Using AI as an Emotion Regulation Sounding Board in Foreign Language Teaching

Abstract

The aim of the present study was to investigate Croatian foreign language teachers' emotional responses related to challenging situations encountered in their workplace, such as dealing with misbehaving learners, demanding parents, conflicts with colleagues, and unexpected scenarios causing discomfort. By adopting a qualitative, holistic approach, the study explored different facets of teachers’ emotional responses (e.g., behavioral and cognitive), the emotion regulation (ER) strategies employed, as well as the use and perception of Artificial Intelligence (AI) in managing unpleasant emotions. The teachers in the study used an online diary to document their work-related emotional experiences. The findings revealed reports of different unpleasant emotions, predominantly anger, frustration, and irritability tied to varied causes, such as learner misbehavior, colleagues’ unprofessional behavior, and relationship with parents. Emotional experiences were accompanied by various bodily and cognitive manifestations, such as shallow, fast breathing, tension, mental fatigue, and rumination, to name a few. To cope with the unpleasant emotions, the teachers resorted to different types of ER, predominantly those belonging to the response-focused category (e.g., swearing or listening to music), followed by seeking support from others, and cognitive reappraisal (antecedent-focused strategies). The teachers used AI as a sounding board for ER. They received advice emphasizing empathy, calmness, problem-solving, support-seeking, and boundary-setting to address emotional challenges. The participants reported mixed experiences with the advice they received, finding it helpful in some cases but often difficult to apply in practice due to its general nature. Lastly, the teachers observed that AI's lack of empathy and contextual understanding made it a less desirable choice compared to human support when addressing their emotional needs.