Artificial IntelligenceIndex Report 2021

CHAPTER 4:

AI Education

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CHAPTER 4:A I E D U CAT I O N

CHAPTER 4:

Chapter Preview
Overview

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New CS PhDs in the United States

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Chapter Highlights

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New CS PhDs by Specialty

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New CS PhDs with AI/ML andRobotics/Vision Specialties

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4.1 STATE OF AI EDUCATION IN HIGHEREDUCATION INSTITUTIONS
5
Undergraduate AI Course Offerings

5

Undergraduate Courses
That Teach AI Skills

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Intro-Level AI and ML Courses

5

Graduate AI Course Offerings

7

Graduate Courses That Focus
on AI Skills

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Faculty Who Focus on AI Research

7

4.2 AI AND CS DEGREE GRADUATESIN NORTH AMERICA

8CS Undergraduate Graduates
in North America

8

New AI PhDs Employment
in North America

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Industry vs. Academia

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New International AI PhDs

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4.3 AI EDUCATION IN THE
EUROPEAN UNION AND BEYOND

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AI Offerings in EU27

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By Content Taught in
AI-Related Courses

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International Comparison

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HIGHLIGHT: AI BRAIN DRAIN
AND FACULTY DEPARTURE

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APPENDIX19

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CHAPTER 4:A I E D U CAT I O NOV E R V I E W

Overview
As AI has become a more significant driver of economic activity, there has been
increased interest from people who want to understand it and gain the necessary
qualifications to work in the field. At the same time, rising AI demands fromindustry are tempting more professors to leave academia for the private sector.
This chapter focuses on trends in the skills and training of AI talent through various
education platforms and institutions.
What follows is an examination of data from an AI Index survey on the state of AI
education in higher education institutions, along with a discussion on computerscience (CS) undergraduate graduates and PhD graduates who specialized in
AI-related disciplines, based on the annual Computing Research Association(CRA) Taulbee Survey. The final section explores trends in AI education in Europe,
drawing on statistics from the Joint Research Centre (JRC) at the European
Commission.

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CHAPTERHIGHLIGHTS

CHAPTER HIGHLIGHTS• An AI Index survey conducted in 2020 suggests that the world’s top universities haveincreased their investment in AI education over the past four years. The number of coursesthat teach students the skills necessary to build or deploy a practical AI model on theundergraduate and graduate levels has increased by 102.9% and 41.7%, respectively, in thelast four academic years.
• More AI PhD graduates in North America chose to work in industry in the past 10 years,
while fewer opted for jobs in academia, according to an annual survey from the Computing
Research Association (CRA). The share of new AI PhDs who chose industry jobs increased by
48% in the past decade, from 44.4% in 2010 to 65.7% in 2019. By contrast, the share of new AI
PhDs entering academia dropped by 44%, from 42.1% in 2010 to 23.7% in 2019.
• In the last 10 years, AI-related PhDs have gone from 14.2% of the total of CS PhDs granted in
the United States, to around 23% as of 2019, according to the CRA survey. At the same time,other previously popular CS PhDs have declined in popularity, including networking, software
engineering, and programming languages. Compilers all saw a reduction in PhDs granted
relative to 2010, while AI and Robotics/Vision specializations saw a substantial increase.
• After a two-year increase, the number of AI faculty departures from universities to industry
jobs in North America dropped from 42 in 2018 to 33 in 2019 (28 of these are tenured faculty
and five are untenured). Carnegie Mellon University had the largest number of AI faculty
departures between 2004 and 2019 (16), followed by the Georgia Institute of Technology (14)and University of Washington (12).
• The percentage of international students among new AI PhDs in North America continued torise in 2019, to 64.3%—a 4.3% increase from 2018. Among foreign graduates, 81.8% stayed in
the United States and 8.6% have taken jobs outside the United States.
• In the European Union, the vast majority of specialized AI academic offerings are taught at
the master’s level; robotics and automation is by far the most frequently taught course in thespecialized bachelor’s and master’s programs, while machine learning (ML) dominates in thespecialized short courses.

