The document discusses transforming education management information systems (EMIS) using artificial intelligence and data analytics. It describes moving from current EMIS, which are school-based data management systems, to integrated, dynamic learning management systems that can support real-time decision making across the education sector. This would allow for learning analytics, predictive algorithms, and responsive policies/plans based on education metrics and outcomes. The conference will address enhancing EMIS through advanced data techniques, teacher assistant tools, assessment tools, and AI-powered professional development while navigating challenges around standards, ethics, and data quality issues.

1. UNESCO EDUCATION SECTOR
International Conference on
Artificial Intelligence and Education
Data-based EMIS and education data analytics
Beijing
People’s Republic of China
16 – 18 May 2019
Transforming current
EMIS from a school-
based aggregated
administrative data
management system
an integrated and dynamic
learning management
systems that can effectively
support real-time decision
making in every aspect of
education sector management
From Into
EMIS: Education Management
Information System

2. UNESCO EDUCATION SECTOR
International Conference on
Artificial Intelligence and Education
Data-based EMIS and education data analytics
Beijing
People’s Republic of China
16 – 18 May 2019
From IntoCurrent EMIS AI-powered LMS
Performance Curricula
Financing
Professional
development
Learning
resources
International
assessment
(PISA, TIMSS)
• Learning Analytics
• Data dashboard
• Predictive decision-making algorithms
ü Formulating responsive policies and plans
ü Monitoring and evaluating education outcomes
(metrics on quality and relevance education)
ü Real-time decision making
- Organised group of information and
documentation service
- Simple metrics: enrolment, attendance,
and grade completion
- Information on access and participation
in education
Needs: data are complete, Reliable, regularly
collected and can be aggregated
and system Integration

3. UNESCO EDUCATION SECTOR 3
Gap in educational data and weak Data Quality
Gaps in educational data coverage
with respect to SDG 4 indicators
SDG4, “Ensure
inclusive and
equitable quality
education and
promote lifelong
opportunities for all”
Source: UNESCO Institute for Statistics (2018).
Concept Note:The Investment Case for SDG 4 Data

4. UNESCO EDUCATION SECTOR 4
Gap in educational data and weak Data Quality
Gathering data in areas with poor
internet connection – solution function
both offline and online
Data Accuracy – validation mechanism
implemented
How ProFuturo addresses Data Challenges
Poor infrastructure
Data from several sources
and in different formats
Best practice
Decision-making based on
data
Enhancing capacity on identifying of relevant
indicators, data reporting (Dashboard), use of
insights/indicators (Advanced Analytics) and
developing apps (Teacher Assistant)
Data integration and consistency
Fixing data redundancies
Data-driven organisation

5. UNESCO EDUCATION SECTOR 5
Learning analytics and AI to enhance teaching and learning
AI tutoring system
to personalise
education
Advanced Data analytics techniques
(visual analytics, Dimension reduction- Principal
component analysis (PCA), Chi square..)
Teacher assistant
dual teacher mode
(teacher-virtual
assistant)
AI assessment tools
Computational Thinking
as a key competency for
learners
AI- human interaction
AI-powered
Professional
development
Main real applications Challenges – Topics to address
Standards
AI Ethics, Privacy and
transparency
Open source Data Hub

6. UNESCO EDUCATION SECTOR 6
Thank you
Paula Valverde
Head of Product and Innovation. Telefonica
@paula_valver