翻译：
How to promote the sustainable development of “AI + education” at the level of sharing results?
Optimize the allocation of educational resources, break the technology gap and algorithm black box. Although the rapid development of artificial intelligence technology has brought unprecedented changes and opportunities to the field of education, the technology gap between different regions, schools and educators also highlights the differences in the application level of artificial intelligence. In order to ensure the sharing of the fruits of the development of “AI + education”, it is necessary to optimize resource allocation with policy guidance and break the technology gap.
1. Optimizing the allocation of educational resources through policy guidance is the key to overcome the technology gap.
The government should play a guiding role in policy and pay attention not only to the total input of educational resources, but also to the reasonable allocation of resources among different regions and schools. By optimizing the financial budget, setting up special funds and other measures to guide educational resources to remote areas, schools with poor economic conditions and special education fields, and constantly improve their technical infrastructure conditions, so as to effectively narrow the educational gap between regions and schools and avoid the technical gap caused by the uneven distribution of resources. To provide a better educational environment for all students.
2. The popularization of artificial intelligence education is also one of the important ways to overcome the “technology gap”.
By opening AI-related courses, holding popular science lectures, providing practice platforms and other ways to popularize artificial intelligence education, we can provide a common starting point for all students, thereby narrowing the technology gap between different student groups and eliminating the difference in resource sharing caused by technology differences.
3. Promoting the transparency of algorithmic decision making is another important measure to promote resource sharing.
In the “artificial intelligence + education” scenario, breaking the “algorithm black box”, correcting the algorithm bias, promoting the transparency of algorithm decision-making, and helping to promote fairness. By introducing an independent third-party institution to conduct regular audits of the algorithm to ensure the fairness and transparency of the algorithm’s decisions, information asymmetry can be eliminated and trust in the education system can be enhanced.