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4 .1 S TAT E O F A I E D U CAT I O N
I N H I G H E R E D U CAT I O N
INSTITUTIONS

4.1 STATE OF AI EDUCATION IN
HIGHER EDUCATION INSTITUTIONSNUMBER of UNDERGRADUATE COURSES THAT TEACH STUDENTS
the SKILLS NECESSARY to BUILD or DEPLOY a PRACTICAL AI
MODEL, AY 2016-20
Source: AI Index, 2020 | Chart: 2021 AI Index Report

200

Number of Courses

In 2020, AI Index developed a survey that
asked computer science departments or
schools of computing and informatics
at top-ranking universities aroundthe world and in emerging economies
about four aspects of their AI education:
undergraduate program offerings, graduate
program offerings, offerings on AI ethics,
and faculty expertise and diversity. The
survey was completed by 18 universities
from nine countries.1 Results from the AI
Index survey indicate that universities haveincreased both the number of AI courses
they offer that teach students how to buildand deploy a practical AI model and thenumber of AI-focused faculty.

150

100

50

0

U N D E R G R A D UAT E A I
COURSE OFFERINGS
Course offerings at the undergraduatelevel were examined by evaluating trends
in courses that teach students the skills
necessary to build or deploy a practical AI
model, intro-level AI and ML courses, andenrollment statistics.

Undergraduate Courses That
Teach AI Skills
The survey results suggest that CS
departments have invested heavily inpractical AI courses in the past four
academic years (AY).2 The number of

2016-172017-18

2018-192019-20
Figure 4.1.1

courses on offer that teach students the skills necessary to build or
deploy a practical AI model has increased by 102.9%, from 102 in AY
2016–17 to 207 in AY 2019–20, across 18 universities (Figure 4.1.1).

Intro-Level AI and ML Courses
The data shows that the number of students who enrolled in or
attempted to enroll in an Introduction to Artificial Intelligencecourse and Introduction to Machine Learning course has jumped byalmost 60% in the past four academic years (Figure 4.1.2).3
The slight drop in enrollment in the intro-level AI and ML courses
in AY 2019–20 is mostly driven by the decrease in the number ofcourse offerings at U.S. universities. Intro-level course enrollment

1 The survey was distributed to 73 universities online over three waves from November 2020 to January 2021 and completed by 18 universities, a 24.7% response rate. The 18 universities are—Belgium:
Katholieke Universiteit Leuven; Canada: McGill University; China: Shanghai Jiao Tong University, Tsinghua University; Germany: Ludwig Maximilian University of Munich, Technical University of Munich;Russia: Higher School of Economics, Moscow Institute of Physics and Technology; Switzerland: École Polytechnique Fédérale de Lausanne; United Kingdom: University of Cambridge; United States:
California Institute of Technology, Carnegie Mellon University (Department of Machine Learning), Columbia University, Harvard University, Stanford University, University of Wisconsin–Madison,
University of Texas at Austin, Yale University.
2 See here for a list of keywords on practical artificial intelligence models provided to the survey respondents. A course is defined as a set of classes that require a minimum of 2.5 class hours (including
lecture, lab, TA hours, etc.) per week for at least 10 weeks in total. Multiple courses with the same titles and numbers count as one course.
3 For universities that have a cap on course registration, the number of students who attempted to enroll in the intro-level AI and ML courses are included.

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NUMBER of STUDENTS WHO ENROLLED or ATTEMPTED to ENROLLin INTRO to AI and INTRO to ML COURSES, AY 2016-20
Source: AI Index, 2020 | Chart: 2021 AI Index Report

10,0008,000
Number of Students

in the European Union has gradually
increased by 165% in the past four
academic years, while such enrollment
in the United States has seen a clear
dip in growth in the last academic
year (Figure 4.1.3). Six of the eight
U.S. universities surveyed say that the
number of (attempted) enrollments forthe introductory AI and ML courses has
decreased within the last year. Some
universities cited students taking leaves
during the pandemic as the main causeof the drop; others mentioned structuralchanges in intro-level AI course offerings—
such as creating Intro to Data Science lastyear—that may have driven students awayfrom traditional intro to AI and ML courses.

4 .1 S TAT E O F A I E D U CAT I O N
I N H I G H E R E D U CAT I O N
INSTITUTIONSCHAPTER 4:A I E D U CAT I O N

6,000

4,000

2,000

0
2016-172017-18

2018-192019-20
Figure 4.1.2

PERCENTAGE CHANGE in the NUMBER of STUDENTS WHO ENROLLED or ATTEMPTED to ENROLL in INTRO to AI and
INTRO to ML COURSES by GEOGRAPHIC AREA, AY 2016-20
Source: AI Index, 2020 | Chart: 2021 AI Index Report165.1%, EU

Percentage Change (2016-17=1)

150%

100%

50%

21.0%, US0%
2016-172017-18

2018-192019-20
Figure 4.1.3

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4 .1 S TAT E O F A I E D U CAT I O N
I N H I G H E R E D U CAT I O N
INSTITUTIONSCHAPTER 4:A I E D U CAT I O N

