Abstract: A system and method for guiding and constraining an Artificial Intelligence (AI) engine to provide personalized educational support to a user on an online learning platform using a multi-assistant framework is disclosed. The system and method access curriculum database and grading rubrics. User interaction data including user responses and selected units or topics is received. A free-response questions (FRQs) are generated using algorithms. The user responses to the FRQs are graded by utilizing the grading rubrics and providing projected score using FRQ grader assistant. The FRQ grader provides feedback aligned with scoring guidelines based on grading rubrics, and delivers projected score. A prompt is generated and transferred to AI engine to generate an assessment corresponding to the user performance based on a grading result on the user responses to FRQs. The generated FRQs, graded user responses, assessment, and projected scores are provided to the user on the online learning platform.

Abstract: The carelessness check system analyzes the nature of mistakes while attempting a primary test and subsequently providing a follow-up test. The method includes receiving the responses of the user by the response tracker while attempting a primary test. The evaluator then evaluates the incorrect responses of the user. Utilizing this information, the test preparation module prepares a follow-up test. The user then attempts the follow-up test and answers the questions. The careless detection module identifies the nature of response of the user given in the follow-up test such that answering two questions correctly in the follow-up test indicates carelessness and answering the first follow-up question indicates a conceptual misunderstanding.

Abstract: A system and method are disclosed for generating standardized performance metrics for a user based on educational data obtained from one or more educational platforms. The system and method involve collecting performance-related data using a data collector integrated within an educational activity to curriculum standard mapping module, where the data reflects the user's educational activities. A normalization module standardizes this data according to predefined teaching curriculum standards. The normalized data is then mapped by assigning weights and confidence scores to determine the user's mastery level over various curriculum standards. A data managing module organizes this mastery information for further analysis. Based on the mapped data, the educational activity to curriculum standard mapping module produces a comprehensive and standardized performance metric for the user.

Abstract: A method is provided for guiding an Artificial Intelligence (AI) engine to generate enriched educational content by contextualizing educational standards with additional information. The method includes accessing a curriculum database containing educational standards and defining multiple extended attribute types, each representing a category of contextual data relevant to the standards. Detailed extended attributes are associated with these types and linked to specific courses within the curriculum, enabling content generation that aligns with both the standards and their educational context. A prompt is generated to direct a Large Language Model (LLM) to map the extended attributes to the corresponding educational standards. This prompt is transferred to the AI engine, enabling it to recognize and apply the extended attribute types for generating contextually enriched educational content.

Abstract: A system and method to evaluate the mastery status of a user and recommend learning resources is disclosed. Receiving plurality of input parameters via a mastery evaluation and learning resource recommendation system. The hierarchical table generation module generates a hierarchical table based on the plurality of input parameters. The hierarchical table represents parent-child relationships linking the standards. Furthermore, the mastery status detection module receives a user performance data from the user performance database. The mastery status detection module first maps the learning resources to the corresponding standard within the hierarchical table and then utilizes algorithms to classify the learning resources into essential and non-essential learning resources and calculate the mastery status of the user. The learning resource recommendation module further recommends the learning resources to the user to attain mastery for the specific standard.

Abstract: A method for guiding and constraining an Artificial Intelligence (AI) engine to deliver personalized learning recommendations based on a user's performance and behavior across online learning platforms. The method includes integrating a framework to enable communication between platforms and a learning system, collecting assessment and session data such as scores, time spent, answer choices, and navigation behavior. A data collection module parses this information to identify learning patterns, difficulties, and unproductive behaviors. Based on the analysis, a prompt is generated to guide the AI engine in producing personalized, actionable recommendations. These recommendations are presented to the user in real time via a popup window within the learning platform, providing adaptive, context-aware support during learning session.

Abstract: A system and method for transforming a computer display into a dynamic knowledge structure representing hierarchical knowledge levels, achievements, and prerequisites that represent foundational knowledge to mastery of a knowledge concept is disclosed. The system and method access a knowledge data that defines knowledge levels. The knowledge levels represent a sequence of knowledge levels to reach mastery levels. An electronic display is transformed to visually present the knowledge levels as interconnected, physical object representations. The interconnections represent the prerequisite knowledge levels. Then personnel knowledge completion levels of a student are accessed.

Abstract: A method of tracking mastery of a user on an online learning platform. The method includes executing code using one or more processors of a computer system to cause the computer system to perform operations include receiving inputs from the user related to selection of a topic that the user wants to study, presenting a set of questions based on educational standards related to the topic. The mastery of the user on the topic is updated in real-time based on the responses submitted by the user on the presented questions. The mastery is also displayed to the user via a graphical representation on the user interface. The educational standards are identified within a topic on which user has lowest mastery levels and receives questions stored in a database that are targeted on the unmastered standards.

