Enhancing Structural Engineering Education: Integrating Artificial Intelligence for Continuous Improvement
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Abstract
The study aims to analyze the impact of artificial intelligence (AI) usage on structural learning through student-developed programming in open-source software languages: Python, Octave, and OpenSees. The research collaborates with 90 undergraduate students in the early courses of civil engineering at the Universidad Nacional de Chimborazo. The ADDIE methodology is employed in the initial phase for planning, development, and monitoring. A survey on students' perceptions regarding effectiveness, satisfaction, recommendation, and feedback is conducted, followed by academic performance evaluation using a grading rubric to verify the achievement of set objectives. An analysis of factors contributing to AI-focused learning is then performed. Initial results revealed outliers, some deviating from study parameters and others discarded for a comprehensive view of study behavior. Regarding the survey data analysis, efficiency and satisfaction exhibited the highest reliability. Subsequently, variables were correlated considering their normality, showing a relationship between effectiveness and satisfaction; however, a strong connection cannot be guaranteed for these or other variables. Therefore, ANOVA tests, indicating positive linear relationships, and hypothesis testing were employed, demonstrating that students achieved objectives with a moderately high degree of effectiveness and satisfaction. The use of technological options and consideration of innovative learning methods can positively enhance the learning experience, contingent on prior education. Exploring artificial intelligence may prove challenging without guided information search based on predefined criteria and constraints.
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