Key points:
It’s never too early for campus or district-wide IT teams to begin planning for upcoming tech upgrades and implementations. Because many of these upgrades happen over summer break, teams can use the upcoming spring semester to ensure their data is in A+ shape to support new AI tools.
AI has already significantly impacted education by improving how students learn, teachers teach, and educational institutions operate. The World Economic Forum’s Shaping the Future of Learning: The Role of AI in Education 4.0 touches upon AI’s extensive potential, from tailored student learning experiences to reducing administrative burdens to using this innovative technology to improve curricula.
For campuses and districts that haven’t started their AI journeys, it’s 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. Solve for data anomalies
Detecting outliers in your data baseline–like observations, events, or data points that deviate from the standard–is key to optimizing AI in your educational system. Although data anomalies don’t always indicate something’s amiss, it’s wise to investigate them to be sure.
While the exact method depends on data types, distribution, and computational resources, anomaly detection can be handled through statistical, machine learning, and clustering-based methods. By detecting and solving data inconsistencies early, tech teams are more likely to ensure accurate AI models and avoid future problems.
2. Automate data cleansing
Automated data cleansing enhances accuracy and consistency by fixing or removing incorrect, corrupted, duplicate, or incomplete data within a dataset. It’s a critical step toward managing data, ensuring accuracy, and warranting trustworthiness.
This stage is vital because clean, well-prepared data prevents AI from generating distorted results and reduces the computational resources needed by training models. Further, clean, automated data frees educational tech teams to concentrate on developing AI models and other valuable tasks instead of fixing data obstacles.
3. Observe data quality metrics continuously
Identify your campus or district’s key data quality metrics to measure and improve datasets regularly. Monitoring these metrics involves assessing, measuring, and managing data for accuracy, consistency, completeness, reliability, and validity.
Regular audits allow tech teams to be informed and agile, detecting potential problems before they get out of control and negatively affect AI outcomes.
4. Make data governance routine
Setting the rules, roles, and uses of data will help ensure that datasets are clean and accurate before being leveraged for AI. This governance of data processes upholds all teams and tools to the standard needed for successful operation.
Perform and recognize data stewardship for employees promoting your school system’s data governance initiatives. Effective data governance helps decrease data inconsistencies in school-wide systems, which improves overall data integration initiatives.
5. Enhance data security
Since 2005, U.S. educational institutions have undergone 3,713 data breaches, affecting 37.6 million records. Data breaches can damage a school system’s reputation and decrease trust among students, faculty, and the community.
Further, a lack of sufficient data security measures in AI systems could lead to non-compliance, which is often accompanied by a school system being held liable for a breach, having funding withdrawn, or being investigated by the U.S. Department of Education.
To avoid such troubles, secure your data through encryption, access controls, firewalls, content filters, network security, endpoint segmentation, regular backups, continuous updates, and security awareness training.
6. Ensure data is standardized
Finally, data standardization helps AI models learn patterns more effectively and consistently. It is essential for preserving data quality and allows different systems to exchange data in a consistent format.
By practicing the most common standardization techniques–data cleaning, 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 relies on stellar data
AI can help transform school systems by adapting to each student’s learning needs and personalizing their learning experience. By automating clerical tasks, educators’ time becomes free for more hands-on instruction. It can also help to identify strengths and weaknesses in student performance, allowing educators to prepare better-targeted instructional strategies.
If your school district is ready for AI, adopt these six data strategies to ensure that your AI model is optimized for all stakeholders, especially students, teachers, and administrators.
There’s no need to wait for summer break to ensure your data gets a top grade. Start now so your AI program is at the head of its class next fall.
