Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots
The rapid growth of artificial intelligence (AI) and machine learning has led to the development and widespread adoption of chatbots in different applications, including education. Chatbots have the potential to facilitate personalised learning and enhance knowledge acquisition. At the same time, th...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/97946 |
| _version_ | 1848766342111428608 |
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| author | Hamza Ali, Muhammad Afrin, Mahbuba Mahmud, Redowan Krishna, Aneesh |
| author_facet | Hamza Ali, Muhammad Afrin, Mahbuba Mahmud, Redowan Krishna, Aneesh |
| author_sort | Hamza Ali, Muhammad |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The rapid growth of artificial intelligence (AI) and machine learning has led to the development and widespread adoption of chatbots in different applications, including education. Chatbots have the potential to facilitate personalised learning and enhance knowledge acquisition. At the same time, these AI tools have demonstrated adverse effects on the learning process and academic integrity. Hence, the primary objective of this work is to develop a novel approach to determine if a user interacting with the chatbot exhibits genuine learning intent, which is under-explored in the literature. The outcome is SentimentGPT, a sentiment analysis framework that leverages natural language processing transformers fine-tuned with emotion recognition to assess the actual learning motivation during user interactions. By discerning authentic learning mindsets from superficial engagements, SentimentGPT enables AI chatbots to deliver tailored responses without compromising educational ethics. Experimental evaluations demonstrate that our approach achieves 77.94% accuracy and an F1 score of 0.91 in identifying a learning mindset, underscoring its potential to enhance user learning experiences within accredited educational settings. |
| first_indexed | 2025-11-14T11:49:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-97946 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:37Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-979462025-07-15T04:40:41Z Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots Hamza Ali, Muhammad Afrin, Mahbuba Mahmud, Redowan Krishna, Aneesh The rapid growth of artificial intelligence (AI) and machine learning has led to the development and widespread adoption of chatbots in different applications, including education. Chatbots have the potential to facilitate personalised learning and enhance knowledge acquisition. At the same time, these AI tools have demonstrated adverse effects on the learning process and academic integrity. Hence, the primary objective of this work is to develop a novel approach to determine if a user interacting with the chatbot exhibits genuine learning intent, which is under-explored in the literature. The outcome is SentimentGPT, a sentiment analysis framework that leverages natural language processing transformers fine-tuned with emotion recognition to assess the actual learning motivation during user interactions. By discerning authentic learning mindsets from superficial engagements, SentimentGPT enables AI chatbots to deliver tailored responses without compromising educational ethics. Experimental evaluations demonstrate that our approach achieves 77.94% accuracy and an F1 score of 0.91 in identifying a learning mindset, underscoring its potential to enhance user learning experiences within accredited educational settings. 2025 Journal Article http://hdl.handle.net/20.500.11937/97946 https://doi.org/10.1109/TTS.2025.3552752 restricted |
| spellingShingle | Hamza Ali, Muhammad Afrin, Mahbuba Mahmud, Redowan Krishna, Aneesh Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title | Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title_full | Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title_fullStr | Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title_full_unstemmed | Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title_short | Cross-Layered Sentiment Analysis for Identifying Learner Intent in AI Chatbots |
| title_sort | cross-layered sentiment analysis for identifying learner intent in ai chatbots |
| url | http://hdl.handle.net/20.500.11937/97946 |