Animeedu: An AI-Powered Personalized Learning Platform with Anime-Based Explanations
Traditional digital learning platforms commonly deliver educational material through text, static presentations, or prerecorded lessons, which may not adequately address differences in learners’ language preferences, interests, and learning pace. This paper presents AnimeEdu, an AI-powered personalized learning platform that combines topic-based explanations with anime-inspired animated teaching to create an interactive learning experience. The proposed platform organizes learning around a workflow in which learners select a topic, preferred language, learning material, and teaching-avatar style. AI-assisted lesson generation is used to prepare simplified explanations, which are presented through an animated teaching interface. The platform further incorporates an AI-based doubt-solving interface, quiz-based assessment, and a dashboard for communicating learner performance. The system design brings together concepts from multimedia learning, adaptive educational systems, conversational AI, and engagement-oriented interface design. The project is evaluated at the functional-workflow level by examining the intended operation of its principal modules and their integration. The available project material describes the platform features but does not provide a controlled learner study or validated pre- and post-assessment dataset; therefore, improvements in learning outcomes are not claimed. AnimeEdu demonstrates a practical model for integrating animated explanations and AI-supported learning assistance, while identifying empirical evaluation, stronger content grounding, and deeper learner adaptation as important directions for further development. Keywords— Personalized learning; Artificial intelligence; Animated education; Conversational AI; Educational technology; Learning assessment
Authors
- Dhanushree C. A.
- Prof. Sharadhi S
- Spandana D
- Shambhavi B.
- Anusha L.
Publication Details
- Journal
- International Journal of Creative and Open Research in Engineering and Management
- Published
- 2026-11-11
- DOI
- https://doi.org/10.55041/ijcope.v2i11.038
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00