Abstract: A mastery level determining method to assess user mastery of educational skills by centralizing and processing data from diverse educational platforms is disclosed. The method involves collecting user educational data from various sources such as online learning platforms, external assessments, and internal quizzes. This data is then normalized to adapt to the educational standards and ingested into a structured mastery framework. The recent and reliable data is evaluated to determine skill mastery states, prioritizing newer and more reliable information. Subsequently, reliability and recency scores are calculated and assigned to each educational data. The method further categorizes user mastery skills into distinct states, providing an overview of the user's knowledge. The simplified mastery states and associated tags are accessible to the user, facilitating personalized learning experiences and assessments within the online learning platforms.

Abstract: Systems and methods are disclosed for subscriber sampling for network packet forwarding based upon unique subscriber identifiers. Control packets within input packets are processed to identify unique subscriber identifiers and related session identifiers, which are stored in records within a tracking table. Each input packet is analyzed to extract a session identifier and a unique subscriber identifier if present within the input packet. When a unique subscriber identifier is not present, the tracking table is accessed to determine a unique subscriber identifier associated with the session identifier extracted from the packet. The input packet is sampled based upon the unique subscriber identifier to determine whether or not to output the input packet as a sampled packet. The subscriber sampling can include hash-based sampling, dynamic function based sampling, and/or other subscriber/call based sampling methods. Sampled packets are forwarded to egress port(s) for further processing.

Abstract: Systems and methods are disclosed for subscriber sampling for network packet forwarding based upon unique subscriber identifiers. Control packets within input packets are processed to identify unique subscriber identifiers and related session identifiers, which are stored in records within a tracking table. Each input packet is analyzed to extract a session identifier and a unique subscriber identifier if present within the input packet. When a unique subscriber identifier is not present, the tracking table is accessed to determine a unique subscriber identifier associated with the session identifier extracted from the packet. The input packet is sampled based upon the unique subscriber identifier to determine whether or not to output the input packet as a sampled packet. The subscriber sampling can include hash-based sampling, dynamic function based sampling, and/or other subscriber/call based sampling methods. Sampled packets are forwarded to egress port(s) for further processing.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for correlating, load balancing and filtering tapped GTP and non-GTP packets. One method for correlating, load balancing and filtering tapped GTP and non-GTP packets includes receiving GTP packets tapped from a plurality of GTP network tap points. The method further includes receiving non-GTP packets tapped from at least one non-GTP network tap point. The method further includes correlating GTP packets with non-GTP packets for a particular subscriber. The method further includes forwarding the GTP packets and non-GTP packets correlated for the particular subscriber to a network monitoring tool.

Abstract: Methods, systems, and computer readable media for testing time sensitive network (TSN) elements are disclosed. According to one method for testing a TSN element, the method occurs at a test system. The method includes synchronizing a test system clock with a clock at a system under test (SUT). The method also includes receiving a sequence of messages, wherein the sequence of messages is generated using schedule rules associated with a TSN stream. The method further includes determining, using timing information associated with the test system clock, whether the schedule rules are accurately implemented by the SUT.

Abstract: A system for testing recovered clock quality includes a test device for operating as a timing synchronization protocol master for communicating with a device under test functioning as a timing synchronization protocol slave or a timing synchronization protocol boundary clock to synchronize a clock of the device under test with a clock of the test device. The system further includes a recovered clock quality tester for receiving, from the device under test, a reverse synchronization message including clock information and for using the clock information to quantify a synchronization error between the clock of the device under test and the clock of the test device.

Abstract: A method for measuring one-way link delay includes transmitting a first packet from a first network device. The first packet is passively intercepted, a first copy of the first packet is transmitted to the first network device, and a second copy of the first packet is transmitted to a second network device. A time of receipt of the first copy of the first packet is recorded as an origin timestamp at the first network device. A time of receipt of the second copy of the first packet is recorded as a receive timestamp at the second network device. A second packet including the origin timestamp is transmitted from the first network device. The second packet is passively intercepted, and a first copy of the second packet is transmitted to the second network device. The first copy of the second packet is received at the second network device, and the origin timestamp is extracted from the first copy of the second packet.

Abstract: A system for testing recovered clock quality includes a test device for operating as a timing synchronization protocol master for communicating with a device under test functioning as a timing synchronization protocol slave or a timing synchronization protocol boundary clock to synchronize a clock of the device under test with a clock of the test device. The system further includes a recovered clock quality tester for receiving, from the device under test, a reverse synchronization message including clock information and for using the clock information to quantify a synchronization error between the clock of the device under test and the clock of the test device.

Abstract: A method for measuring one-way link delay includes transmitting a first packet from a first network device. The first packet is passively intercepted, a first copy of the first packet is transmitted to the first network device, and a second copy of the first packet is transmitted to a second network device. A time of receipt of the first copy of the first packet is recorded as an origin timestamp at the first network device. A time of receipt of the second copy of the first packet is recorded as a receive timestamp at the second network device. A second packet including the origin timestamp is transmitted from the first network device. The second packet is passively intercepted, and a first copy of the second packet is transmitted to the second network device. The first copy of the second packet is received at the second network device, and the origin timestamp is extracted from the first copy of the second packet.