Analysis of the Effectiveness of AI-Based Chatbot as a Learning Assistant for Students with Visual Learning Style

Background. The background of this research is based on the development of AI-based learning technology that opens up opportunities to create a more personalized and adaptive learning experience. Students with visual learning styles often do not get maximum support in conventional learning methods, so a technological approach that suits their characteristics is needed.

Purpose. The purpose of this study is to analyze the effectiveness of AI-based chatbots as learning assistants for students with visual learning styles, both in terms of improving learning outcomes and students' perception of the learning experience provided by chatbots.

Method. The method used was quasi-experimental with a pretest-posttest control group design. The study population consisted of junior high school students with a dominant visual learning style. The research instruments include learning outcome tests and perception questionnaires. Data analysis was carried out using statistical tests and case studies.

Results. The results of the study showed a significant improvement in the learning outcomes of students who used AI-based chatbots. Students also responded positively to the chatbot's visual features, especially in terms of information clarity, attractive appearance, and interface interactivity. Case studies reinforce quantitative data through more meaningful learning experience narratives.

Conclusion. The conclusion of this study is that AI-based chatbots are effective as learning assistants for students with visual learning styles and have great potential to be integrated into more inclusive and adaptive digital learning systems.