AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories

TL;DR

This paper proposes a new classification framework for AI-based facial emotion recognition tools in education, focusing on teacher-users and their characteristics to enhance understanding of the technology's impact.

Contribution

It introduces a novel three-part teacher-user classification and organizes FER solutions into technology and application categories for educational contexts.

Findings

Classifies teachers based on orientation, condition, and preference.

Organizes FER solutions into technology and application categories.

Provides a framework to understand FER's impact on teachers and education.

Abstract

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.