G R A D UAT E A I C O U R S E
OFFERINGSNUMBER of GRADUATE COURSES THAT TEACH STUDENTS the
SKILLS NECESSARY to BUILD or DEPLOY a PRACTICAL AI MODEL,AY 2016-20

The survey also looks at course offeringsat the graduate or advanced degree level,
specifically at graduate courses that teachstudents the skills necessary to build or
deploy a practical AI model.4

Source: AI Index, 2020 | Chart: 2021 AI Index Report

Graduate offerings that teach students
the skills required to build or deploy apractical AI model increased by 41.7% in
the last four academic years, from 151
courses in AY 2016–17 to 214 in AY 2019–20(Figure 4.1.4).

Number of Courses

Graduate Courses That Focus
on AI Skills

200

150

100

50

0

FAC U LT Y W H O F O C U S O N
AI RESEARCH

2017-18

2018-192019-20
Figure 4.1.4

NUMBER of TENURE-TRACK FACULTY WHO PRIMARILY FOCUS
THEIR RESEARCH on AI, AY 2016-20
Source: AI Index, 2020 | Chart: 2021 AI Index Report

150

Number of Faculty

As shown in Figure 4.1.5, the numberof tenure-track faculty with a primaryresearch focus on AI at the surveyed
universities grew significantly over the
past four academic years, in keepingwith the rising demand for AI classes anddegree programs. The number of AIfocused faculty grew by 59.1%, from 105 in
AY 2016–17 to 167 in AY 2019–20.

2016-17100

50

0
2016-172017-18

2018-192019-20
Figure 4.1.5

4 See here for a list of keywords on practical artificial intelligence models provided to the survey respondents. A course is defined as a set of classes that require a minimum of 2.5 class hours (including
lecture, lab, TA hours, etc.) per week for at least 10 weeks in total. Multiple courses with the same titles and numbers count as one course.

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4.2 AI AND CS DEGREE
G R A D UAT E S I N N O R T H
A M E R I CA

CHAPTER 4:A I E D U CAT I O NThis section presents findings from the annual Taulbee Survey from the Computing Research Association (CRA). The annual
CRA survey documents trends in student enrollment, degree production, employment of graduates, and faculty salaries inacademic units in the United States and Canada that grant doctoral degrees in computer science (CS), computer engineering (CE), or information (I). Academic units include departments of computer science and computer engineering or, in somecases, colleges or schools of information or computing.

4.2 AI AND CS DEGREE GRADUATESIN NORTH AMERICAC S U N D E R G R A D UAT E G R A D UAT E S
I N N O R T H A M E R I CA

NEW CS PHDS IN THE
U N I T E D S TAT E S

Most AI-related courses in North America are a part of
the CS course offerings at the undergraduate level. The
number of new CS undergraduate graduates at doctoral
institutions in North America has grown steadily in the last
10 years (Figure 4.2.1). More than 28,000 undergraduatescompleted CS degrees in 2019, around three times higher
than the number in 2010.

The section examines the trend of CS PhD graduates in
the United States with a focus on those with AI-related
specialties.5 The CRA survey includes 20 specialties intotal, two of which are directly related to the field of AI,
including “artificial intelligence/machine learning” and“robotics/vision.”

NUMBER of NEW CS UNDERGRADUATE GRADUATES at DOCTORAL INSTITUTIONS in NORTH AMERICA, 2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

Number of New CS Undergraduate Graduates

30,00025,00020,00015,00010,000

5,000

0
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019
Figure 4.2.1

5 New CS PhDs in this section include PhD graduates from academic units (departments, colleges, or schools within universities) of computer science in the United States.

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4.2 AI AND CS DEGREE
G R A D UAT E S I N N O R T H
A M E R I CA

N E W C S P H D S BY S P E C I A LT Y
Among all computer science PhD graduates in 2019, those
who specialized in artificial intelligence/machine learning(22.8%), theory and algorithms (8.0%), and robotics/
vision (7.3%) top the list (Figure 4.2.2). The AI/ML specialty
has been the most popular in the past decade, and thenumber of AI/ML graduates in 2019 is higher than thenumber of the next five specialties combined. Moreover,robotics/vision jumped from the eighth most popularspecialization in 2018 to the third in 2019.

