Affect has been the subject of increasing attention in cognitive accounts of learning. Many intelligent tutoring systems now seek to adapt pedagogy to student affective and motivational processes in an effort to increase the effectiveness of tutorial interaction and improve learning outcomes. However, the majority of research on tutorial feedback has focused on pedagogical content, often at the expense of the affective component of the learning process. It is unclear under which circumstances it is more appropriate to focus directly on student affect and when support is best offered through task-related feedback. This paper proposes an inductive framework for modeling task-based and affect-based feedback to inform the behavior of pedagogical agents within a narrative-centered learning environment.