Does learner conscientiousness matter when generating emotional support in feedback?

This paper describes the development of an algorithm for use by an Empathic Conversational Agent for choosing appropriate emotional support messages to a learner receiving feedback on their performance. We present a study where we employed a User as Wizard approach to explore how such statements were used by people giving feedback to a fictional learner with high or low conscientiousness and varying grades. We found that the type of emotional support employed depended primarily on the grade that had been achieved, but conscientiousness also influenced the amount of advice given. Interesting differences were found in how people combine Emotional Support messages depending on grade and level of conscientiousness, which inspired an algorithm for automatically choosing appropriate messages depending on context.