The Ethics of EdTech · Another EdTech example: Diversity is a challenge, algorithms can support teachers 16 Today Big data allows addressing individual students, even in large groups

The Ethics of EdTech

How AI influences education

and why coding becomes politics

Dr. Jörg Dräger | @joergdraeger

AI in education: People talk about tech hype, disruption and money

The Ethics of EdTech 2

But it is a bit more complicated: A think tank’s view of coding becoming political and ethics of EdTech important

3

MASSIFICATION: MORE ACCESS – FOR THE GIFTED & MOTIVATED

PERSONALIZATION: INDIVIDUALIZED LEARNING FOR ALL

GAMIFICATION: OVERCOMING MOTIVATIONAL BARRIERS

INTERACTION: WeQ – THE POWER OF PEER-TO-PEER

ORIENTATION: GUIDANCE THROUGH THE JUNGLE OF OPPORTUNITIES

JOB MATCHING: MAKING EDUCATION MORE VALUABLE

„Systemic“ view „Political“ view


As a start

4

Technology is not an end in itself. We need technology to deal with three key challenges


University students worldwide

67 mn

198 mn

UN

ES

CO

Institu

te f

or

Sta

tistics

(2016);

Cente

r fo

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igher

Education G

erm

any

(2015)

x 3in 20 yrs

x 3in 20 yrs

Challenge 1: Education has become a “mass business”

5The Ethics of EdTech

Mic

hael

Fulle

n,

Str

ato

sph

ere

(2013)

Amount of atypical US students(older, part-time, off-campus)

75% 19.212

Number of degree programs in Germany(January 2018)

Challenge 2: The diversity is growing rapidly


HR

K H

ochschulk

om

pass 2

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U.S

. D

epart

ment

of

Education,

Dig

est

of

Education S

tatistics

and N

ational

Assessm

ent

of

Education

al

Pro

gre

ss (

2013).

Cost of growth in education (schematic)

Number of studentsNumber of studentsNumber of studentsNumber of students

Re

sso

urc

es

Re

sso

urc

es

Re

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urc

es

Re

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CCCCostsostsostsosts

Cost per pupil (US, inflation adjusted)

80%

120%

160%

200%

240%

1971 1975 1980 1984 1988 1990 1992 1994 1996 1999 2004 2008

CostCostCostCost

Reading skillsReading skillsReading skillsReading skills

+129%

± 0%

Dealing with mass Dealing with diversity

Challenge 3: Skills are stagnating, cost is exploding


MASSIFICATION: MORE ACCESS – FOR THE GIFTED & MOTIVATED

PERSONALIZATION: INDIVIDUALIZED LEARNING FOR ALL

GAMIFICATION: OVERCOMING MOTIVATIONAL BARRIERS

INTERACTION: WeQ – THE POWER OF PEER-TO-PEER

ORIENTATION: GUIDANCE THROUGH THE JUNGLE OF OPPORTUNITIES

JOB MATCHING: MAKING EDUCATION MORE VALUABLE

Technology helps us solve these challenges …


… and (AI) algorithms are the drivers of these change agents

9

Given the mass of learners,

we need “intelligent” algorithmic

decision making

to personalize the different forms

of learning, orientation, matching


PERSONALIZATION

GAMIFICATION

ORIENTATION

JOB MATCHING

PEER-2-PEER

MASSIFICATION

And now to the “political” view – looking at examples

10

Algorithmic decision making can do wonders –and can go terribly wrong


A warm up: Why a simple algorithms becomes political

11

Detour 1: +30 Minuten

Detour 2: +60 Minuten

Not everybody can take the short detour -But who decides? And according to which criteria?

Not everybody can take the short detour -But who decides? And according to which criteria?


Back to EdTech: NYC has a mass problem. Every year pupils need to find their “perfect” high school

12

No preference school

41%

„Analog Times“ (until 2003)

77,000 8th graders 426 high schools

New York Public School System


Ho

w G

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Ne

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Hig

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Ap

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Pro

cess

.

Tu

llis,

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cy.

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ew

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rk T

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s: 0

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Today an algorithmic matching system makes for happier students and parents (even though it doesn’t fix all equity issues)

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w G

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ory

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ork

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Hig

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Ap

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cess

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Tu

llis,

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cy.

