Commercial facial affect detection software is typically trained on large databases and achieves high accuracy in detecting basic emotions, but their use in educational settings is unclear. The goal of this research is to determine how basic emotions relate to the achievement emotion states that are more relevant in academic settings. Such relations, if accurate and consistent, may be leveraged to make more effective use of the commercial affect-detection software. For this study, we collected affect data over four days from a classroom study with 65 students using Betty’s Brain. Basic emotions obtained from commercial software were aligned to achievement emotions obtained using sensor-free models. Interpretable classifiers enabled the study of relationships between the two types of emotions. Our findings show that certain basic emotions can help infer complex achievement emotions such as confusion, frustration and engaged concentration. This suggests the possibility of using commercial software as a less context-sensitive and more development-friendly alternative to the affect detector models currently used in learning environments.