Class-wide comprehension modeling and ai-sourced teacher content recommendations

A computer system comprising:

• one or more computer processors;
• and
• one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to:
• access historical consumption information describing historical educational content consumption behaviors of a set of historical users and historical comprehension information comprising a historical measure of comprehension of historical educational content by the set of historical users;
• generate a training dataset based on the accessed historical consumption information and historical comprehension information;
• train a machine-learned model configured to predict a collective measure of comprehension for a set of users consuming educational content based on behaviors of the set of users as the users consume the educational content;
• apply the machine-learned model to behaviors of a set of target users consuming target educational content to determine a collective target measure of comprehension for each of a plurality of portions of the target educational content;
• and
• for a portion of the target educational content corresponding to a below-threshold collective target measure of comprehension:
• identify one or more characteristics of the portion of the target educational content;
• apply a content identification model to the identified characteristics of the portion of the target education content to identify a set of supplemental educational content related to the portion of the target education content;
• and
• generate a teacher interface to present the set of supplemental educational content with the target educational content.

A non-transitory computer-readable medium comprising stored instructions that when executed by one or more processors of one or more computing devices, cause the one or more computing devices to:

• access historical consumption information describing historical educational content consumption behaviors of a set of historical users and historical comprehension information comprising a historical measure of comprehension of historical educational content by the set of historical users;
• generate a training dataset based on the accessed historical consumption information and historical comprehension information;
• train a machine-learned model configured to predict a collective measure of comprehension for a set of users consuming educational content based on behaviors of the set of users as the users consume the educational content;
• apply the machine-learned model to behaviors of a set of target users consuming target educational content to determine a collective target measure of comprehension for each of a plurality of portions of the target educational content;
• and
• for a portion of the target educational content corresponding to a below-threshold collective target measure of comprehension:
• identify one or more characteristics of the portion of the target educational content;
• apply a content identification model to the identified characteristics of the portion of the target education content to identify a set of supplemental educational content related to the portion of the target education content;
• and
• generate a teacher interface to present the set of supplemental educational content with the target educational content.