AI-Based Adaptive Learning in Higher Education: Improving Student Engagement and Learning Outcomes

Alvita Ni’amullah, Rizkiyah Hasanah




Abstract

Artificial Intelligence (AI) based adaptive learning has great potential to improve student engagement and learning outcomes by personalizing the teaching-learning process. This study aims to analyze the implementation of AI-based adaptive learning in higher education and its impact on student engagement and learning outcomes. This study uses a qualitative approach with a case study method. The subjects of the study consisted of the Rector, Dekan, Head of the Study Program, Lecturers, IT Team, and students. Data collection techniques were done through observation, in-depth interviews, and documentation. Data analysis techniques used include data reduction, data presentation, and conclusion. The study results indicate that implementing AI-based adaptive learning in higher education provides significant benefits, such as the ability to adjust learning materials to the needs of individual students, increase learning motivation, and support students in understanding the material more deeply. AI is used to identify student learning patterns, provide real-time feedback, and recommend relevant learning resources. However, the challenges faced include the need for more technological literacy among students and lecturers, technical constraints in implementing AI systems, and ethical policies in using student data. This study significantly contributes to developing technology-based learning models in higher education. By highlighting the role of AI in creating more personalized learning experiences, this research encourages the strategic adoption of AI technology in education.

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References

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