk: The risk is very low in terms of security, yet it exists. Moreover, there might be some loss in functionality compared to other tools, as it cannot build on from a prompt standpoint.
Small Language Models
- Reward: An SLM maintains the privacy protections established by Google or Microsoft while personalizing the tool for a specific need. By targeting a specific task, it is also easier to set specific guardrails and train teachers.
- Risk: In addition to the risks mentioned with LLMs, they might have a more limited knowledge base compared to an LLM.
The Open-Source Model
- Reward: The models are highly customizable, allowing districts to tailor them to their specific needs and integrate them with existing systems. This allows them to maintain control over their data, ensuring it is used in compliance with privacy regulations and local policies.
- Risk: Setting up and maintaining an open-source model requires significant technical expertise and substantial computational resources, which may necessitate additional investments in infrastructure and staff training. There are security risks involved in handling sensitive student data, and ensuring robust protection is essential.
Conclusion
Integrating Generative AI tools in school districts offers significant benefits, particularly in creating personalized learning experiences and Individualized Education Programs (IEPs). However, it’s crucial to balance these innovations with strong data privacy measures. By choosing the right AI model—whether a general Large Language Model, a tailored Small Language Model, or a customizable open-source model—districts can enhance education while protecting sensitive student information.
FAQs
Q: What are the legal standards that require schools to limit access to students’ PII?
A: FERPA and IDEA are the legal standards that require schools to limit access to students’ PII.
Q: What is the risk of using Large Language Models (LLMs) in education?
A: The risk is very low in terms of security, yet it exists. Moreover, there might be some loss in functionality compared to other tools, as it cannot build on from a prompt standpoint.
Q: What is the advantage of using Small Language Models (SLMs) in education?
A: An SLM maintains the privacy protections established by Google or Microsoft while personalizing the tool for a specific need. By targeting a specific task, it is also easier to set specific guardrails and train teachers.
