Presentation by Olaf Zawacki-Richter, University of Oldenburg, Senior EDEN Fellow, at the 2019 European Distance Learning Week's fourth-day webinar on "Artificial Intelligence (AI) in Higher Education" - 14 November 2019 Recording of the discussion is available: https://eden-online.adobeconnect.com/p7d4zev81s1s/ & https://www.youtube.com/watch?v=4eebqKEIcM8

1. AI in Higher Education –
Challenges & Opportunities
Prof. Olaf Zawacki-Richter, Ph.D.
University of Oldenburg, Germany
European Distance Learning Week
organized by EDEN
14. November 2019

2. Folie 2
https://en.wikipedia.org/wiki/Moore%27s_law (CC-BY-SA)
Moore's Law
BIG Data

3. Folie 3
 EDUCAUSE Horizon Report 2019 Higher Education Edition:
Experts anticipate AI in education to grow by 48 anually until 2022
 Contact North (2018): "there is little
doubt that the [AI] technology is inex-
orably linked to the future of higher
education" (p. 5)
 Heavy investments: TU of Eindhoven
will launch an AI Systems Institute with
50 new professorships for education
and research in AI
Relevance of AI in Education (AIEd)

4. Folie 4

5. Slide 5
Artificial Intelligence – Machine Learning – Deep Learning
Copeland (2016): https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-
machine-learning-deep-learning-ai/

6. Folie 6
 General Artificial Intelligence
o aka "strong AI"
o still only science fiction
 Narrow Artificial Intelligence
o aka "weak AI"
o aka "good old-fashioned AI" (Haugeland, 1985)
o aka "machine learning"
o a one-trick horse
Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, Mass.: MIT Press.

7. Folie 7
Hinojo-Lucena et al. (2019, S. 1):
 "This technology [AI] is already being introduced in the field
of higher education, although many teachers are unaware
of its scope and, above all, of what it consists of."
Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez,
J.-M. (2019). Artificial Intelligence in Higher Education: A Bibliometric Study on
its Impact in the Scientific Literature. Education Sciences, 9(1), 51.
https://doi.org/10.3390/educsci9010051
So… what is AI in Education (AIEd)?

9. Folie 9
 learner-facing (e.g. adaptive LMS or ITS)
 teacher-facing
(e.g. assessment and plagiarism detection tools)
 system-facing AIEd
(e.g. monitoring tools on institutional level)
AI in Education (AIEd)
Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of
artificial intelligence in schools and colleges. Nesta Foundation.

10. Systematic Review on AIEd in HE

11. Zawacki-Richter, O., Marín, V. I., Bond, M., & 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, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

12. Zawacki-Richter, O., Kerres, M.,
Bedenlier, S., Bond, M., & Buntins,
K. (Eds.). (2019). Systematic
reviews in educational research:
Methodology, perspectives and
application. Heidelberg: Springer
Open, Verlag für Sozialwissen-
schaften.

13. Folie 13
Systematic Review Questions
 Mapping:
How have publications on AI in higher education developed over
time, in which journals are they published, and where are they
coming from in terms of geographical distribution and the author's
disciplinary affiliations?
 Concept and Ethics:
How is AI in education conceptualised and what kind of ethical
implications, challenges and risks are considered?
 Applications:
What is the nature and scope of AI applications in the context of
higher education?

14. Folie 14
Context of AIEd Studies
 Higher Education (n = 146)
 K-12 (n = 6)
 Cont. Ed. / Corporate Training (n = 1)
 Vocational Training (n = 1)

15. Folie 15
Only 13 papers (8.9%) by first authors with an Education background.

16. Folie 16
Student life-cycle (Reid, 1995):
 administrative services (e.g. admission, counselling, library
services) – 92 studies (63.0 %)
 academic support services (e.g. assessment, feedback,
tutoring) – 48 studies (32.8 %)
 Six studies (4.1 %) covered both levels
Reid, J. (1995). Managing learner support. In F. Lockwood (Ed.), Open
and distance learning today (pp. 265–275). London: Routledge.

18. Folie 18
Profiling and prediction: admissions
 Chen and Do (2014): "the accurate prediction of students' academic
performance is of importance for making admission decisions as
well as providing better educational services" (p. 18).
 Acikkar and Akay (2009) predict admission decisions based on a
physical ability test, scores in the National Selection and Placement
Examination, and GPA
 Cukurova University, Adana,
Turkey
 Accuracy: 97% in 2006 using
SVM

19. Folie 19
ITS:
Duolingo chat bots for
language learning

20. Folie 20
Automated Essay Scoring (Gierl et al., 2014)
 AES for large scale assessment
 Medical Council of Canada Qualifying Examination
 Clinical Decision Making Constructed-Response Items (CDM-DCR)
 5.540 students took the test in 2013
 100 raters need 4 days (2.800 to 3.200 hours) to score the items
Gierl, M. J., Latifi, S., Lai, H., Boulais, A., & Champlain, A. (2014). Automated essay scoring and the
future of educational assessment in medical education. Medical Education, 48(10), 950–962.

21. Folie 21
Automated Essay Scoring (Gierl et al., 2014)
 LightSIDE software (open source) for machine learning
classification
 Agreement between
97.3 and 98.2 %
 Kappa values: almost
perfect agreement
 Required 3.5 hours of
one rater of coding to
prepare the data, and
10 s to score the 2013
examination

22. Folie 22
Adaptive Systems
CENTURY Intelligent Learning Platform
 "CENTURY is the first teaching and learning platform to use AI.
Our technology, named CAI, provides students with a truly
personalised education and enables teachers to make evidence-
based interventions."
 https://www.century.tech/the-platform/

24. Folie 24
Intelligent Classroom Behavior Management System
https://www.businessinsider.de/china-school-facial-recognition-technology-2018-5?r=US&IR=T

25. Folie 25
Ethical issues and challenges
Only 2 out of 146 included studies discussed ethical
issues associated with AIEd applications!
"All AI researchers should be concerned with the ethical
implications of their work"
Russel & Norvig (2010, p. 1020)
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern
approach (3rd ed). Upper Saddle River: Prentice Hall.

26. Prof. Dr. Olaf Zawacki-Richter
Carl von Ossietzky Universität Oldenburg
Center for Lifelong Learning (C3L)
Center for Open Education Research (COER)
olaf.zawacki.richter@uni.oldenburg.de
@Zawacki_Richter
http://www.uni-oldenbur.de/coer/
Thanks for your attention!