TY - GEN
T1 - The Privacy-Efficiency Tradeoff
T2 - 27th International Conference on Human-Computer Interaction (HCII 2025)
AU - Liu, Mandie
AU - Zeng, Yi
AU - Fan, Xuyu
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2025
Y1 - 2025
N2 - Online learning platforms have become popular tools for delivering education, offering convenient and affordable learning opportunities to people worldwide. However, the integration of camera monitoring and AI learning analytics raises critical questions about their pedagogical effectiveness and user acceptance. This study investigates the efficacy of two distinct AI learning analytics in camera-monitored virtual classrooms: (1) Dashboard that analysis real-time biometric data (facial expressions, body movements, etc.) into performance reports, and (2) Learning moment, with which AI captures screenshots when detecting high student engagement (via the same biometric indicators) and shows to students. Through a 2 x 2 x 2 factorial experiment (N = 268), participants completed sequential 5-min burn knowledge courses with quizzes, receiving assigned feedback after the initial session. Results demonstrated that both tools enhanced learning performances, yet reduced platform reuse intent. Critically, concurrent use of both tools diminished dashboard's efficacy, which may be due to students' aversion to AI surveillance and privacy violations. While class size showed no significant moderating effect on learning performance, one-to-many classes mitigated the negative impact of analytics on reuse intent, suggesting peer presence mitigates surveillance anxiety. These findings reveal a tension in AI-augmented education: real-time analytics improve pedagogical outcomes but trigger privacy-efficacy trade-off. Our research provides critical design insights for camera-monitored learning platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
AB - Online learning platforms have become popular tools for delivering education, offering convenient and affordable learning opportunities to people worldwide. However, the integration of camera monitoring and AI learning analytics raises critical questions about their pedagogical effectiveness and user acceptance. This study investigates the efficacy of two distinct AI learning analytics in camera-monitored virtual classrooms: (1) Dashboard that analysis real-time biometric data (facial expressions, body movements, etc.) into performance reports, and (2) Learning moment, with which AI captures screenshots when detecting high student engagement (via the same biometric indicators) and shows to students. Through a 2 x 2 x 2 factorial experiment (N = 268), participants completed sequential 5-min burn knowledge courses with quizzes, receiving assigned feedback after the initial session. Results demonstrated that both tools enhanced learning performances, yet reduced platform reuse intent. Critically, concurrent use of both tools diminished dashboard's efficacy, which may be due to students' aversion to AI surveillance and privacy violations. While class size showed no significant moderating effect on learning performance, one-to-many classes mitigated the negative impact of analytics on reuse intent, suggesting peer presence mitigates surveillance anxiety. These findings reveal a tension in AI-augmented education: real-time analytics improve pedagogical outcomes but trigger privacy-efficacy trade-off. Our research provides critical design insights for camera-monitored learning platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
KW - AI Learning Analytics
KW - Camera-monitored Learning
KW - AI Surveillance
UR - https://www.scopus.com/pages/publications/105008650589
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105008650589&origin=recordpage
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001554119600030
U2 - 10.1007/978-3-031-94153-5_34
DO - 10.1007/978-3-031-94153-5_34
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-3-031-94152-8
T3 - Communications in Computer and Information Science
SP - 354
EP - 364
BT - HCI International 2025 Posters
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
A2 - Salvendy, Gavriel
PB - Springer
CY - Cham
Y2 - 22 June 2025 through 27 June 2025
ER -