AI-Based Adaptive Learning Systems and their role in Enhancing Student Academic Performance

Abstract

The purpose of the study is to explore students’ perception regarding AI-based adaptive learning systems and examine how students perceive that AI systems have impacted their academic performance. The study is focused on such fundamental areas like awareness, perceived usefulness, personalization, engagement, challenges, and behavioural intention to use adaptive technology on learning. The research approach was quantitative and survey-based design. The data was collected using a sample of 300 students of different levels of education who were previously exposed to AI-based adaptive learning systems. On the basis of the demographic features and various dimensions of adaptive learning, the responses were gathered with the assistance of a structured questionnaire. The analysis of the data was based on descriptive statistical tools, including frequencies, percentages, mean scores, and standard deviations.

The results prove that the students are highly aware of the AI-based adaptive learning systems and that the students have high perceived usefulness of the technologies. It was also discovered that individualization and flexibility were strengths particularly in providing of personalized learning pathways and pacing. The research also indicates the presence of a positive impact on student engagement, self-directed learning, and perceived academic performance, particularly in reaching the learning objectives and positively affecting test performance. Nevertheless, such threats like the limitation of the internet, technical difficulties, and usability problems were discovered to impede optimal use. In spite of these obstacles, students were also reported to have a high behavioural intention to use AI-based adaptive learning systems again.

The paper concludes that AI-based adaptive learning systems can be highly effective to improve the student learning process and academic performance in case they are correctly implemented. To maximise their educational impact, it is necessary to address infrastructural and technical issues, as well as to offer institutional support and guidance. The results provide useful information to teachers, learning institutions, and system developers aiming to incorporate adaptive learning technologies to educational practice.

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