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

• URL: http://arxiv.org/abs/2512.10441v1
• Date: Thu, 11 Dec 2025 09:06:45 GMT
• Title: Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis
• Authors: Nour El Houda Ben Chaabene, Hamza Hammami, Laid Kahloul,
• Abstract summary: This paper presents a psychologically-aware conversational agent designed to enhance both learning performance and emotional well-being in educational settings.<n>The system combines Large Language Models (LLMs), a knowledge graph-enhanced BERT (KG-BERT), and a bidirectional Long Short-Term Memory (LSTM) with attention to classify students' cognitive and affective states in real time.
• Score: 0.15293427903448018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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) 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 university students demonstrated improved motivation, reduced stress, and moderate academic gains compared to baseline methods. These results underline the promise of integrating semantic reasoning, multimodal fusion, and temporal modeling to support adaptive, student-centered educational interventions.

Related papers

• Conversational Learning Diagnosis via Reasoning Multi-Turn Interactive Learning [16.641654527654246]
  ParLD is a preview-analyze-reason framework for conversational learning diagnosis.<n>It uses multi-agent collaboration to diagnose students' cognitive state over multiple dialogue turns.
  arXiv  Detail & Related papers  (2026-03-03T18:29:00Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models [59.693733170193944]
  Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings.<n>Recent reinforcement learning approaches address this limitation but face two critical challenges.<n>We propose the Unidirectional Cognitive Optimization (UCO) method to address these challenges.
  arXiv  Detail & Related papers  (2025-11-12T01:27:02Z)
• OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning [20.08144763551689]
  We propose OnlineMate, a multi-agent learning companion system driven by large language models (LLMs)<n>OnlineMate is capable of simulating peer-like agent roles, adapting to learners' cognitive states during collaborative discussions, and inferring their psychological states, such as misunderstandings, confusion, or motivation.<n> Experimental results in simulated learning scenarios demonstrate that OnlineMate effectively fosters deep learning and discussions while enhancing cognitive engagement in online educational settings.
  arXiv  Detail & Related papers  (2025-09-18T09:56:45Z)
• ChatGPT produces more "lazy" thinkers: Evidence of cognitive engagement decline [0.0]
  This study investigates the impact of generative artificial intelligence (AI) tools on the cognitive engagement of students during academic writing tasks.<n>The results revealed significantly lower cognitive engagement scores in the ChatGPT group compared to the control group.<n>These findings suggest that AI assistance may lead to cognitive offloading.
  arXiv  Detail & Related papers  (2025-06-30T18:41:50Z)
• Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale [0.0]
  The absence of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience.<n>This paper explores the integration of sentiment analysis into learning management systems (LMS) to bridge the student-teacher's gap.
  arXiv  Detail & Related papers  (2025-06-05T18:13:07Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing [77.14348157016518]
  Research on KT modeling focuses on predicting future student performance based on existing, unupdated records of student learning interactions.<n>We propose a knowledge-tracking cognitive representation optimization (CRO-KT) model that uses dynamic programming algorithms to optimize the structure of cognitive representation.
  arXiv  Detail & Related papers  (2025-04-05T09:32:03Z)
• Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems [34.760230622675365]
  Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience.<n>The emergence of large language models (LLMs) further enables better human-machine interaction.<n>LLMs can produce diverse student responses according to the given language ability and personality traits.
  arXiv  Detail & Related papers  (2024-04-10T06:03:13Z)
• A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition [72.36055502078193]
  We propose a hierarchical framework, based on chain regression models, for affective recognition from vocal bursts. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE" tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:08:45Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.