EQīLevel: Emotion-Aware Reinforcement Learning for Adaptive Academic Tutoring

Abstract

Intelligent tutoring systems (ITS) used in cybersecurity education often lack the ability to respond to learners’ emotional states during complex analytical tasks. This paper introduces EQīLevel, an emotionally adaptive AI tutoring architecture that integrates reinforcement learning (RL), sentiment detection, and large language model (LLM) dialogue within a lightweight command-line interface (CLI) + FastAPI framework. Traditional intelligent tutoring systems often rely on rigid rule-based structures and rarely account for learners’ emotional states, which can reduce engagement and persistence. EQīLevel addresses this limitation by analyzing voice-based cues and dynamically adapting lesson difficulty, tone, and pacing through a JSON-based Model Context Protocol (MCP). The MCP encodes emotion, performance, and learning-style variables into structured state representations that guide Generative Pre-trained Transformer (GPT) dialogue generation and reinforcement learning policy updates. Evaluation using simulated learner interactions demonstrated 78% successful adaptation to frustration scenarios, Whisper transcription accuracy with a 5.3% word error rate (WER), emotion detection accuracy of 84% with 81% tone alignment, and improved reinforcement learning convergence, with average rewards rising from 0.41 to 0.63. In cybersecurity education, EQīLevel illustrates how emotionally adaptive tutoring may help learners remain resilient when confronting ambiguous and adversarial scenarios such as phishing detection and threat analysis. By combining emotional awareness with adaptive instructional control, EQīLevel demonstrates a scalable framework for emotionally adaptive tutoring.