Affective Learning Companions Toward Education 5.0
Our goal is to support children in learning through the use of Haru, a tabletop social robot. We explore how children perceive Haru, whether they would accept the robot in their learning environments, and what kinds of affective clues would be most effective to convey, using Haru. Preliminary research, discussed below, shows great potential in having Haru as a companion to support children learn.
Research Goals
Video Insights
Identification of Activities
We investigate the types of activities that Haru can support in various learning environments (e.g., children distracted during class; an adolescent learning about healthy eating behaviours). We believe that some behaviours can be improved more readily than others with Haru.
Investigation of Data Gathering
We investigate how we can gather information about a person's behaviour. Which type of information could be gathered via Haru’s future functions (e.g., audio, visual, sentiment analysis). We aim to equip Haru with multisensory functions for deep human understanding.
Investigation of Data Output
Once we gather a person's information, our goal shifts to data output. Focusing on efficient interaction between Haru and a person, we can use the collected data to allow for exploration that could improve behviour. Questions such as "How do we deliver information to people so their behaviours can be improved effectively over time?" "Can we deliver data so anyone can benefit from it?" Our current plan is to a apply data video approach for this investigation. Our study confirmed that the way we deliver messages in a video can influence the user’s perception (Sallam et al., 2022). We plan to apply this approach to deliver information to our users.
 |
 |
 |
Deep Human Understanding for Education 5.0
For deeper human understanding in school settings, Haru will be gathering various types of information about children’s experiences. For example, EEG data will be captured to infer their potential focus, facial expressions will be captured to understand their emotion or social cues, and voice data will be also analyzed to understand their sentiment. We aim to deliver smooth communication between children and Haru.
Haru Explored: Dialogues and Interactions
Haru has great potential, such as engaging in conversations, recognizing and expressing emotions, ensuring safety in interactions, detecting others' facial expressions, and presenting data visually.
Promoting AI Literacy
Our AI Literacy program introduces children to artificial intelligence through engaging, hands-on learning experiences. The program aims to help students understand how AI works, recognize its capabilities and limitations, and develop the skills to use AI critically, safely, and responsibly.
We deliver AI literacy activities at local schools in the Okanagan, working with students in grades 5-6. Each session lasts approximately 1.5 hrs, and the program runs for 4 months. To date, approximately 75 students have participated.
The curriculum covers three areas: AI Awareness, AI Mechanics, and AI Impact. Through interactive discussions, programming activities, and practical projects, students explore both the technical foundations of AI and its implications for society.
AI Literacy in Action
Watch this video for an example of how we introduce and explain AI concepts in our lessons.
Lesson Samples
Explore sample materials from our AI literacy curriculum.
Related Publications
Unpacking Micro Data Videos: Key Elements and Design Practices in Minute-Long Data Videos for Mobile Usage
Samar Sallam, Yumiko Sakamoto, Anuradha Herath, Julia Petrie, Mariana Brussoni, John Jacob, and Pourang Irani. 2025. Unpacking Micro Data Videos: Key Elements and Design Practices in Minute-Long Data Videos for Mobile Usage. Proc. ACM Hum.-Comput. Interact. 9, 5, Article MHCI036 (September 2025), 33 pages. https://doi.org/10.1145/3743722
Parent and Educator Concerns on the Pedagogical Use of AI-Equipped Social Robots
Francisco Perella-Holfeld, Samar Sallam, Julia Petrie, Randy Gomez, Pourang Irani, and Yumiko Sakamoto. (2024, September). Parent and Educator Concerns on the Pedagogical Use of AI-Equipped Social Robots. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Vol.8, No. 3 (2024)
Exploring the Design of Social Robot User Interfaces for Presenting Data-Driven Stories
Anuradha Herath, Samar Sallam, Yumiko Sakamoto, Randy Gomez, and Pourang Irani. (2023, December). Exploring the Design of Social Robot User Interfaces for Presenting Data-Driven Stories. In Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia (MUM '23). (pp. 321-339). Association for Computing Machinery.
How Should a Social Robot Deliver Negative Feedback Without Creating Distance Between the Robot and Child Users?
Yumiko Sakamoto, Anuradha Herath, Tanvi Vuradi, Samar Sallam, Randy Gomez and Pourang Irani. (2023, December). How Should a Social Robot Deliver Negative Feedback Without Creating Distance Between the Robot and Child Users? In Proceedings of the 11th International Conference on Human-Agent Interaction (HAI '23). (pp. 325-334). Association for Computing Machinery.
Presenting Data with Social Robots: An Exploration into Conveying Data Videos using an Artificial Physical Narrator
Anuradha Herath, Samar Sallam, Tanvi Vuradi, Yumiko Sakamoto, Randy Gomez and Pourang Irani. (2023, December). Presenting Data with Social Robots: An Exploration into Conveying Data Videos using an Artificial Physical Narrator (HAI '23). (pp. 476-478). Association for Computing Machinery.
Design and Development of a Teleoperation System for Affective Tabletop Robot Haru
Vasylkiv, Y., Ma, Z., Li, G., Brock, H., Nakamura, K., Pourang, I., & Gomez, R. (2021, August). Shaping Affective Robot Haru’s Reactive Response. In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) (pp. 989-996). IEEE.
