The existing literatures based on AI- facial emotion recognition (FER) presents a challenge for non-specialists, necessitating a collaborative inter-disciplinary effort to establish a comprehensive framework that enhances comprehension of this new technology and its implications for the end-users. Prevailing categorizations principally revolve around methodological, implementation, and analytical aspects, along with limited attention to its educational applications as well as user-centric perspectives. This current study primarily focuses on potential educators who prefer to work upon FER tools. It introduces a threefold classification of these educators, based upon their orientation, context, and preferences, drawing from established taxonomies of affective educational objectives and relevant theoretical foundations. Also, this research systematically gathers and categorizes the various FER solutions documented in the literature. This work holds significance for advancing the comprehension of the interplay between educators and FER technology among proponents, critics, and end-users.

1. Maja Pantic and Marian Stewart Bartlett. Machine analysis of facial expressions. In Kresimir Delac and Mislav Grgic, editors, Face Recognition, pages 377–416. I-Tech Education and Publishing, 2007.
2. Mark Andrejevic and Neil Selwyn. Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2):115–128, 2020.
3. Andrew McStay. Emotional ai and edtech: serving the public good? Learning, Media and Technology, 45(3):270– 283, 2020.
4. Ben Williamson, Sian Bayne, and Suellen Shay. The datafication of teaching in higher education: critical issues and perspectives, 2020.
5. Reinhard Pekrun, Thomas Goetz, Wolfgang Titz, and Raymond P. Perry. Positive emotions in education. In Erica Frydenberg, editor, Beyond Coping: Meeting Goals, Visions, and Challenges, pages 149–173. Oxford University Press, 2002.
6. Riccardo Mazza and Vania Dimitrova. Coursevis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human-Computer Studies, 65(2):125– 139, 2007.
7. Roberto Martinez Maldonado, Judy Kay, Kalina Yacef, and Beat Schwendimann. An interactive teacher’s dashboard for monitoring groups in a multi-tabletop learning environment. In Intelligent Tutoring Systems: 11th International Conference, ITS 2012, Chania, Crete, Greece, June 14-18, 2012. Proceedings 11, pages 482–492. Springer, 2012.
8. Sinem Aslan, Nese Alyuz, Cagri Tanriover, Sinem E Mete, Eda Okur, Sidney K D’Mello, and Asli Arslan Esme. Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms. In Proceedings of the 2019 chi conference on human factors in computing systems, pages 1–12, 2019.
9. Bui Ngoc Anh, Ngo Tung Son, Phan Truong Lam, Le Phuong Chi, Nguyen Huu Tuan, Nguyen Cong Dat, Nguyen Huu Trung, Muhammad Umar Aftab, and Tran Van Dinh. A computer-vision based application for student behavior monitoring in classroom. Applied Sciences, 9(22):4729, 2019.
10. Ashwin TS and Ram Mohana Reddy Guddeti. Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Education and information technologies, 25(2):1387– 1415, 2020.
11. Weiqing Wang, Kunliang Xu, Hongli Niu, and Xiangrong Miao. Emotion recognition of students based on facial expressions in online education based on the perspective of computer simulation. Complexity, 2020:1–9, 2020.
12. Jeniffer Xin-Ying Lek, Jason Teo, et al. Academic emotion classification using fer: A systematic review. Human Behavior and Emerging Technologies, 2023, 2023.
13. Reinhard Pekrun, Thomas Goetz, Anne C Frenzel, Petra Barchfeld, and Raymond P Perry. Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (aeq). Contemporary educational psychology, 36(1):36–48, 2011.
14. Anand Ramakrishnan, Brian Zylich, Erin Ottmar, Jennifer LoCasale-Crouch, and Jacob Whitehill. Toward automated classroom observation: Multimodal machine learning to estimate class positive climate and negative climate. IEEE transactions on affective computing, 2021.
15. Zhenzhen Luo, Chaoyu Zheng, Jun Gong, Shaolong Chen, Yong Luo, and Yugen Yi. 3dlim: Intelligent analysis of students’ learning interest by using multimodal fusion technology. Education and Information Technologies, pages 1–21, 2022.
16. B.S. Bloom. Taxonomy of Educational Objectives: The Classification of Educational Goals. Cognitive domain. Handbook 1. Number v. 1 in Taxonomy of Educational Objectives: The Classification of Educational Goals. Longman, 1956.
17. D.R. Krathwohl, B.S. Bloom, and B.B. Masia. Taxonomy of Educational Objectives: The Classification of Educational Goals. Affective domain. Handbook II. Number v. 1 in Taxonomy of Educational Objectives: The Classification of Educational Goals. Longman Group, 1964.
18. Ralph Hoepfner et al. CSE-RBS Test Evaluations: Tests of Higher-Order Cognitive, Affective, and Interpersonal Skills. ERIC, 1972.
19. Allan Robert Brandhorst. Reconceptualizing the Affective Domain. ERIC, 1978.
20. William J Gephart, Robert B Ingle, and Frederick J Marshall. Evaluation in the affective domain. Phi Delta Kappa„ 1980.
21. Barbara L McCombs and Robert J Marzano. Putting the self in self-regulated learning: The self as agent in integrating will and skill. Educational psychologist, 25(1):51–69, 1990.
22. Philip Clarkson, Alan Bishop, and Wee Tiong Seah. Mathematics education and student values: The cultivation of mathematical wellbeing. In International research handbook on values education and student wellbeing, pages 111–135. Springer, New York, 2010.
23. Hye Jin Yun and Jungwon Cho. Affective domain studies of k-12 computing education: a systematic review from a perspective on affective objectives. Journal of Computers in Education, pages 1–38, 2021.
24. Jeff Irvine. Taxonomies in education: Overview, comparison, and future directions. Journal of Education and Development, 5(2):1, 2021.
25. Jennifer A Fredricks, Phyllis C Blumenfeld, and Alison H Paris. School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1):59–109, 2004.
26. J. Dewey. The Collected Works of John Dewey. DigiCat, 2022.
27. A. Renninger, M. Nieswandt, and S. Hidi. Interest in Mathematics and Science Learning. American Educational Research Association, 2015.
28. J.G. Freeman. Interest and Special Education: The Role of Interest in Teaching Children with Learning Disabilities. University of Michigan, 1998.
29. M. C. Coleman. Design thinking and the school library. Knowledge Quest, 44(5):62–68, 2016.
30. Joseph Giacomin. What is human centred design? The design journal, 17(4):606–623, 2014.
31. Ido Roll and Ruth Wylie. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2):582–599, 2016.
32. Mei Zhang and Lijun Zhang. Cross-cultural o2o english teaching based on ai emotion recognition and neural network algorithm. Journal of Intelligent & Fuzzy Systems, 40(4):7183–7194, 2021.
33. John D Krumboltz and William W Farquhar. The effect of three teaching methods on achievement and motivational and outcomes in a how-to-study course. Psychological Monographs: General and Applied, 71(14):1, 1957.
34. Calvin C Nelson. Affective and cognitive attitudes of junior high school teachers and pupils. The Journal of Educational Research, 58(2):81–83, 1964.e classification schemes will necessitate revision and expansion.

Facial emotion recognition FER in education FER teacher users