Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909956701159424 |
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| author | Chaabene, Nour El Houda Ben Hammami, Hamza Kahloul, Laid |
| author_facet | Chaabene, Nour El Houda Ben Hammami, Hamza Kahloul, Laid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis Chaabene, Nour El Houda Ben Hammami, Hamza Kahloul, Laid Computation and Language 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. |
| title | Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.10441 |