AI-Based Facial Emotion Recognition Solutions for Education: A Study of
  Teacher-User and Other Categories
R. Yamamoto Ravenor
Existing information on AI-based facial emotion recognition (FER) is not
easily comprehensible by those outside the field of computer science, requiring
cross-disciplinary effort to determine a categorisation framework that promotes
the understanding of this technology, and its impact on users. Most proponents
classify FER in terms of methodology, implementation and analysis; relatively
few by its application in education; and none by its users. This paper is
concerned primarily with (potential) teacher-users of FER tools for education.
It proposes a three-part classification of these teachers, by orientation,
condition and preference, based on a classical taxonomy of affective
educational objectives, and related theories. It also compiles and organises
the types of FER solutions found in or inferred from the literature into
"technology" and "applications" categories, as a prerequisite for structuring
the proposed "teacher-user" category. This work has implications for
proponents', critics', and users' understanding of the relationship between
teachers and FER.