11
User-Adaptive Systems
A. Jameson. "Adaptive Interfaces and Agents"
in Human-Computer Interface Handbook, 2008
Read from Usability Challenges (p. 13) to p 22.
For the rest of the content you can rely in the slides that we covered in class
[updated with content from Jan 18]
Two question by 4pm on Monday, Jan 15
No summary
post questions in Piazza, folder "Jan11"
18
Taking Over Routing tasks
Provision of Help
E. Horvitz. Principles of Mixed-Initiative User Interfaces. CHI '99, 159166
One question by noon Wednesday, Jan 17
No summary
Bunt A., Conati C. and McGrenere J. (2007). Supporting Interface Customization Using a Mixed-Initiative Approach. IUI 2007, International Conference on Intelligent User Interfaces, 92-101.
One Question by 4pm Wednesday, Jan 17
No summary
post questions in Piazza folder "Jan18"
Adapting the Interface
Bunt A., Conati C. and McGrenere J. (2007). Supporting Interface Customization Using a Mixed-Initiative Approach. IUI 2007, International Conference on Intelligent User Interfaces, 92-101.
REVIEW your questions to discuss in class
slides on MICA
slides on p value and effect size
Freye et at (2023)
Lessons Learned
from Designing and Evaluating CLAICA: A
Continuously Learning AI Cognitive Assistant
IUI 2023, International Conference on Intelligent User Interfaces,
553-567 (but lots of big pictures)
Pilzer et Al (2020)
Supporting Software Developers' Focused Work on Window-Based Desktops
CHI 2020
2
Liu et al (2017) BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation (link) , CHI 2017, p. 5869-5880 [Gauransh]
Gajos, K. et al (2006) .
Exploring the design space for adaptive graphical user interfaces. In Proceedings of AVI ’06, Advanced Visual Interfaces
Lallé
, et
al. (2021)
Gaze-Driven Adaptive Interventions for Magazine-Style Narrative
Visualizations. IEEE
Trans. Vis. Comput. Graph. 27(6): 2941-2952 (2021)
[Joy]
Aleven et al. (2023). c1.
Chapter 7 in Handbook of AI in Education
(mandatory pp 127-138, Bayesian Networks Excluded).
[Katherine Breen]
Adaptation Cycle for KT and CBM
Gajos, K. et al (2006) .
Exploring the design space for adaptive graphical user interfaces. In Proceedings of AVI ’06, Advanced Visual Interfaces
summary by 4pm on Wednesday.
BE PREPARED TO ASK YOUR QUESTIONS from last Thursday in CLASS
[SLIDES]
Kardan
and Conati (2015)
Providing Adaptive Support in an Interactive Simulation
for Learning: An Experimental Evaluation. CHI 2015: 3671-3680
Gupta et al (2023) Enhancing Stealth Assessment in Collaborative Game-Based Learning with Multi-task Learning. AIED 2023: 304-315 [Shivam Chandhok]
[these two readings go together [Romina Mahinpei]
Alam et al (2023), Does Knowing When Help Is Needed Improve Subgoal Hint Performance in an Intelligent Data-Driven Logic Tutor? AAAI 2023: 15895-15902
Maniktala et al (2020) Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help (just section 4 p. (5-14) [provides more details on the techniques in previous paper]
- the problem (strongly recommended to use a running example to clarify)
- brief summary of relevant related work
- tentative proposed solution(s),
- envisioned challenges
- tentative workplan and timeline
One of the purposes of this presentation is to get feedback from the class, so feel free to mention specific points on which you may want this feedback.
Plan for about XXX of presentation.
The 3-page proposal mentioned in the syllabus (containing the same info as above) will not be due until Friday end of the day
S
D'Mello et al (2023) Affect Detection From Wearables in the "Real" Wild: Fact, Fantasy, or Somewhere In between? IEEE Intell. Syst. 38(1): 76-84 (2023) [Vedant Bahel]
Feleey et at (2023) Modelling the Training Practices of Recreational Marathon Runners to Make Personalized Training Recommendations. UMAP 2023: 183-193
[Shashwat Suri]
- 6' per group
Make sure to include for sure
- the changes you made in response to feedback you received on the proposal
- an updated project timeline
If you have time,you can also
- describe progress made so far
- discuss any challenges encountered and your ideas on how to overcome them
Shabel et al (2020) The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. SIGIR 2020: 991-1000 [ Mehar]
Unraveling ML Models of Emotion With NOVA: Multi-Level Explainable AI for Non-Experts. IEEE Trans. Affect. Comput. 13(3): 1155-1167 (2022)
[Kaseya]
Conati et a. (2022) Personalized XAI for AI-driven Personalization. XAI workshop@I IJCAI 2022
THIS PAPER IS FROM LAST WEEK, please make sure to review it and know your 2 questions
Ritschel, et al (2019)I Irony Man: Augmenting a Social Robot with the Ability to Use Irony in Multimodal Communication with Humans. AAMAS 2019: 86-94 [Annie]
Abulimiti
et al (2023)
When to generate hedges in peer-tutoring interactions. SIGDIAL 2023: 572-583
[Clair]
- group of 3: 15'
- group of 2: 10'
- one person: 7'
plus time for questions
Make sure that your presentation includes
- the problem you are addressing and how it is related to the course
- clear positioning of your project with respect to related work
- your proposed solution
- evaluation, if any.
- discussion of contributions
- limitations
- possibilities for future work.
These last two points are very important. A good discussion here can compensate for limitations in what you actually managed to do.
FINAL REPORT DUE APRIL 22