Emotional support is a fundamental social construct for human beings, closely tied to mental and physical wellbeing. In the context of a classroom, teachers' emotional support has been linked to students' increased motivation, better learning outcomes, and decreased stress, ultimately representing a protective factor against the development of mental illness. Students often work on projects in teams, and many experience issues with teammates, leading to stress and frustration. However, teachers' limited time and resources represent a challenge to the provision of effective support to such students. Technology is a possible mediator between teachers and students. By means of online interventions, a conversational agent may collect students' teamwork experiences and deliver support messages at the same time, providing not only a monitoring tool for teachers but also a source of support to students. This intervention requires conversational agents with a validated framework of effective emotional support messages, adapted to the students' personalities and experiences. In this paper, the first steps for this intervention are presented. First, a corpus of emotional support statements provided by teachers for students working in teams is collected. Second, these statements are validated in emotional support categories. Third, participants are presented with a situation where they have to provide support to a student rating another one on one aspect of group work: Productivity. We investigate the adaptation of such messages to students' Emotional Stability and the given rating. Two versions of an algorithm are created based on the results.
CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; • Applied computing → Collaborative learning.