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.

翻译：现有关于基于人工智能的面部表情识别（FER）的信息对于计算机科学领域以外的人士而言难以理解，需要跨学科努力来构建一个促进对该技术及其用户影响理解的分类框架。多数支持者从方法论、实现方式和分析角度对FER进行分类；极少从其在教育中的应用角度进行分类；而尚未有从用户角度进行的研究。本文主要关注（潜在）教师用户对教育领域FER工具的使用情况。基于情感教育目标的经典分类学及相关理论，本文提出从倾向、条件与偏好三个维度对教师进行三分法分类。同时，本文将文献中记载或推断出的FER解决方案类型系统整理为"技术"与"应用"两大类别，作为构建拟议"教师用户"类别的先决条件。本研究对于支持者、批评者及用户理解教师与FER之间的关系具有启示意义。