Over the past 10 years, AI/ML and robotics/vision are the
CS PhD specializations that exhibit the most significant
growth, relative to 18 other specializations (Figure 4.2.3).
The percentage of AI/ML-specialized CS PhD graduates
among all new CS PhDs in 2020 is 8.6 percentage points(pp) larger than in 2010, followed by robotics/visionspecialized doctorates at 2.4 pp. By contrast, the shareof CS PhDs specializing in networks (-4.8 pp), software
engineering (-3.6 pp), and programming languages/
compilers (-3.0 pp) experienced negative growth in 2020.

NEW CS PHDS (% of TOTAL) in the UNITED STATES by SPECIALITY, 2019
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

Artificial Intelligence/Machine Learning
Theory and Algorithms
Robotics/VisionDatabases/Information Retrieval
Security/Information Assurance
Graphics/Visualization
Networks
Other
Software Engineering
Human-Computer Interaction
Operating Systems
Informatics: Biomedical/Other Science
Hardware/ArchitectureHigh Performance Computing
Programming Languages/Compilers
Social Computing/Social Informatics/CSCW
Computing Education
Scientific/Numerical Computing
Information Systems
Information Science
0%

5%

10%

15%

20%

% of New CS PhDs
Figure 4.2.2

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A M E R I CA

CHAPTER 4:A I E D U CAT I O NPERCENTAGE POINT CHANGE in NEW CS PHDS in the UNITED STATES from 2010 to 2019 by SPECIALTY
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

Artificial Intelligence/Machine Learning
Robotics/Vision
Human-Computer Interaction
Security/Information Assurance
Computing Education
Databases/Information Retrieval
High Performance Computing
Theory and Algorithms
Information Science
Social Computing/Social Informatics/CSCW
Information Systems
Operating Systems
Graphics/Visualization
Other
Hardware/Architecture
Informatics: Biomedical/Other Science
Scientific/Numerical Computing
Programming Languages/Compilers
Software Engineering
Networks
-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

Percentage Point Change in New CS PhDs
Figure 4.2.3

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4.2 AI AND CS DEGREE
G R A D UAT E S I N N O R T H
A M E R I CA

N E W C S P H D S W I T H A I / M L A N D R O B OT I C S/ V I S I O N S P E C I A LT I E S
Figure 4.2.4a and Figure 4.2.4b take a closer look at the number of recent AI PhDs specializing in AI/ML or robotics/
vision in the United States. Between 2010 and 2019, the number of AI/ML-focused graduates grew by 77%, while thepercentage of these new PhDs among all CS PhD graduates increased by 61%. The number of both AI/ML and robotics/
vision PhD graduates reached an all-time high in 2019.

NEW CS PHDS with AI/ML and ROBOTICS/VISIONSPECIALTY in the UNITED STATES, 2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

NEW CS PHDS (% of TOTAL) with AI/ML and
ROBOTICS/VISION SPECIALTY in the UNITED STATES,
2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

300

30%

New CS PhDs (% of Total)

Number of Graduates

22.8%

200

100

.

10%

7.3%

0%

0
2..

20%

2010 2011 2012 2013 2014 2015 2016 2017 2018 20192010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Number of Artificial Intelligence/Machine Learning New PhDs

Artificial Intelligence/Machine Learning Specialty (% of New CS PhDs)

Number of Robotics/Vision New PhDs

Robotics/Vision Specialty (% of New CS PhDs)

Figure 4.2.4a

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4.2 AI AND CS DEGREE
G R A D UAT E S I N N O R T H
A M E R I CA

N E W A I P H D S E M P LOY M E N T I N N O R T H A M E R I CA
Where do new AI PhD graduates choose to work? This section captures the employment trends of new AI PhDs in
academia and industry across North America.6

Industry vs. Academia
In the past 10 years, the number of new AI PhD graduates in North America who chose industry jobs continues to grow, as
its share increased by 48%, from 44.4% in 2010 to 65.7% in 2019 (Figure 4.2.5a and Figure 4.2.5b). By contrast, the shareof new AI PhDs entering academia dropped by 44%, from 42.1% in 2010 to 23.7% in 2019. As is clear from Figure 4.2.5b,these changes are largely a reflection of the fact that the number of PhD graduates entering academia has remainedroughly level through the decade, while the large increase in PhD output is primarily being absorbed by the industry.

EMPLOYMENT of NEW AI PHDS to ACADEMIA or
INDUSTRY in NORTH AMERICA, 2010-19

EMPLOYMENT of NEW AI PHDS (% of TOTAL) to
ACADEMIA or INDUSTRY in NORTH AMERICA, 2010-19

Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report
65.7%

New AI PhDs (% of Total)

Number of New AI PhDs

60%
150

100

50

N.

