Presentation at Cyberlearning 2019 breakout session

1. Human Interfaces to Artificial
Intelligence in Education
Peter Brusilovsky
University of Pittsburgh
Vasile Rus
University of Memphis

2. • Long term problem!
– The experience of “expert systems”
• Modern issues
– Possible biases of AI-based decisions with
no ability to inspect
– Lack of trust to decisions recommendations
coming from AI
– Limited ability to control or impact AI 2
Human-AI Communication

3. • Transparent AI
• Human-Centered
design of
AI systems
• “Natural” communication with AI systems
• User interfaces for recommender systems
– Controlling and explaining recommendations
3
Growing stream of research

4. What’s about AI in Education?
4
A classic view on an AI-Ed system architecture

5. What are Possible Solutions?
• Explain
– Why a specific learning activity item is good at the current
point?
• Visualize
– What is the current state of the learner model
• Communicate
– Talk to an AI-Ed system in a natural way
• Control
– Edit or negotiate your learned morel
– Express your goals and preferences in the learning process
5

6. Two Sides of the Same Coin
Explain Visualize
CommuicateControl
6
Transparency
Interactivity
No full transparency
without interactivity
Control is challenging
without transparency

7. VISUALIZE!
Making domain and learner model visible and explorable
7

8. TileBars (Hearst, 1995)
8
Hearst,MartiA.1995."TileBars:Visualizationoftermdistributioninformation
infulltextinformationaccess."InCHI'9559-66.Denver:ACM.

9. John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual
interactive recommendation. CHI '08
PeerChooser (O’Donovan, 2008)
9

10. Open Learner Models
10
Bull, S., Brusilovsky, P., and Guerra, J. (2018) Which Learning Visualisations to Offer Students? In: V. Pammer-Schindler, M. Pérez-Sanagustín, H.
Drachsler, R. Elferink and M. Scheffel (eds.) Proceedings of 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK,
September 3–5, 2018, Springer, pp. 524–530.

11. Open Learner Model (ELM-ART)
11
Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial
Intelligence in Education 12 (4), 351-384.

12. Mastery Grids: Open Social LM
12

13. Topic-Level vs. Concept-level OLM
13

14. EXPLAIN!
Make it more clear for students why specific recommended
learning activities are recommended and how they relate to their
knowledge and learning goals
14

15. Transparent Educational Recommender?
15
Lack of
Transparency !

16. ATEC Workshop
2 0 1 9
Los Angeles
16
Explanations with OLM
Barria-Pineda,Jordan,andPeterBrusilovsky.2019."Explaining
EducationalRecommendationsThroughaConcept-levelKnowledge
Visualization."InProceedingsofthe24thInternationalConferenceon
IntelligentUserInterfaces:Companion,103--04.NewYork,NY,USA:
ACM.

17. Demo: Explaining Recommendations
17

18. Remedial Recommendations
Remedial Visual
explanations
Related concepts highlighted
Knowledge estimates as bar-chart
Recent success rate as bar-color
Warning sign on “struggled”
concepts
18

19. Remedial Recommendations
Textual explanations
# of “struggled” concepts
# of “proficient concepts”
(Knowledge Est. > .66)
19
Barria-Pineda, Jordan, Kamil Akhuseyinoglu, and Peter Brusilovsky. 2019. "Explaining Need-based Educational
Recommendations Using Interactive Open Learner Models." In International Workshop on Transparent Personalization
Methods based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation
And Personalization, UMAP '19. Larnaca, Cyprus.

20. Activity 1
• Identify places and context where AI
could be used to enhance
• Each table to suggest 3-7 contexts with
votes
– how many people at the table would like to
talk about this context both to present
existing experience and to learn
20

21. Activity 2
• Group contexts into 3 clusters focused
on similar types/context of using AI
• Each table focus on a groups of similar
types/contexts.
• The goal is to exchange information and
generate a list of issues (ethical and
technical)/controversies/biases/possible
problems associated with AI use in each
context 21