Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis

This paper presents a psychologically-aware conversational agent designed to enhance both learning performance and emotional well-being in educational settings. The system combines Large Language Models (LLMs), a knowledge graph-enhanced BERT (KG-BERT), and a bidirectional Long Short-Term Memory (LSTM) network with attention to classify students' cognitive and affective states in real time. Unlike prior chatbots limited to either tutoring or affective support, our approach leverages multimodal data-including textual semantics, prosodic speech features, and temporal behavioral trends-to infer engagement, stress, and conceptual understanding. A pilot study with 45 university students demonstrated improved motivation, reduced stress, and moderate academic gains compared to unimodal baselines. We explicitly discuss the exploratory nature of this small-sample pilot, report effect sizes and inter-rater reliability alongside significance tests, and provide an ablation analysis isolating the contribution of the knowledge-graph component and of each modality. These results underline the promise-while acknowledging the current limits in scale and modality balance-of integrating semantic reasoning, multimodal fusion, and temporal modeling to support adaptive, student-centered educational interventions.