0

40%

23.7%

20%

0%
2010 2011 2012 2013 2014 2015 2016 2017 2018 20192010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Number of New AI PhDs to Academia

New AI PhDs to Academia (% of Total New AI PhDs)

Number of New AI PhDs to Industry

New AI PhDs to Industry (% of Total New AI PhDs)

100

Figure 4.2.5a

Figure 4.2.5b

6 New AI PhDs in this section include PhD graduates who specialize in artificial intelligence from academic units (departments, colleges, or schools within universities) of computer science, computerengineering, and information in the United States and Canada.

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4.2 AI AND CS DEGREE
G R A D UAT E S I N N O R T H
A M E R I CA

NEW INTERNATIONAL AI PHDS (% of TOTAL NEW AI PHDS) in NORTH AMERICA, 2010-19
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

New International AI PhDs (% of Total New AI PhDs)

70%
64.3%

60%

50%

40%

30%

20%
2010

2011

INTERNATIONALNEW AI(% of TOTAL)inSTATES
2012
2013
2014
2015PHDS20162017
2018the UNITED
2019
by LOCATION OF EMPLOYMENT, 2019
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

N E W I N T E R N AT I O N A L
AI PHDS
The percentage of international students
among new AI PhD graduates in North
America continued to rise in 2019, to
64.3%—a 4.3 percentage point increase from2018 (Figure 4.2.6). For comparison, of all
PhDs with a known specialty area, 63.4% ofcomputer engineering, 59.6% of computerscience, and 29.5% of information recipientsare international students in 2019.
Moreover, among foreign AI PhD graduatesin 2019 in the United States specifically,
81.8% stayed in the United States for
employment and 8.6% have taken jobs
outside the United States (Figure 4.2.7).
In comparison, among all international
student graduates with known specialties,
77.9% have stayed in the United States
while 10.4% were employed elsewhere.

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Figure 4.2.6

INTERNATIONAL NEW AI PHDS (% of TOTAL) in the UNITED STATESby LOCATION OF EMPLOYMENT, 2019
Source: CRA Taulbee Survey, 2020 | Chart: 2021 AI Index Report

9.6%
Unknown

9.6%
Unknown

8.6%
Outside the United States
8.6%
Outside the United States

81.8%
United States

81.8%
United States

Figure 4.2.7

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4 . 3 A I E D U CAT I O N I N
THE EUROPEAN UNIONA N D B E YO N D

CHAPTER 4:A I E D U CAT I O NThis section presents research from the Joint Research Center at the European Commission that assessed the academic offeringsof advanced digital skills in 27 European Union member states as well as six other countries: the United Kingdom, Norway,
Switzerland, Canada, the United States, and Australia. This was the second such study,7 and the 2020 version addressed four
technological domains: artificial intelligence (AI), high performance computing (HPC), cybersecurity (CS), and data science (DS),applying text-mining and machine-learning techniques to extract content related to study programs addressing the specificdomains. See the reports “Academic Offer of Advanced Digital Skills in 2019–20. International Comparison” and “Estimation of
Supply and Demand of Tertiary Education Places in Advanced Digital Profiles in the EU,” for more detail.

4.3 AI EDUCATION IN THE
EUROPEAN UNION AND BEYOND
A I O F F E R I N G S I N E U 27
The study revealed a total number of 1,032 AI programs
across program scopes and program levels in the 27 EU
countries (Figure 4.3.1). The overwhelming majority of
specialized AI academic offerings in the EU are taught at
the master’s level, which leads to a degree that equips

students with strong competencies for the workforce.
Germany leads the other member nations in offering
the most specialized AI programs, followed by the
Netherlands, France, and Sweden. France tops the list inoffering the most AI programs at the master’s level.

NUMBER of SPECIALIZED AI PROGRAMS in EU27, 2019-20
Source: Joint Research Centre, European Commission, 2020 | Chart: 2021 AI Index Report

Number of Specialized AI Programs

Master30

Bachelor
Short Courses

20

10

Bulgaria

Luxembourg

Croatia

Malta

Slovenia

GreeceSlovakia

Cyprus

LatviaRomania

Czechia

Portugal

Austria

Lithuania

Estonia

Hungary

Belgium

Spain

PolandItaly

IrelandFinlandDenmark

SwedenFranceNetherlands

Germany

0

Figure 4.3.1

7 Note that the 2020 report introduced methodological improvements from the 2019 version; therefore, a strict comparison is not possible. Improvements include the removal of certain keywords andthe addition of others to identify the programs. Still, more than 90% of all detected programs in the 2020 edition are triggered by keywords present in the 2019 study.

