The document outlines an Erasmus+ project called Artificial Intelligence in Teaching (AIT) that aims to identify and analyze best practices of AI in higher education in Denmark, Portugal, and the UK. The objectives are to study examples of AI use, identify national approaches, and develop a roadmap for future development. Preliminary outcomes found that while AI is widely researched in higher education, it is not widely used to support learning, and the impact of AI on society is not widely taught. The project will disseminate its findings to increase knowledge of AI dimensions and inform institutional decisions.

1. Artificial Intelligence in Teaching (AIT):
A road map for future developments
José Bidarra, Universidade Aberta, Portugal,
Henrik Køhler Simonsen, SmartLearning, Denmark,
Wayne Holmes, Nesta, United Kingdom
Project 2019-1-DK01-KA203-060293 W

2. 1. The promise of AI in Higher Education
2. The stakeholders of AI in Higher Education
3. The context of Erasmus+ Project AIT
4. Project objectives and expected outcomes
5. National cases and strategies
6. Dissemination
Project 2019-1-DK01-KA203-060293 W

3. The promise of AI in Higher Education (HE)
- Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
Project 2019-1-DK01-KA203-060293 W

4. The promise of AI in Higher Education (HE)
- Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
- AI is already a part of life. For instance, personal agents, such as Siri, Alexa or
Cortana, and algorithms that bring us personalised recommendations, for instance in
Amazon or Netflix.
Project 2019-1-DK01-KA203-060293 W

5. The promise of AI in Higher Education (HE)
- Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
- AI is already a part of life. For instance, personal agents, such as Siri, Alexa or
Cortana, and algorithms that bring us personalised recommendations, for instance in
Amazon or Netflix.
- AI-powered learning systems are increasingly being deployed in schools, colleges
and universities.
Project 2019-1-DK01-KA203-060293 W

6. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
W

7. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
W

8. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Learning
about AI
W

9. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Learning
about AI
Learning for
AI
W

10. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Learning
about AI
Learning for
AI
W

11. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
Learning
about AI
Learning for
AI
W

12. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Learning for
AI
W

13. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Learning for
AI
W

14. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Learning for
AI
W

15. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Training
tomorrow’s
AI engineers
Learning for
AI
W

16. The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Training
tomorrow’s
AI engineers
Learning for
AI
Learning to
live with AI
W

17. Some questions for you:
- What examples of learning with AI in higher education do you know?
- What examples of learning about AI in higher education do you know?
- What examples of learning for AI in higher education do you know?
Project 2019-1-DK01-KA203-060293 W

18. The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
W

19. The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
W

20. The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
W

21. The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
Researchers
W

22. The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
Researchers
Decision
makers
W

23. The context of the AIT project
- Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
Project 2019-1-DK01-KA203-060293 H

24. The context of the AIT project
- Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
- The “Artificial Intelligence in Teaching” Erasmus+ project (AIT) aims to identify and
analyse AI best practices in HE in three countries and to develop a roadmap for
future developments and use.
Project 2019-1-DK01-KA203-060293 H

25. The context of the AIT project
- Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
- The “Artificial Intelligence in Teaching” Erasmus+ project (AIT) aims to identify and
analyse AI best practices in HE in three countries and to develop a roadmap for
future developments and use.
- The AIT project seeks to uncover national characteristics, specific technologies, and
didactic and pedagogical approaches to AI in HE in the United Kingdom, Portugal
and Denmark.
Project 2019-1-DK01-KA203-060293 H

26. AIT project objectives
A. To identify and analyse practical examples of AI in HE.
B. To identify and analyse best practices of AI in HE.
C. To identify national approaches to AI in HE.
D. To develop a roadmap for the development of AI in HE.
E. To disseminate knowledge about AI in HE.
These objectives are all related to the national characteristics of the countries in
the project: Denmark, Portugal and the United Kingdom.
Project 2019-1-DK01-KA203-060293 H

27. AIT project expected outcomes
i. increased knowledge of the dimensions of AI at all levels in the HE
sector,
ii. research-based data of AI technologies in use across the HE sector,
iii. a research-based roadmap for future development and use of AI in HE,
iv. increased knowledge of AI for informing institutional decisions and
policymaking across Europe.
Project 2019-1-DK01-KA203-060293 H

28. Study of national cases (DK, UK, PT)
- Identification of cases and activities;
Project 2019-1-DK01-KA203-060293 H

29. Study of national cases (DK, UK, PT)
- Identification of cases and activities;
- Classification of focus area (some categories):
• Intelligent Tutoring Systems;
• Dialogue-based Tutoring Systems;
• Exploratory Learning Environments;
• Automatic writing evaluation;
• Language Learning;
• Tutoring chatbots;
• Learning analytics;
• Augmented and Virtual Reality.
Project 2019-1-DK01-KA203-060293 H