Shaping Affective Robot Haru’s Reactive Response
Vasylkiv, Y., Ma, Z., Li, G., Brock, H., Nakamura, K., Pourang, I., & Gomez, R. (2021, August). Shaping Affective Robot Haru’s Reactive Response. In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) (pp. 989-996). IEEE.
Automating Behavior Selection for Affective Telepresence Robot
Vasylkiv, Y., Ma, Z., Li, G., Sandry, E., Brock, H., Nakamura, K., Pourang, I. and Gomez, R., 2021, May. Automating behavior selection for affective telepresence robot. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2026-2032). IEEE.
Persuasive Data Storytelling with a Data Video during Covid-19 Infodemic: Affective Pathway to Influence the Users’ Perception about Contact Tracing Apps in less than 6 Minutes
Sakamoto Y, Sallam S, Leboe-McGowan J, Salo A, Irani P. Persuasive data storytelling with a data video during Covid-19 infodemic: Affective pathway to influence the users’ perception about contact tracing apps in less than 6 minutes. Proceedings of the 2022 Pacific Vis. 2022.
Towards Design Guidelines for Effective Health-Related Data Videos: An Empirical Investigation of Affect, Personality, and Video Content
Sallam S, Sakamoto Y, Leboe-McGowan J, Latulipe C, Irani P. Towards Design Guidelines for Effective Health-Related Data Videos: An Empirical Investigation of Affect, Personality, and Video Content. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.
Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos
Choe, E. K., Sakamoto, Y., Fatmi, Y., Lee, B., Hurter, C., Haghshenas, A., & Irani, P. (2019). Persuasive data videos: Investigating persuasive self-tracking feedback with augmented data videos. In AMIA Annual Symposium Proceedings (Vol. 2019, p. 295). American Medical Informatics Association.
Hooked on data videos: assessing the effect of animation and pictographs on viewer engagement
Amini, F., Riche, N. H., Lee, B., Leboe-McGowan, J., & Irani, P. (2018, May). Hooked on data videos: assessing the effect of animation and pictographs on viewer engagement. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (pp. 1-9).
Evaluating Data-Driven Stories and Storytelling Tools
Amini, F., Brehmer, M., Bolduan, G., Elmer, C., & Wiederkehr, B. (2018). Evaluating data-driven stories and storytelling tools. In Data-driven storytelling (pp. 249-286). AK Peters/CRC Press
Data Representations for In-Situ Exploration of Health and Fitness Data
Amini, F., Hasan, K., Bunt, A., & Irani, P. (2017, May). Data representations for in-situ exploration of health and fitness data. In Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare (pp. 163-172).
Authoring Data-Driven Videos with DataClips
Amini, F., Riche, N. H., Lee, B., Monroy-Hernandez, A., & Irani, P. (2016). Authoring data-driven videos with dataclips. IEEE transactions on visualization and computer graphics, 23(1), 501-510.
Understanding Data Videos: Looking at Narrative Visualization through the Cinematography Lens
Amini, F., Henry Riche, N., Lee, B., Hurter, C., & Irani, P. (2015, April). Understanding data videos: Looking at narrative visualization through the cinematography lens. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1459-1468).
Collaborators
 |
 |
Team Members
Pourang P. Irani, PhD
Professor of Computer Science UBC Principal's Research Chair in Ubiquitous Analytics
Co-Director, Okanagan Visualization and Interaction Lab
University of British Columbia (Okanagan campus)
Room No: 315
Science Building
1177 Research Rd
Kelowna, BC V1V 1V7
Email: pourang.irani@ubc.ca
Pourang Irani is a Professor of Computer Science at the University of British Columbia, Okanagan (UBCO), where he holds the Principal's Research Chair in Ubiquitous Analytics. Previously, he was a faculty member at the University of Manitoba (2002–2021), where he held a Canada Research Chair in Ubiquitous Analytics (2013–2021). An internationally recognized researcher in Human-Computer Interaction (HCI), his work spans information visualization, ubiquitous and immersive analytics, wearable computing, and intelligent interactive systems. His pioneering research in ubiquitous analytics focuses on enabling natural, comfortable, and context-aware interactions with information anywhere and anytime, extending beyond traditional desktop environments. His current research focuses on human-centred AI, AI transparency, embodied intelligence, and responsible interactive technologies, with particular interest in how people, including children and non-expert users, understand and interact with intelligent systems. At UBCO, he co-leads the Digital Transparency Research Cluster and co-directs the Okanagan Visualization and Interaction Lab (OVI Lab). He also served as co-lead of the NSERC CREATE program in Visual and Automated Disease Analytics (VADA), which trained over 100 researchers in visual analytics, AI, and data science for healthcare applications. Dr. Irani's contributions have earned international recognition, including the 2023 CHCCS/SCDHM Achievement Award for sustained contributions to HCI and research awards at ACM CHI. His international appointments include an invited research position at Microsoft Research (2014) and a CIMI Excellence Research Chair in Toulouse, France (2017–2018). He has also served as an Associate Chair for ACM CHI and an Associate Editor of ACM Transactions on Computer-Human Interaction (TOCHI). Through interdisciplinary research, mentorship, and collaboration, he continues to advance human-centred, intelligent, and responsible technologies that empower people to engage with increasingly complex digital environments.
