ESTD PERPUSTAKAAN UNTAD
ESTD PERPUSTAKAAN UNTAD
| JudulPENGARUH ARTIFICIAL INTELLIGENCE (AI) DALAM PROBLEM BASED LEARNING (PBL) UNTUK MENINGKATKAN KEMAMPUAN REPRESENTASI SISWA PADA MATERI LARUTAN PENYANGGA |
| Nama: ALFI HAIRUNNISA RAHAYU LESTARI |
| Tahun: 2025 |
| Abstrak Alfi Hairunnisa, 2025. THE INFLUENCE OF ARTIFICIAL INTELLIGENCE (AI) IN PROBLEM BASED LEARNING (PBL) TO IMPROVE STUDENTS’ REPRESENTATION ABILITIES ON BUFFER SOLUTIONS MATERIAL.Thesis. Chemistry Education Study Program, Department of Mathematics and Natural Sciences Education, Faculty of Teacher Training and Education, Tadulako University. Supervisor (1) Tri Santoso (2) Dewi Satria Ahmar. Technological advances in the digital age have brought about various innovations in the world of education, one of which is the use of Artificial Intelligence (AI) that can help make the learning process more interactive and adaptive. One popular form of AI used in learning is ChatGPT. The integration of AI in learning, particularly in the PBL model, is considered capable of stimulating critical thinking and supporting the improvement of students' cognitive abilities. This study aims to investigate the impact of implementing AI-based ChatGPT in the PBL model on students' ability to represent buffer solution material. The research method employs a quantitative approach with a pretest-posttest design. The research population consisted of all 257 students in 9 classes of grade XI at SMAN 5 Palu. The sampling technique used was cluster sampling, resulting in two selected classes: the control class XI M 5 (n=22) and the experimental class XI M 4 (n=22). Data analysis was conducted using descriptive and inferential analysis through an independent sample t-test. The results of the study indicate that there is an effect of the application of ChatGPT-based Artificial Intelligence (AI) in the PBL model on students' representation skills in buffer solution material. This is particularly evident in the macroscopic and symbolic representation aspects. The average posttest scores for students' representation skills in the experimental class in the macroscopic, submicroscopic, and symbolic aspects were 17.2, 9.74, and 15.33, respectively, with an overall score of 70.22. Meanwhile, the average posttest scores for the control class in the macroscopic, submicroscopic, and symbolic aspects were 14.54, 6.90, and 12.52, respectively, with an overall score of 56.59. These findings indicate that the integration of AI in the PBL model is effective in enhancing students' understanding of chemical concepts. This study is expected to serve as a reference for the development of more innovative chemistry learning models that align with the needs of the 21st century. Keywords:Artificial Intelligence, ChatGPT, Problem Based Learning, Representation, Buffer Solutio |