30. National characteristics (DK, UK, PT)
- Overview of EU principles on AI in HE
- National digital maturity and strategies for AI
- Analysis of national AI focus areas
- Overview of HE system and national strategy for AI in HE
- Evidence of AI in HE (research, practice / with, for, about AI)
Project 2019-1-DK01-KA203-060293 H

31. AIT project preliminary outcomes
- AI is widely taught and researched in HE (i.e., learning about AI).
Project 2019-1-DK01-KA203-060293 J

32. AIT project preliminary outcomes
- AI is widely taught and researched in HE (i.e., learning about AI).
- The impact of AI on human lives (i.e., learning for AI) is not widely
taught in HE.
Project 2019-1-DK01-KA203-060293 J

33. AIT project preliminary outcomes
- AI is widely taught and researched in HE (i.e., learning about AI).
- The impact of AI on human lives (i.e., learning for AI) is not widely
taught in HE.
- AI is not widely used to support learning in HE (i.e., learning with AI).
Project 2019-1-DK01-KA203-060293 J

34. Emerging framework
Project 2019-1-DK01-KA203-060293
Learning
with AI
Learning
about AI
Learning
for AI
✔
J

35. Emerging framework
Project 2019-1-DK01-KA203-060293
Learning
with AI
Learning
about AI
Learning
for AI
✔
Intelligent
Tutoring
Systems
Dialogue-
based
Tutoring
Systems
Exploratory
Learning
Environments
Automatic
writing
evaluation
ITS+
Language
Learning
Chatbots
Augmented
and Virtual
Reality
Learning
Network
Orchestrators
Learning
Analytics
✔ (✔)
J

36. National examples of learning with AI in HE
PT
- ABC Teach (learning
analytics, fuzzy logic and
affective computing)
- Learning Scorecard
(descriptive learning
analytics)
- ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293 J

37. National examples of learning with AI in HE
PT
- ABC Teach (learning
analytics, fuzzy logic and
affective computing)
- Learning Scorecard
(descriptive learning
analytics)
- ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293
DK
- Area 9
(an intelligent tutoring
system)
- Damvad Analytics
(learning analytics, Southern
Denmark University)
- AI in Business Economics
(exploratory learning
environment
J

38. National examples of learning with AI in HE
PT
- ABC Teach (learning
analytics, fuzzy logic and
affective computing)
- Learning Scorecard
(descriptive learning
analytics)
- ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293
UK
- Ada
(a student-support chatbot,
Bolton College)
- OU Analyse
(learning analytics, Open
University)
- Scholarly Knowledge Mining
(KMI)
DK
- Area 9
(an intelligent tutoring
system)
- Damvad Analytics
(learning analytics, Southern
Denmark University)
- AI in Business Economics
(exploratory learning
environment
J

39. Dissemination
- Erasmus+ Project Results Platform;
- Project website https://learninghub.smartlearning.dk/projekter/artificial;
- Social networking (Facebook, LinkedIn, Twitter, Instagram);
- Websites, newsletters and press releases by each institution;
- Workshops, seminars and a final conference;
- Articles published in Journals and conferences.
Project 2019-1-DK01-KA203-060293 J

40. Some answers from you:
Project 2019-1-DK01-KA203-060293 J
?

41. References
- Holmes, W., Bialik, M. and Fadel, C. (2019). Artificial Intelligence in Education: Promises and
Implications for Teaching and Learning. Boston, MA: The Center for Curriculum Redesign.
- Kukulska-Hulme, A., Beirne, E., Conole, G., Costello, E., Coughlan, T., Ferguson, R., FitzGerald, E.,
Gaved, M., Herodotou, C., Holmes, W., Mac Lochlainn, C., Nic Giollamhichil, M., Rienties, B.,
Sargent, J., Scanlon, E., Sharples, M. and Whitelock, D. (2020). Innovating Pedagogy 2020:
Open University Innovation Report 8. Milton Keynes: The Open University.
- Luckin, R., Holmes, W., Forcier, L. and Griffiths, M. (2016). Intelligence Unleashed. An Argument
for AI in Education. London: Pearson.
- Zawacki-Richter, O., Marín, V. I., Bond, M. and Gouverneur, F. (2019) ‘Systematic review of
research on artificial intelligence applications in higher education – where are the
educators?’, International Journal of Educational Technology in Higher Education, vol. 16, no.
1 [Online]. DOI: 10.1186/s41239-019-0171-0
Project 2019-1-DK01-KA203-060293 J