1.
What framework for a responsible use of
AI in education?
Stéphan Vincent-Lancrin, Ph.D.
Deputy Head of Division,
Senior Analyst and Project Leader
Centre for Educational Research and Innovation,
Directorate for Education and Skills
Berlin, 02 October 2020

2.
AI on the rise

3.
12.6
6.1
3.1
0.6
1.8
0.8
1.3
0.1
0
2
4
6
8
10
12
14
AR/VR AI Robotics Blockchain
2025 2018
Current and estimated expenditures in advanced
education technology
Advanced Education Technology Expenditure, 2018 and 2025 estimate, USD Billions
Source: HolonIQ, January 2019

4.
China represents over 50% of global education
venture capital investment
0.6
2.0 1.6
2.3
5.2
1.0
1.5
1.1
1.3
1.6
0.4
0.7
0.3
0.5
0
1
2
3
4
5
6
7
8
9
2014 2015 2016 2017 2018
China Unied States India European Union Others
1.8
4.2
3.2
4.4
8.2
33%
48% 50% 52%
63%
56%
36% 34% 30%
20%
3%
2
6% 9% 9%
4%
2%
3%
7% 6%
4%
12%
6%
2% 2%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2014 2015 2016 2017 2018
China Unied States India European Union Others
Venture capitalists have invested USD 8B in 2018, up from USD 2B in 2014 – mainly from China
Source: HolonIQ, January 2019

5.
a few key points

6.
• Inclusive growth, sustainable development
and well-being
• Human-centred values and fairness
• Transparency and explainability
• Robustness, security and safety
• Accountability
OECD (and G20) Principles on Artificial Intelligence

7.
• Usefulness
– Develop solutions with stakeholders (teachers, etc.) – not just EdTech
– Work with schools on the benefits of the technological solution so it gets used
• Effectiveness
– Verify that AI solutions do what they say (e.g. gives accurate diagnosis/predictions)
– Ensure it improves outcomes (e.g. supports interventions to solve the problems)
• Equity
– Privilege cheap solutions running on existing platforms (digital divide is bigger than we
thought)
– Establish standards and facilitate inter-operability
What does that mean in education?

8.
• Fairness
– Ensure that you are not replicating biases due to your historical data (e.g. machine
learning) or due to the human choices in designing the algorithm
– Ensure that you are not creating new biases (e.g. look at the results)
• Transparency
– Open data/open algorithm: allow anyone (i.e. other experts) to see and
verify/challenge/improve the algorithm
– Explain how the algorithm works and which choices were made (to the extent possible)
– Involve stakeholders to discuss the choices made when there is high stake
• Data protection
– Data protection regulation in most countries: GDPR in Europe, FERPA in the US, etc.
– Risk management policy: zero risk policy is not possible
What does it mean in education?

9.
• Questions
– Are there (possible) benefits we do not
want because of the risks of misuse of
the data – or just the solution is too
intrusive?
– Should AI give feedback, support,
diagnose – or make decisions?
– Constant monitoring/tracking
(surveillance?), bio-markers, etc.?
• Can we have creative solutions?
– Delete data as they are collected?
– Invest in plausible theories?
– Build trust
• Ethics and regulation should allow us
to do something…
Ethics or regulation?

10.
Stephan.Vincent-Lancrin@oecd.org
stephan-vincent-lancrin
THANK YOU
https://oe.cd/educationceriinnovationstrategy