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4 . 3 A I E D U CAT I O N I N
THE EUROPEAN UNIONA N D B E YO N D

By Content Taught in AI-Related Courses
What types of AI technologies are the most popularamong the course offerings in three levels of specialized
AI programs in the European Union? Data suggests thatrobotics and automation are by far the most frequently
taught courses in the specialized bachelor’s andmaster’s programs, while machine learning dominatesin the specialized short courses (Figure 4.3.2). As shortcourses cater to working professionals, the trend showsthat machine learning has become one of the key
competencies in the professional development andimplementation of AI.

It is also important to mention the role of AI ethics andAI applications, as both content areas claim a significant
share of the education offerings among the threeprogram levels. AI ethics—including courses on security,
safety, accountability, and explainability—accounts for
14% of the curriculum on average, while AI applications—such as courses on big data, the internet of things, andvirtual reality—take a similar share on average.

SPECIALIZED AI PROGRAMS (% of TOTAL) by CONTENT AREA in EU27, 2019-20
Source: Joint Research Centre, European Commission, 2020 | Chart: 2021 AI Index Report

Short Courses

Bachelor

Master

Robotics & Automation
Machine Learning
AI Applications
AI Ethics
Computer Vision
Natural Language Processing
Connected And Automated Vehicles
Knowledge Representation And Reasoning;
Planning; Searching; Optimization
Multi-Agent Systems
Philosophy Of AI
AI (Generic)Audio Processing
0%

20%

40%

0%

20%

40%

0%

20%

40%

% of Total Specialized AI Programs
Figure 4.3.2

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I N T E R N AT I O N A L C O M PA R I S O N
The JRC report compared AI education in the 27 EU
member states with other countries in Europe, including
Norway, Switzerland, and the United Kingdom, as well
as Canada, the United States, and Australia. Figure 4.3.3
shows the total number of 1,680 specialized AI programsin all countries considered in the 2019–20 academic
year. The United States appears to have offered moreprograms specialized in AI than any other geographic
area, although EU27 comes in a close second in terms ofthe number of AI-specialized master’s programs.

4 . 3 A I E D U CAT I O N I N
THE EUROPEAN UNIONA N D B E YO N D

The United States appears
to have offered moreprograms specialized
in AI than any othergeographic area although
EU27 comes in a close
second in terms of thenumber of AI-specialized
master’s programs.

NUMBER of SPECIALIZED AI PROGRAMS by GEOGRAPHIC AREA and LEVEL, 2019-20
Source: Joint Research Centre, European Commission, 2020 | Chart: 2021 AI Index Report

United States
UnitedKingdom
EU27
Australia
CanadaBachelor
MasterNorwayShort Courses

Switzerland
0

100

200

300

Number of Courses
Figure 4.3.3

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HIGHLIGHT

AI Brain Drain and Faculty Departure
2018. They relied on data hand-collected from
LinkedIn as well as authors’ affiliation from the
Scopus database of academic publications andconferences to complement the results from theLinkedIn search.

Michael Gofman and Zhao Jin, researchers
from the University of Rochester and Cheung
Kong Graduate School of Business, respectively,
published a paper titled “Artificial Intelligence,
Education, and Entrepreneurship” in 2019 that
explores the relationship between domainspecific knowledge of university students andtheir ability to establish startups and attract
funding.8 For the source of variation in students’AI-specific knowledge, the co-authors used thedeparture of AI professors—what they referred
to as “an unprecedented brain drain”—fromuniversities to industry between 2004 andThe paper found that AI faculty departures
have a negative effect on AI startups foundedby students who graduate from universities
where those professors used to work, withthe researchers pointing to a chilling effect on
future AI entrepreneurs in the years following
the faculty departures. PhD students are themost affected, compared with undergraduate

NUMBER of AI FACULTY DEPARTURES in NORTH AMERICA, 2004-19
Source: Gofman and Jin, 2020 | Chart: 2021 AI Index Report

40

Number of Faculty

TenuredTotal Number of Departures

30

Untenured

20

10

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

0

Figure 4.4.1

8 See AI Brain Drain Index for more details.

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HIGHLIGHT

AI Brain Drain and Faculty Departure (continued)
and master’s students, and the negative impact
intensifies when the AI professors who leave are
replaced by faculty from lower-ranked schoolsor untenured AI professors.

to industry dropped from 42 in 2018 to 33 in2019 (28 of these are tenured faculty and 5 areuntenured). Between 2004 and 2019, Carnegie
Mellon University had the largest number ofAI faculty departures in 2019 (16), followed by
the Georgia Institute of Technology (14) andUniversity of Washington (12), as shown in
Figure 4.4.2.

