Designing ethical emotion ai in online learning among ability-diverse learners

Chen, Si

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https://hdl.handle.net/2142/127431 Copy

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

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.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127431

Copyright and License Information

Copyright 2024 Si Chen

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