AI in Education Adaptive Learning, Assessment, and Student Behavior Analytics
Volume 1 Issue 1
Year of Publication : 2025
Authors : Gopika. P, Dhanapriya. K, Ashika. R
Doi : XXXX XXXX XXXX
Keywords
AI in Education, Adaptive learning system, Personalised Learning Intelligent Tutoring System (ITS), Student Assessment/Student Evaluation/Automated Grading Generation,/Learning Analytics*/Automatic Analysis of a Students Behaviour / Predictive Analytics.
Abstract
AI is revolutionizing the education industry one step at a time with new and improved methods of learning, assessment and insights to students well-being. AI-powered adaptive learning Intelligence can also be applied to personalizing the curricular content and instruction on who your students are, how they’re doing, the rate they learn at, and what their interests do for them based on performance so you get it all done 1)Intelligence-based algorithms allow students to have a personalized experience. Both of these solution sets are based on state-of-the-art machine learning models, and use natural language processing and predictive analytics to get a real-time readout on how learners are interacting with the content in order to keep them engaged – and knowledgeable. And again, not only does AI drive personalized learning, but also enable assessment (a.k.a., scoring) through automated grading and real- time feedback around open response types (essay or coding). If predictive models work, they might even allow teachers to identify students most likely to drop out or under-achieve, and intervene early enough for them to be turned around. Their AI-backed student behavior analytics solution uses data from LMS, online actions, and engagement scores to predict alerts about whether a students’ participation, motivation or performance is going south. This allows schools to design curriculum as well as targeted support or intervention. ‘But some hurdles remain to AI implementation, like privacy concerns or algorithmic bias, or an escape from human interaction between students and teachers. Possible future work includes the improvement of AI explainability, the integration of multimodal data formats, enabling collaborative learning and branching out from typical cases of lifelong learning. This paper presents an overview, critical view of its deployed factors concerned with AI in adaptive learning, assessment and student behavior analytics), new challenges to be faced by this approach too as well new trends the impact on (existing practices for) teaching learning processes and how we can enhance it adopting Intelligent Technologies.
Cite this Article
Gopika. P, Dhanapriya. K, Ashika. R, 2025. "AI in Education Adaptive Learning, Assessment, and Student Behavior Analytics", International Journal for Research in Computer Science and Information Technology (IJRCSIT) 1(1): 31-39.