Student Knowledge Analysis using Gen-AI

Abstract

Abstract: The present invention is a student knowledge analysis using gen-AI, utilizes a thorough framework to identify the important factors that influence the intention to adopt Gen-AI and these factors include perceived risk, ease of employ, effectiveness, knowledge of technological pedagogical content (KTPC) and trust. Using a combination method that integrates SEM and ANN, the research delves into the intricate interplay of these predictors and how they collectively influence the acceptance of Gen-AI.

Specification

Description:Title of the invention:
Student Knowledge Analysis using Gen-AI

Field of the invention:
The present invention relates to the field of a student knowledge analysing method and particularly relates to a student knowledge analysis using gen-AI.

Prior art to the invention:
A patent document with application number "WO2023168517", titled "adaptive learning in a diverse learning ecosystem", describes, "A system for training a student to operate an actual machine includes an electronic learning module and a simulation system for simulating operation of the actual machine. An adaptive learning artificial intelligence (ALAI) module receives student performance data to adapt training of the student. The student performance data includes instructor-graded performance results of the student based on the student operating the actual machine, simulation performance results for the student operating a simulated machine in a simulation system that simulates operation of an actual machine and electronic learning content results from an electronic learning module that delivers electronic learning content to a student computing device used by the student. The ALAI module comprises a learner profile module that profiles the student, a training task recommendation module that generates AI-generated recommendations, and an explainability and pedagogical intervention module for displaying on the instructor computing device explanations for the AI-generated recommendations".

wherein, the present invention a student knowledge analysis using gen-AI.

Objects of the invention:
The primary object of the present invention is a student knowledge analysis using gen-AI.

Summary of the invention:
It is an aspects of the present disclosure, utilizes a thorough framework to identify the important factors that influence the intention to adopt Gen-AI and these factors include perceived risk, ease of employ, effectiveness, knowledge of technological pedagogical content (KTPC) and trust. Using a combination method that integrates SEM and ANN, the research delves into the intricate interplay of these predictors and how they collectively influence the acceptance of Gen-AI.






















Detailed description:
The following specification particularly describes the invention and the manner in which it is to be performed.

The embodiment explores the complex landscape of different factors that impact the adoption of Gen-AI in higher education. The study utilizes a thorough framework to identify the important factors that influence the intention to adopt Gen-AI. These factors include perceived risk, ease of employ, effectiveness, knowledge of technological pedagogical content (KTPC) and trust. Using a combination method that integrates SEM and ANN, the research delves into the intricate interplay of these predictors and how they collectively influence the acceptance of Gen-AI. Perceived usability, utility, KTPC, and trust are some of the important factors that are uncovered by the research, which examines a varied cohort of 242 participants representing Indian higher education institutions. These participants include undergraduates, postgraduates, and faculty members. Curiously, when it comes to the intention to implement AI in higher education, perceived benefit does not seem to be a major factor. The study uses SPSS, a statistical tool package, to analyse demographic data and finds that there are non-compensatory and nonlinear connections between gender, age, and AI intention. The built ANN model achieves a 71% accuracy rate in predicting the intention to use AI by utilizing the significant predictors discovered through SEM. In addition to shedding light on the theoretical underpinnings, this study additionally provides practical implications for how Gen-AI can be effectively integrated into higher education.
, Claims:Claims:

1) We claim,
A student knowledge analysis using gen-AI, a method claim, utilizes a thorough framework to identify the important factors that influence the intention to adopt Gen-AI and these factors include perceived risk, ease of employ, effectiveness, knowledge of technological pedagogical content (KTPC) and trust,
wherein, using a combination method that integrates SEM and ANN, the research delves into the intricate interplay of these predictors and how they collectively influence the acceptance of Gen-AI.

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