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As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically provide feedback to students about the quality of summaries written at the end of intelligent textbook sections. The study builds on the work of Botarleanu et al. , who used a Longformer Large Language Model (LLM) to develop a summary grading model. Their model explained around 55% of holistic summary score variance as assigned by human raters. This study uses a principal component analysis to distill summary scores from an analytic rubric into two principal components – content and wording. This study uses two encoder-only classification large language …