Student Sentiment Analysis: Implementation of Artificial Intelligence in Improving Teaching Quality
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Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology.
Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy.
Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences.
Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology.
Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.
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