Six tips to help your school make the most of AI

Key points:

It's never too early for campus or district IT teams to start planning for upcoming technology upgrades and implementations. Because many of these updates occur over summer break, teams can take advantage of the upcoming spring semester to ensure their data is in optimal condition to support new ai tools.

ai has already had a significant impact on education by improving the way students learn, teachers teach, and educational institutions operate. The World Economic Forum <a target="_blank" href="https://www.weforum.org/press/2024/04/revolutionizing-classrooms-how-ai-is-reshaping-global-education/” target=”_blank” rel=”noreferrer noopener”>Shaping the future of learning: the role of ai in Education 4.0 addresses the broad potential of ai, from personalized student learning experiences to reducing administrative burdens and using this innovative technology to improve curricula.

For campuses and districts that have not yet begun their ai journey, it is critical to know that ai models are only as good as the data that goes into the tool.

To ensure data can adequately train ai to improve education-related outcomes, consider these six strategies.

1. Resolve data anomalies

Spotting outliers in your database, such as observations, events, or data points that deviate from the standard, is key to optimizing ai in your education system. Although anomalies in data do not always indicate that something is wrong, it is advisable to investigate them to be sure.

While the exact method depends on data types, distribution, and computational resources, anomaly detection can be handled using statistical, machine learning, and clustering-based methods. By detecting and resolving data inconsistencies early, technology teams are more likely to ensure accurate ai models and avoid future problems.

2. Automate data cleaning

Automated data cleansing improves accuracy and consistency by correcting or removing incorrect, corrupt, duplicate, or incomplete data within a data set. It is a fundamental step towards data management, ensuring accuracy and ensuring reliability.

This stage is vital because clean, well-prepared data prevents ai from generating distorted results and reduces the computational resources required for training models. Additionally, clean, automated data frees up edtech teams to focus on developing ai models and other valuable tasks rather than solving data bottlenecks.

3. Continually observe data quality metrics.

Identify key data quality metrics for your campus or district to regularly measure and improve data sets. Tracking these metrics involves evaluating, measuring, and managing data to determine its accuracy, consistency, completeness, reliability, and validity.

Regular audits keep technology teams informed and agile, detecting potential issues before they get out of control and negatively impact ai results.

4.Make data governance routine

Establishing data rules, roles, and uses will help ensure that data sets are clean and accurate before being leveraged for ai. This governance of data processes keeps all teams and tools at the standard necessary for successful operation.

Perform and recognize data stewardship for employees who advance your school system's data governance initiatives. Effective data governance helps decrease data inconsistencies across school systems, which improves overall data integration efforts.

5. Improve data security

Since 2005, American educational institutions have suffered 3,713 data breachesaffecting 37.6 million records. Data breaches can damage a school system's reputation and decrease trust among students, teachers, and the community.

Additionally, the lack of sufficient data security measures in ai systems could lead to non-compliance, which is often accompanied by the school system being held liable for a violation, having funding withdrawn, or being withdraw the funds. being investigated by the US Department of Education..

To avoid such problems, secure your data through encryption, access controls, firewalls, content filters, network security, endpoint segmentation, regular backups, continuous updates, and security awareness training.

6. Make sure data is standardized

Finally, data standardization helps ai models learn patterns more effectively and consistently. It is essential to preserve data quality and allows different systems to exchange data in a consistent format.

By practicing the most common standardization techniques (data cleansing, data governance, data normalization, and data transformation), educational institutions can safeguard data consistency, which is critical for training ai and machine learning models. .

A+ ai is based on stellar data

ai can help transform school systems by adapting to the learning needs of each student and personalizing their learning experience. By automating administrative tasks, educators' time is freed up for more hands-on instruction. It can also help identify strengths and weaknesses in student performance, allowing educators to prepare better-targeted instructional strategies.

If your school district is ai-ready, adopt these six data strategies to ensure your ai model is optimized for all stakeholders, especially students, teachers, and administrators.

There's no need to wait for summer vacation to make sure your data gets top marks. Get started now so your ai program will be at the top of its class next fall.