Enhancing Digital Literacy and Speaking Skills of Students Based on Artificial Intelligence Through Deep Learning

Abstract

The enhancement of AI-based digital literacy and speaking skills among students through deep learning represents a strategic realization of digital transformation across all sectors, including higher education. This development is examined through a structured framework comprising (1) introduction, (2) objectives, (3) implementation, (4) procedural steps, (5) materials, (6) references, and (7) assessment and final evaluation. The research subjects consisted of 840 students enrolled in the Indonesian Language course, distributed as follows: 71 from Public Health A, 63 from Public Health B, 76 from Public Health C, 235 from Undergraduate Medicine, 67 from Nutrition A, 64 from Nutrition B, 59 from Dentistry A, 46 from Dentistry B, 30 from Nursing A, 35 from Nursing B, 24 from Informatics A, 22 from Informatics B, 24 from Informatics C, 8 from Informatics D, and 16 from Informatics E. Data were collected via Google Forms. The results indicate that improvements in AI-based digital literacy and speaking skills through deep learning were executed with a balanced distribution across planning (38.93%), implementation (54.81%), and evaluation (45.45%). These findings demonstrate a significant equilibrium between knowledge and skills, as well as between theory and practice, in advancing students' AI-based digital literacy and oral communication competencies through deep learning.