This paper presents a novel educational framework that leverages Long Short-Term Memory (LSTM) networks and their variations to simulate the concept of long term dependency learning through an interactive game-based model. The frame- work introduces a dual-sequence pattern learning environment where a primary sequence is influenced by a secondary memory sequence. A reflex-based AI agent mimics LSTM behavior by combining information from both sequences to predict the next element in the main sequence. This system aims to help learners intuitively understand how LSTMs and related models retain and utilize past information to inform current decisions. Feedback- driven gameplay fosters deeper engagement, while gradually increasing complexity helps solidify the concept of long-term dependencies. The model effectively bridges theoretical under- standing with hands-on learning by simulating the memory- driven decision making process characteristic of recurrent neural networks.
Long Short-Term Memory (LSTM), Reflex Agent, Memory-Augmented Learning, Recurrent Neural Net- works (RNNs), Educational Game, AI in Education, Sequence Prediction