With the updated data of 2019 from Gofman
and Jin, Figure 4.4.1 shows that after a twoyear increase, the total number of AI faculty
departures from universities in North America

NUMBER of AI FACULTY DEPATURES in NORTH AMERICA (with UNIVERSITY AFFILIATION) by UNIVERSITY, 2004-18
Source: Gofman and Jin, 2020 | Chart: 2021 AI Index Report

Carnegie Mellon University
Georgia Institute of Technology
University of Washington
University of California, Berkeley
University of Toronto
Stanford University
University of Southern California
University of Texas at Austin
University of Michigan
University of Illinois at Urbana-Champaign
University of California, San Diego
Purdue University
Harvard University
0

2

4

6

8

10

12

14

16

Number of Faculty
Figure 4.4.2

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APPENDIX

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APPENDIXC R A TAU L B E E S U R V E Y
Prepared by Betsy Bizot (CRA senior research associate)and Stu Zweben (CRA survey chair, professor emeritus at
The Ohio State University)

SourceComputing Research Association (CRA) members
are 200-plus North American organizations active incomputing research: academic departments of computerscience and computer engineering; laboratories andcenters in industry, government, and academia; andaffiliated professional societies (AAAI, ACM, CACS/AIC,IEEE Computer Society, SIAM USENIX). CRA’s mission
is to enhance innovation by joining with industry,
government, and academia to strengthen research andadvanced education in computing. Learn more about
CRA here.

Methodology
CRA Taulbee Survey gathers survey data during the fallof each academic year by reaching out to over 200 PhDgranting departments. Details about the Taulbee Survey
can be found here. Taulbee does not directly survey thestudents. The department identifies each new PhD’s areaof specialization as well as their type of employment.
Data is collected from September to January of eachacademic year for PhDs awarded in the previous
academic year. Results are published in May after datacollection closes. So the 2019 data points were newly
available last spring, and the numbers provided for 2020
will be available in May 2021.

CHAPTER 4 PREVIEWThe CRA Taulbee Survey is sent only to doctoral
departments of computer science, computer engineering,and information science/systems. Historically, (a)Taulbee covers 1/4 to 1/3 of total BS CS recipients in
the United States; (b) the percent of women earning
bachelor’s degrees is lower in the Taulbee schools thanoverall; and (c) Taulbee tracks the trends in overall CS
production.

Nuances• Of particular interest in PhD job market trends are
the metrics on the AI PhD area of specialization. The
categorization of specialty areas changed in 2008 andwas clarified in 2016. From 2004-2007, AI and robotics
were grouped; from 2008-present, AI is separate; 2016clarified to respondents that AI includes ML.
• Notes about the trends in new tenure-track hires(overall and particularly at AAU schools): In the 2018
Taulbee Survey, for the first time, we asked how many
new hires had come from the following sources: new
PhD, postdoc, industry, and other academic. Results
indicated that 29% of new assistant professors camefrom another academic institution.
• Some may have been teaching or research faculty
rather than tenure-track, but there is probably somemovement between institutions, meaning the total
number hired overstates the total who are actually new.

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A I I N D E X E D U CAT I O N S U R V E Y
Prepared by Daniel Zhang (Stanford Institute for HumanCentered Artificial Intelligence)

Methodology
The survey was distributed to 73 universities online overthree waves from November 2020 to January 2021 andcompleted by 18 universities, a 24.7% response rate. The
selection of universities is based on the World University
Rankings 2021 and Emerging Economies University
Rankings 2020 by The Times Higher Education.
The 18 universities are:
• Belgium: Katholieke Universiteit Leuven• Canada: McGill University• China: Shanghai Jiao Tong University, Tsinghua University• Germany: Ludwig Maximilian University of Munich,Technical University of Munich• Russia: Higher School of Economics, Moscow Institute ofPhysics and Technology• Switzerland: École Polytechnique Fédérale de Lausanne• United Kingdom: University of Cambridge• United States: California Institute of Technology,
Carnegie Mellon University (Department of Machine
Learning), Columbia University, Harvard University,
Stanford University, University of Wisconsin–Madison,
University of Texas at Austin, Yale University

Key Definitions• Major or a study program: a set of required and elective
courses in an area of discipline—such as AI—that leads toa bachelor’s degree upon successful completion.
• Course: a set of classes that require a minimum of 2.5
class hours (including lecture, lab, TA hours, etc.) per
week for at least 10 weeks in total. Multiple courses withthe same titles and numbers count as one course.

