Designing ethical emotion ai in online learning among ability-diverse learners
Chen, Si
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https://hdl.handle.net/2142/127431
Description
Title
Designing ethical emotion ai in online learning among ability-diverse learners
Author(s)
Chen, Si
Issue Date
2024-09-05
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Yun
Doctoral Committee Chair(s)
Huang, Yun
Committee Member(s)
Wang, Yang
Bosch, Nigel
Kushalnagar, Raja
Department of Study
Information Sciences
Discipline
Information Sciences
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Human-computer Interaction
Language
eng
Abstract
Emotion AI, also known as affective computing, encompasses the recognition, interpretation, simulation, and response to human emotions and cues. Despite its potential, there has been limited systematic exploration of its ethical and inclusive design, particularly in the realm of online learning. This thesis examines the ethical considerations surrounding emotion AI for inclusive online education. Specifically, the research makes novel contributions for two learner groups: the hearing community and the d/Deaf or hard of hearing (DHH) community. For hearing learners, recognition of emotions from facial movements can be applied to enhance their self-awareness and improve knowledge sharing in video-based learning. Combining facial expression recognition with self-reported emojis enables these learners to express and reflect on their emotions more comprehensively than by using emojis alone. For DHH learners, emotion AI is more effective when they have access to video comments in American Sign Language (ASL) rather than just English captions in video-based online learning. Additionally, ASL video comments featuring cartoon-like filters displaying human-like emotions are more entertaining and engaging for DHH learners, fostering a stronger sense of connection with their peers. To promote inclusive learning between hearing and DHH learners, a design fiction approach is further employed, which proposes customizable overlay solutions for seamless interactions among these diverse learners, enhancing inclusivity while preserving emotional authenticity. While this thesis centers on designing inclusive emotion AI for video-based learning, the insights offer both theoretical and practical implications in broader application domains.
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