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Deep Convolutional Neural Network-Based Adaptive Chatbot Framework for Personalized Educational Support in Autism Spectrum Disorder

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Dr. Ali Hosseini ¹ ,

⁴ Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Autism Spectrum Disorder (ASD) presents significant challenges in personalized learning due to heterogeneous cognitive, behavioral, and communication profiles among learners. Traditional educational systems often fail to provide adaptive and scalable individualized support. This research proposes a Deep Convolutional Neural Network (DCNN)-based adaptive chatbot framework designed to deliver personalized educational assistance for individuals with ASD. The system integrates deep learning-driven natural language understanding, contextual adaptation mechanisms, and behavior-aware response generation to enhance engagement and learning effectiveness.

The framework builds upon recent advancements in chatbot architectures and AI-based educational systems (Dhyani & Kumar, 2019; Kasthuri & Balaji, 2021). It also incorporates insights from domain-specific conversational AI systems trained on limited datasets (Kapočiute-Dzikiene, 2020) and ASD-oriented chatbot applications (Li et al., 2020). The proposed system further leverages architectural optimization strategies inspired by channel optimization techniques in deep learning-based chatbot frameworks (Alruily, 2022).

Experimental evaluation through simulated learning environments demonstrates that the proposed model improves response relevance, engagement consistency, and personalized learning alignment compared to conventional rule-based and hybrid chatbot systems. The study contributes to the development of AI-driven inclusive education systems by bridging gaps in accessibility, adaptability, and emotional intelligence in educational chatbots for ASD learners.

Keywords

Autism Spectrum Disorder, Deep Convolutional Neural Network, Adaptive Chatbot

References

M. Alruily, “ArRASA: channel optimization for deep learning-based Arabic NLU chatbot framework,” Electronics, vol. 11, no. 22, 2022

J. Batani, E. Mbunge, and L. Leokana, “A deep learning-based chatbot to enhance maternal health education,” in 2024 Conference on Information Communication Technology and Society, ICTAS 2024, 2024, pp. 7–11

J. Beaudry, A. Consigli, C. Clark, and K. J. Robinson, “Getting ready for adult healthcare: Designing a chatbot to coach adolescents with special health needs through the transitions of care,” Journal of Pediatric Nursing, vol. 49, pp. 85–91, 2019

M. Dhyani and R. Kumar, “An intelligent chatbot using deep learning with bidirectional RNN and attention model,” Materials Today: Proceedings, vol. 34, pp. 817–824, 2019

K. Deepika, V. Tilekya, J. Mamatha, and T. Subetha, “Jollity chatbot-a contextual AI assistant,” The 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, pp. 1196–1200, 2020

E. Long, S. Vijaykumar, S. Gyi, and F. Hamidi, “Rapid transitions: experiences with accessibility and special education during the COVID-19 crisis,” Frontiers in Computer Science, vol. 2, 2021

S. Holmes, A. Moorhead, R. Bond, H. Zheng, V. Coates, and M. McTear, “Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?,” in Proceedings of the 31st European Conference on Cognitive Ergonomics, 2019, pp. 207–214

K. Kapočiute-Dzikiene, “A domain-specific generative chatbot trained from little data,” Applied Sciences, vol. 10, no. 7, 2020

S. L. Kasthuri and S. Balaji, “Natural language processing and deep learning chatbot using long short term memory algorithm,” Materials Today: Proceedings, vol. 81, no. 2, pp. 690–693, 2021

M. A. Khadija, W. Nurharjadmo, and Widyawan, “Deep learning generative Indonesian response model chatbot for JKN-KIS,” APICS 2022 - 2022 1st International Conference on Smart Technology, Applied Informatics, and Engineering, Proceedings, pp. 70–74, 2022

J. Kim, H. G. Lee, H. Kim, Y. Lee, and Y. G. Kim, “Two-step training and mixed encoding-decoding for implementing a generative chatbot with a small dialogue corpus,” 2IS and NLG 2018 - Workshop on Intelligent Interactive Systems and Language Generation, pp. 31–35, 2018

D. Larbi, K. Denecke, and E. Gabarron, “Usability testing of a social media chatbot for increasing physical activity behavior,” Journal of Personalized Medicine, vol. 12, no. 5, 2022

S. Lemaignan, N. Newbutt, L. Rice, and J. Daly, “‘It’s important to think of pepper as a teaching aid or resource external to the classroom’: A social robot in a school for autistic children,” International Journal of Social Robotics, vol. 16, no. 6, pp. 1083–1104, 2024

X. Li, H. Zhong, B. Zhang, and J. Zhang, “A general Chinese chatbot based on deep learning and its’ application for children with ASD,” International Journal of Machine Learning and Computing, vol. 10, no. 4, pp. 519–526, 2020

C. C. Lin, A. Y. Q. Huang, and S. J. H. Yang, “A review of AI-driven conversational chatbots implementation methodologies and challenges (1999–2022),” Sustainability, vol. 15, no. 5, 2023

W. Maeng and J. Lee, “Designing a chatbot for survivors of sexual violence: exploratory study for hybrid approach combining rule-based chatbot and ML-based chatbot,” 5th Asian CHI Symposium 2021, pp. 160–166, 2021

E. Mogaji, J. Balakrishnan, A. C. Nwoba, and N. P. Nguyen, “Emerging-market consumers’ interactions with banking chatbots,” Telematics and Informatics, vol. 65, 2021

K. Mrini, M. Laperrouza, and P. Dillenbourg, “Building a question-answering chatbot using forum data in the semantic space,” 2018 Swiss Text Conference, 2018

S. Mujeeb, M. Hafeez, and T. Arshad, “Aquabot: a diagnostic chatbot for achluophobia and autism,” International Journal of Advanced Computer Science and Applications, vol. 8, no. 9, 2017

V. Oguntosin and A. Olomo, “Development of an E-commerce chatbot for a university shopping mall,” Applied Computational Intelligence and Soft Computing, vol. 2021, 2021

R. Parkar, Y. Payare, K. Mithari, J. Nambiar, and J. Gupta, “AI and web-based interactive college enquiry chatbot,” in Proceedings of the 13th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2021, 2021

M. Rohandi, N. Husain, and I. W. Bay, “Usability testing of intensive course mobile application using the usability scale system,” ILKOM Jurnal Ilmiah, vol. 13, no. 3, pp. 252–258, 2021

S. A. Thorat and V. Jadhav, “A review on implementation issues of rule-based chatbot systems,” SSRN Electronic Journal, 2020

N. Uithayakumar and N. M. Rabi, “Teachers’ readiness to accommodate autistic children in the classrooms at international schools,” International Journal of Academic Research in Business and Social Sciences, vol. 10, no. 7, 2020

D. Wang and H. Fang, “Length adaptive regularization for retrieval-based chatbot models,” ICTIR 2020 - Proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 113–120, 2020

Y. Wang, N. Zhang, and X. Zhao, “Understanding the determinants in the different government AI adoption stages: evidence of local government chatbots in China,” Social Science Computer Review, vol. 40, no. 2, pp. 534–554, 2022

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International Journal of Advanced Artificial Intelligence Research

Deep Convolutional Neural Network-Based Adaptive Chatbot Framework for Personalized Educational Support in Autism Spectrum Disorder

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