CHAPTER 4 PREVIEW• Practical Artificial Intelligence Models - Keywords:
Adaptive learning, AI Application, Anomaly detection,
Artificial general intelligence, Artificial intelligence,
Audio processing, Automated vehicle, Automatictranslation, Autonomous system, Autonomous vehicle,
Business intelligence, Chatbot, Computational creativity,
Computational linguistics, Computational neuroscience,
Computer vision, Control theory, Cyber physical steam,
Deep learning, Deep neural network, Expert system, Face
recognition, Human-AI interaction, Image processing,Image recognition, Inductive programming, Intelligencesoftware, Intelligent agent, Intelligent control, Intelligent
software development, Intelligence system, Knowledge
representation and reasoning, Machine learning, Machinetranslation, Multi-agent system, Narrow artificial
intelligence, Natural language generation, Natural
language processing, Natural language understanding,
Neural network, Pattern recognition, Predictive analysis,
Recommender system, Reinforcement learning, Robotsystem, Robotics, Semantic web, Sentiment analysis,
Service robot, Social robot, Sound synthesis, Speaker
identification, Speech processing, Speech recognition,
Speech synthesis, Strong artificial intelligence,Supervised learning, Support vector machine, Swarm
intelligence, Text mining, Transfer learning, Unsupervised
learning, Voice recognition, Weak artificial intelligence(Adapted from: Joint Research Centre, European
Commission, p.68)• AI Ethics - Keywords: Accountability, Consent,
Contestability, Ethics, Equality, Explainability, Fairness,
Non-discrimination, Privacy, Reliability, Safety, Security,
Transparency, Trustworthy ai, Uncertainty, Well-being(Adapted from: Joint Research Centre, European
Commission, p.68)

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E U ACA D E M I C O F F E R I N G , J O I N T
R E S E A R C H C E N T E R, E U R O P E A NCOMMISSION
Prepared by Giuditta De-Prato, Montserrat López Cobo,and Riccardo Righi

SourceThe Joint Research Centre (JRC) is the European
Commission’s science and knowledge service. The JRC
employs scientists to carry out research in order to provide
independent scientific advice and support to EU policy.
Learn more about JRC here.

Methodology
By means of text-mining techniques, the study identifies
AI-related education programs from the programs’descriptions present in JRC’s database. To query the
database, a list of domain-specific keywords is obtained
through a multistep methodology involving (i) selectionof top keywords from AI-specific scientific journals;(ii) extraction of representative terms of the industrial
dimension of the technology; (iii) topic modeling; and (iv)validation by experts. In this edition, the list of keywords
has been enlarged to better cover certain AI subdomains
and to expand to related transversal domains, suchas philosophy and ethics in AI. Then the keywords aregrouped into categories, which are used to analyze thecontent areas taught in the identified programs. The
content areas used are adapted from the JRC report“Defining Artificial Intelligence: Towards an Operational
Definition and Taxonomy of Artificial Intelligence,”
conducted in the context of AI Watch.
The education programs are classified into specialized
and broad, according to the depth with which they
address artificial intelligence. Specialized programs

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are those with a strong focus in AI, e.g., “automationand computer vision” or “advanced computer science(computational intelligence).” Broad programs target theaddressed domain, but in a more generic way, usuallyaiming at building wider profiles or making reference tothe domain in the framework of a program specialized ina different discipline (e.g., biomedical engineering).
Due to some methodological improvements introducedin this edition, namely the addition of new keywords, astrict comparison is not possible. Still, more than 90%
of all detected programs in this edition are triggered by
keywords present in the 2019 study.
The original source on which queries are performed is the
Studyportals’ database, which is made up of over 207,000
programs from 3,700 universities in over 120 countries.
Studyportals collects information from institutions’
websites, and their database is regularly updated. This
source, although offering the widest coverage among
all those identified, still suffers from some lack ofcoverage, mostly because it only tracks English-language
programs. This poses a comparability issue between
English-native-speaking countries and the rest, but also
between countries with differing levels of incorporationof English as a teaching language in higher education.
Bachelor’s-level studies are expected to be more
affected by this fact, where the offer is mostly taught ina native language, unlike master’s, which attracts moreinternational audiences and faculties. As a consequence,
this study may be showing a partial picture of the level ofinclusion of advanced digital skills in bachelor’s degree
programs.

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