Abstract. As feedback is an important part of learning and motivation, we investigate how to adapt the feedback of a conversational agent to learner personality (as well as to learner performance, as we expect an interaction effect between personality and performance on feedback). We investigate two aspects of feedback. Firstly, we investigate whether the conversational agent should employ a slant (or bias) in its feedback on particular test scores to motivate a learner with a particular personality trait more effectively (for example, using "you are slightly below expectations" versus "you are substantially below expectations" depending on learner conscientiousness). Secondly, we investigate which emotional support messages the conversational agent should use (for example: using praise, emotional reflection, reassurance or advice) given learner personality and performance.We investigate the adaptation of this feedback to a learner personality, in particular the traits in the Five Factor Model. Five experiments were run where participants gave progress feedback and emotional support to students with different personalities and test scores. The type of emotional support given varied between different personalities (e.g. neurotic individuals with poor grades received more emotional reflection). Two algorithms were created using different methods to describe the adaptations and evaluated on how well they described the experimental data using DICE scores. A refined algorithm was created based on the results. Finally, we ran a qualitative study with teachers to investigate the algorithm's effectiveness and further refine the algorithm.

Abstract. This paper uses a User-as-Wizard approach to evaluate how people apply diversity to a set of recommendations. In particular, it considers how diversity is applied for a recipient with high or low Openness to Experience, a personality trait from the Five Factor Model. While there was no effect of the personality trait on the degree of diversity applied, there seems to be a trend in the way in which it was applied. Maximal categorical diversity (across genres) was more likely to be applied to those with high Openness to Experience, at the expense of maximal thematic diversity (within genres).

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Personality impacts all areas of our lives; it governs who we are and how we react to life’s challenges. Personalized systems that adapt to end users should take into account the user’s personality to perform well. Several methodologies (e.g. User-as-Wizard, indirect studies) that use personality adaptation require first for personality to be conveyed to the participant; this has few validated approaches. Furthermore, measuring personality is often time consuming, prone to response bias (e.g. using questionnaires) or data intensive (e.g. using behaviour or text mining). This paper presents a methodology for creating and validating stories to convey psychological traits and for using such stories with a personality slider scale to measure these traits. We present the validation of the scale and evaluate its reliability. To evidence the validity of the methodology, we outline studies where the stories and scale have been effectively applied (in recommender systems, intelligent tutoring systems, and persuasive systems).