Analysis of Programming and Artificial Intelligence Based on Gemini Canvas in Supporting Deep Learning: A Structural Equation Modelling (SEM) Approach

Abstract

Highly skilled, resilient, and intelligent human resources require 21st-century skills. Students need more than just academic knowledge. Students urgently require core 21st-century skills, including digital literacy skills such as information literacy, media literacy, and technological literacy. To achieve these skills, coding and artificial intelligence (CAI) or progamming and artificial intelligence (PAI)education is needed to support deep learning as part of the implementation of learning skills and innovation—other 21st-century skills—to prepare human resources for Indonesia's Golden Age in 2045.The method used is a mixed method with data collection techniques in the form of documentation, observation, and questionnaires, analyzed using regression with the Structural Equation Modelling (SEM) approach in the Lisrel application. The issues are (1) to what extent can CAI based on Gemini Canvas support deep learning and (2) to what extent do the impact of digital literacy, human resources in artificial intelligence (AI), and the integration of AI in education on the implementation of CAI learning support deep learning through teachers' skills in CAI based on Gemini Canvas, either directly or indirectly? This research aims to (1) analyze how CAI based on Gemini Canvas can support deep learning and (2) determine the impact of digital literacy, human resources in AI, and the integration of AI in education on the implementation of CAI learning in supporting deep learning through teacher skills in CAI based on Gemini Canvas, either directly or indirectly. The results indicate that Gemini Canvas-based CAI can support deep learning, and the direct influence of the Gemini Canvas-based CAI teacher skill variable on the implementation of CAI learning in supporting deep learning is 0.65. Teachers' skills in CAI based on Gemini Canvas are directly influenced by digital literacy (0.27), human resources in AI (0.17), and the integration of AI in education (0.50). The indirect influence of the implementation of CAI learning in supporting learning is from digital literacy (0.176), human resources in AI (0.111), and the integration of AI in education (0.325). The impact of this research is that the 21st-century skills applied in CAI can support the implementation of deep learning to the fullest extent.