Emerging innovations for using big data and artificial intelligence to super-charge education programs

• In Event: How can education leaders leverage big data and AI when scaling?

Proposal

The revolution in big data and AI provides multiple opportunities for super-charging impact at scale. We will present examples of how IDinsight uses big data and AI to help organizations at scale increase their reach, strengthen implementation efficiency, and improve cost-effectiveness. Beyond the Educate Girls example, others include:

Using machine-learning for smart targeting: As organizations scale, they aim to reach as many eligible recipients of their program as possible. However, many organizations work with marginalized and vulnerable populations that are invisible in official government records. Machine learning can help organizations predict where marginalized populations are found so that they can more effectively target service provision.
1. Indus Action: Large institutions, especially governments, often rely on eligible individuals to sign-up to receive program benefits. We are working with Indus Action to build algorithms that use data to predict the probability that a citizen is eligible for certain government benefits. These algorithms form the basis for smart outreach campaigns that channel resources to where they would be most likely to reach eligible households.
2. Satellite imagery: Smart targeting models, like those used by Educate Girls and Indus Action, typically rely on existing programmatic and administrative data to form predictions. However, in many environments this data does not exist, is out-of-date, or is of questionable quality. In such cases, we envision the potential for using satellite imagery to predict outcomes and pinpoint marginalized populations.

Using AI-powered tools to strengthen implementation processes: While machine-learning is increasingly used to determine how and where organizations scale, recent innovations have created opportunities for organizations to use AI to strengthen their internal operations and implementation processes at scale.
1. EDOBest: Large institutions must decide how to allocate resources efficiently across thousands/millions of program communities and recipients. Given the size of programs at scale, resource allocation is a computationally-intensive task that is typically done manually, requiring large amounts of staff time and leads to inefficient allocations. We are working with Ministry of Education of Edo State in Nigeria to use algorithms to optimally assign cohorts of 1,000+ teachers to schools across the state. These algorithms enable the ministry to complete teacher assignment in a fraction of the time, are easily adapted to new constraints and reusable in future years.
2. Triaging support to program recipients using AI: Organizations often use help-desks to triage requests for support from program recipients. As organizations expand operations, help-desks can become inundated with requests, leading to delays and in some cases limiting the potential scale of programs. We have built a tool called Ask-A-Question to reduce the number of requests to help-desks so that they can focus on the most complicated, highest-priority needs.
3. Increasing access to data for decision-making: Organizations at scale generate large amounts of data, but often struggle with using that data to inform decision-making in real-time. To help organizations better access and use internal data, we built Ask-A-Metric, which allows authorized users to query their M&E database in natural language, over WhatsApp. We are piloting this tool with four organizations.

Author

• Ashwini Maslekar, Educate Girls