Th

e N

ew

Yo

rk T

ime

s: 0

5.1

2.2

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4


Parental Satisfaction

96%

„Digital Age“New York Public School System

Preference list

1) 7)

2) 8)

3) 9)

4) 10)

5) 11)

6) 12)

Algorithmic matching

But: France abandoned its algorithmic allocation system for university students due to bias and lack of transparency


740,000 high school grads

11,000 post schooled options

French Universities

Hill

er

V.

an

d T

erc

ieu

xO

. (2

01

4):

“C

ho

ix d

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n F

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un

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Lessons learned: Matching algorithms can be highly political

15

Without transparency, algorithmic decision making systems lack public support and are doomed to fail


Another EdTech example: Diversity is a challenge, algorithms can support teachers

16

Today

Big data allows addressing

individual students, even in large groups

Past

Teachers taught to

the average student(„middle head“)


Algorithms can help students find the right course of study …


+61%4-year

graduationrate

Algorithms(+ Advisory Network)Arizona State University

80,000 studentsThousands of

courses to choose

Recommendation:

Agriculture 203

Recommendation:

Psychology 101

Recommendation:

Theoretical Physics

444

AS

U A

nn

ua

l R

ep

ort

20

14

… and alert their support network when they encounterdifficulties


Georgia State University

32,000 students800 algorithmicallyinitiated alerts daily

Academics Finances

Interests Health

Campus life Supports

+68%graduation

rate

Algorithms(+ Advisory Network)

GS

U 2

01

5 S

tatu

s R

ep

ort

/ C

om

ple

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olle

ge

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org

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p.2

But: algorithms are also used to exclude potential valedictorians


Best students excluded …

… due to low admitted

students acceptance rate

We

ap

on

so

fM

ath

De

stru

ctio

n,

Ca

thy

O‘N

eil

(20

16

), p

.55

-57

Not-to-be-blamedUniversity

College Ranking

105

108

113

360

Goal: improve college ranking

Lessons learned: Use or abuse

20

Without a clear purpose and proper ethical norms, good algorithms can go wrong



SOCIETY: Algorithmic competence for all

EDUCATIONAL LANDSCAPE: Dare the public discourse

INSTITUTIONS: Think about processes, not tools

CLASSROOMS: Overcome the locality problem

WE as educators, investors and techies are responsible forthe ethics of EdTech and for leading the public debate

„So how do we leverage the potential of AI in education and avoid failure?“

Classrooms: Education is local, expensive technology needs to be global. Stop reinventing the wheel in EdTech


• Make EdTech the norm in

every classroom• Invest in systems rather than in

institutions

• Join forces, e.g. with thegaming industry

Scale upLocality problem

Market share of typical education

institution:0,X %

Market share of typical digital

application:X0 %

Institutions: Even if it is more complex – think aboutprocesses, not just about tools


• Software tools as part of

institutional processes• Complex analysis and

implementation

• Fit to institutional culture, needfor participation and traning of

faculty• Harder to scale …

Re-design processes

One piece of software will

personalize learning

One AI algorithm will orient students’

learning path

Not all dreams come true

Educational landscape: Whether you want it or not – EdTechis politics. Be proactive, make EdTech political


?

If you ever see a black box …

• Establish rules and regulations

– and make them binding• Lead the public debate, be

open about ADM purposes and

standards• Push for transparency, allow for

accountability

… think about France (or the InBloom case in the US)

Society: Universities should help making everybodyalgorithmically competent


Techies should just code

The rest of us doesn’t need to understand

algorithmic systems

Split labor doesn‘t make sense

• Do research: biases of algorithmicsystems, …

• Train techies in ethics, develop a code of ethics of AI

• Train students in all fields in

algorithmic thinking and use ofADM systems

• Consult politicians on the use and

danger of AI systems – in education and beyond

Higher Ed has a job to do

Summary: EdTech drives systemic disruption. This makes ita political issue

MASSIFICATION

PERSONALIZATION

GAMIFICATION

INTERACTION

ORIENTATION

JOB MATCHING

Mass

Diversity

Cost $$$$$$

$$$$$$


Actively adress critical issuesof AI / EdTech applications

Challenges in education

Systemic disruption through AI with six effects

Political implications

It is our job to proactively lead the debate and make AI / EdTech a success

27

„We need to make coding political.

We need to push for an open debateabout ethical norms, transparency andaccountability – and for adequate

self-regulation.

We need to rethink & readjust the

relationship of man and machine – andmake society at large algorithmicallycompetent“



Dr. Jörg Dräger | @joergdraeger

Documents

The Ethics of EdTech · Another EdTech example: Diversity is a challenge, algorithms can support teachers 16 Today Big data allows addressing individual students, even in large groups