Abstract: Embodiments of the present disclosure relate to a method, a system and a computer program product for generating and/or creating knowledge assessment items for an item bank using artificial intelligence and machine learning.

Abstract: Embodiments of the present disclosure are related to a method, system and computer program product to monitor and detect additional display devices coupled to a candidate device in an examination environment, where the candidate device is being administered the examination, and embodiments of the present disclosure include preventing mirroring of examination of content from the candidate device on the additional display devices coupled to the candidate's device providing a secure examination environment.

Abstract: Embodiments of the present disclosure relate to a method, a system, and a computer program product for generating knowledge assessment items for an assessment of candidates in an examination and populating the generated knowledge assessment items in an item bank, the knowledge assessment items including different item types, and the knowledge assessment items being generated using artificial intelligence and machine learning, and further the knowledge assessment items generated (created) by the AI/ML module are authenticated and vetted by a subject matter expert before storing or updating the knowledge assessment item(s) in the item bank.

Abstract: Embodiment of the present disclosure relate to method, system and computer program products system of administering an examination at a candidate computing device communicatively coupled with a test administration server within a distributed computing network, by continuously monitoring a candidate performance context during administration of the examination in accordance with an initial level of monitoring parameters, and on detection of an anomaly associated with the candidate performance context, activating an escalated level of monitoring based at least in part upon detecting the anomaly, transmitting, to a proctor computing system, a candidate performance alert based on the anomaly, and receiving, from the proctor computing system based on the candidate performance alert, a proctor intervention assessment.

Abstract: Method and system of delivering and administering tests remotely on a candidate device based on a test specification, wherein the test package contains the test specifications and test package is created and stored in a distributed cache and based on a minimal latency time, download and/or push the test package from the distributed cache to the candidate device and administer the test to the candidate, collecting the accruals and uploading the accruals for the test for assessment of the candidate.

Abstract: A method and system of adaptive testing via continuous probabilistic assessment. The method comprises continuously estimating a candidate performance measure (“THETA”) based on scoring a sequence of test performance submissions acquired from a candidate computing device in response to a sequence of items presented to the candidate computing device, determining, responsive to the continuously assessing, a probabilistic performance band that is associated with the THETA, generating, based at least in part on the probabilistic performance band and a predetermined cutoff score, a decision to extend the testing in accordance with an extended sequence of items presented to the candidate computing device, and assigning, based on the extended testing, a conclusive fail status or a conclusive pass status based on cumulatively updating the probabilistic performance band.

Abstract: A method and system of training and deploying an artificial intelligence (AI) neural network for skills based examinations. A method of training the AI neural network includes providing, via input layers of the AI neural network, a training dataset of human action images for skills based examinations that require human actions performed in accordance with a predetermined sequence, generating, at an output layer of the AI neural network, a correlation between the training dataset and a validation dataset of human action images for the skills-based examination, the input layers and the output layer being interconnected via a set of fully connected layers of the AI neural network, and validating the AI neural network based on a training loss function expressed in accordance with the correlation between the training dataset and the validation dataset.

Abstract: A method and system of training an artificial intelligence (AI) neural network in authenticating a voice fingerprint. The method includes extracting data from a training dataset of voice fingerprints with an AI neural network that includes a residual convolutional neural network (ResCNN), generating processed voice fingerprint data from the extracted data based at least in part on a softmax function implemented in accordance with a softmax layer of the ResCNN, preparing a training dataset and a validation dataset of voice fingerprints based on the processed voice fingerprint data, training the AI neural network based on the training dataset and validating the trained AI neural network based at least in part on the validation dataset.

Abstract: A method and system of adaptive testing via continuous real time scoring. The method comprises scoring a first performance submission received from a candidate computing device in accordance with a first item sourced from the item bank that is presented to the candidate computing device, selecting, from the plurality of unique test items of the item bank, one of a more and a less difficult at least a second item, scoring an at least a second performance submission received from the candidate computing device, generating a candidate performance profile based at least on the first and the at least a second performance submissions received, and assigning either a conclusive fail or a conclusive pass based on the candidate performance profile as generated.