Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience

This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) subcommittee members of the years 2021–2022 to provide lectures at CIS activities and conferences and constructed a basic CI conceptual knowledge structure for high-school student learning. From 2021 to 2022 in Taiwan, we collected high-school students' learning data, including labels, attitudes, environment, and effort, from the CI&AI-FML platform using robots and learning tools, then processed the data using natural language processing (NLP) techniques to efficiently evaluate high-school students' learning state. We then applied three evolutionary computation techniques: genetic algorithm (GA), particle swarm optimization (PSO), and genetic algorithm neural network (GANN) in the proposed genetic assessment agent for the co-learning model, with learning performance regression analysis. In this paper, a CI&AI-FML human and machine co-learning Metaverse model is presented as a solution, which provides hands-on learning and experience while also supporting student-centered online learning during the COVID-19 pandemic. Students participated in the course during the 2022 Spring semester to learn basic CI concepts and experience CI applications through interaction with machines using the developed CI&AI- FML learning tools. The experimental results indicate that the genetic assessment agent with the GANN method has better performance in the student and machine co-learning model as compared to the other two methods, and it is effective for student and machine co-learning model construction.