1. Use of Artificial Intelligence in educational portals and OER repositories
Irene-Angelica Chounta
Senior Researcher
Centre for Educational Technology, University of Tartu

2. Objective To provide an overview of AI and ML practices
To discuss benefits, challenges and risks
To explore actions for fostering the adoption of AI and ML

3. Artificial Intelligence and Machine Learning
Artificial Intelligence (AI):
technologies that allow machines to act and take decisions imitating
human intelligence (McCarthy, 1998)
Machine Learning (ML):
algorithms that teach machines how to perform specific tasks
through experimentation (Michalski, Carbonell, & Mitchell, 2013).

4. Artificial Intelligence and Machine Learning
Intelligent Tutoring Systems (ITS)
computer-assisted instruction systems (CAI)
1970s
Constructionism (Papert, 1980)
1980s
1990s
Online Learning (eLearning)
2000s
MOOCs
Learning Analytics
2010s
OER, educational portals
Artificial Intelligence (AI): technologies that allow machines to act and take decisions imitating human intelligence (McCarthy, 1998)
Machine Learning (ML): algorithms that teach machines how to perform specific tasks through experimentation (Michalski, Carbonell, & Mitchell, 2013).

5. AI in education (incl. OER & portals) today
Assessment
Recommendations
Personalization and adaptation
Learners
Instructors
Other stakeholders (e.g. course designers, policy makers, content authors)
Intelligent Tutoring Systems
Online Learning Platforms (e.g. LMS, MOOCS)
OER portals and repositories

6. Assessment Predictive student models (ITS)
Social network analysis (MOOCs)
Learning analytics (LMS, educational portals)

7. Recommendations Knowledge-based (ontology) recommendations
Content-based recommendations
Collaborative filtering
Social-network recommendations
Hybrid recommendations

8. Recommendations Knowledge-based (ontology) recommendations
Content-based recommendations
Collaborative filtering
Social-network recommendations
Hybrid recommendations

9. Personalization, adaptation
Feedback
Automated or semi-automated textual descriptions (prompts)
Visualizations / Adaptive dashboards
Learning Materials
Materials’ adaptation focused on localization / quality
Adaptation/personalization focused on users’ needs

10. Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton
OER portal and Open Online Learning platform
Cognitive ML student models for performance assessment
Authoring tools for instructors
Open Learning CMU
eDidaktikum
Knewton
NextLab / GoLab
IBM Watson + Edmodo

11. Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton
NextLab / GoLab
IBM Watson + Edmodo
Educational portal for teachers’ training
Competence models for real-time tracking of skills acquisition
Work-in-progress

12. Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton
NextLab / GoLab
IBM Watson + Edmodo
Adaptive learning platform
Machine learning models to assess students’ mastery levels

13. Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton
NextLab / GoLab
IBM Watson + Edmodo
Online labs
Machine learning analytics to inform teachers about students’ progress
Has been successfully integrated in K-12 classrooms in Europe and internationally

14. Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton
NextLab / GoLab
IBM Watson + Edmodo
AI personalized agents for identifying learning gaps
NLP, machine learning student models
New partnership

15. Adopting AI: Potential benefits
Allows adaptation and personalization of materials, learning environments and learning process
Provides tools for stakeholders to retrieve appropriate resources
Scaffolds learning through monitoring, mirroring and guiding

16. Adopting AI: From research to practice
New technologies take too long to make it to the classroom – if at all
We lack a common view on shared technological challenges
Users’ expectations about technology change rapidly
Stakeholders lack a common understanding about computational tools, benefits and pitfalls

17. Adopting AI: Challenges
Pedagogical challenges
Technical challenges
Privacy challenges

18. Adopting AI: Challenges
Pedagogical challenges
Technical challenges
Privacy challenges
New roles for teachers and students
The social aspect of learning
Quality control of material
Evidence-based approaches

19. Adopting AI: Challenges
Pedagogical challenges
Technical challenges
Privacy challenges
User-centered designs over one- size-fits-all
Cost vs. efficiency /effectiveness tradeoff
Openness and accessibility, privacy and security

20. Adopting AI: Challenges
Pedagogical challenges
Technical challenges
Privacy challenges
Personalization over privacy dilemma
Just because its “there”, doesn’t mean its ok to use it
Informed consent

21. Conclusion
Despite the promising benefits, adoption of new technologies is slow (and painful….)
One small step at a time: Supporting local structures and directives
Facilitate transition through communication, openness, experimentation

22. The next day Steps to promote AI integration:
to help stakeholders familiarize with AI and ML research and practice;
to carry out long-term initiatives for demonstrating AI technologies in the field
to involve stakeholders when designing cutting-edge computational tools

23. A new learning paradigm: BYOR
A socio-technical approach for AI-supported OER portals to promote 21st century skills: - students are encouraged to retrieve, use and assess OER resources; - teachers create conditions for enabling a new learning approach; - a crowdsourcing infrastructure to ensure quality control and sharing of resources

24. Discussion
Happy to answer your questions :)