Abstract: Systems, methods, and computer program products for automatically allocating tasks to resources using a machine learning model and a hypergraph are described herein. A method comprises receiving workflow information characterizing one or more updates to the task management environment; providing the workflow information as input to the machine learning model; generating, by the machine learning model, a candidate task allocation based on the workflow information and the hypergraph information; updating the hypergraph information in accordance with the workflow information; determining a weight for each of the one or more updates; selecting an individual update of the one or more updates such that the weight of the individual update is greater than or equal to a weight threshold; and updating the machine learning model to reflect the individual update.

Abstract: Techniques are described with respect to a system, method, and computer program product for predicting conversation for multi-party discussions. An associated method includes analyzing a plurality of linguistic inputs; generating an interest graph based on the analysis; extracting a plurality of temporal data from the interest graph; and generating a Post-Salutations Alignment Model (PSAM) based on clustering of the plurality of temporal data.

Abstract: According to one embodiment, a method, computer system, and computer program product for assisting with priority-based responses to requests is provided. The embodiment may include analyzing one or more conversations, wherein analyzing includes identifying data from within the one or more conversations, wherein the data includes at least one request and at least one response. The embodiment may also include training a machine learning model on the identified data using long short-term memory with layer-wise relevance propagation. The embodiment may further include assisting a user in responding with a new response to a new request using the trained model.

Abstract: Techniques are described with respect to a system, method, and computer program product for predicting conversation for multi-party discussions. An associated method includes analyzing a plurality of linguistic inputs; generating an interest graph based on the analysis; extracting a plurality of temporal data from the interest graph; and generating a Post-Salutations Alignment Model (PSAM) based on clustering of the plurality of temporal data.

Abstract: A method includes: in response to receiving opt-in consent from a student, obtaining, by a processor set, student data associated with the student; monitoring, by the processor set, performance of the student in a course using a predictive machine learning model that predicts a score in the course based on the student data; detecting, by the processor set, a negative trend in the performance of the student based on the monitoring; and in response to detecting the negative trend, matching, by the processor set, the student with a mentor for the course using a matching model.

Abstract: Methods, systems, and computer program products for automatically modifying communication content using artificial intelligence techniques are provided herein. A computer-implemented method includes identifying one or more portions of communication content within a group communication session to be modified by processing the communication content and one or more items of contextual information associated with the group communication session; generating at least one item of modified communication content related to at least a portion of the one or more identified portions of communication content by processing the one or more identified portions of communication content using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one item of modified communication content.