Designing Emotionally Adaptive Chatbots for Diverse Users: A User-Centered Human-AI Interface Framework

Year 2026, Volume: 10 Issue: 1, 1 - 12, 16.12.2025

Priyanka Deshmukh , Bhavana Karmore , Mahendra Ingole , Kamal Upreti

https://doi.org/10.31127/tuje.1715271

Cited By: 1

Abstract

Recent advancements in conversational AI have improved task efficiency but often neglect the emotional and cognitive diversity of users. This research introduces a novel, user-centered framework for emotionally adaptive chatbots that integrates ML-based emotion recognition with personalized responses that are ethically filtered — meaning they are designed to respect user privacy, fairness, and transparency principles. The Berlin Emotional Speech Database (EmoDB) was used to train and evaluate three machine learning models using MFCC features. Among them, the XGBoost model achieved the highest classification accuracy of 77.6%, outperforming Random Forest (75.0%) and SVM (68.2%). To evaluate user experience, a dataset of 385 participants was generated using a 15-item Likert-scale questionnaire adapted from the UTAUT model and extended with trust and emotional alignment measures. Statistical tests, including a t-test (p = 0.711) between neurodiverse and non-neurodiverse users and an ANOVA (p = 0.337) across domains, confirmed the consistency and inclusivity of perceived satisfaction. Visual analytics, including correlation heatmaps and radar charts, revealed that users with predicted emotions such as happiness and neutral reported the highest satisfaction scores (mean = 4.49, SD = 0.29 and mean = 4.26, SD = 0.31, respectively). A seven-layered modular architecture was proposed, supporting real-time emotional adaptivity, personalization, and ethical compliance. The framework is integration-ready with NLP engines like GPT and Dialogflow, offering a scalable solution for affective AI deployment across healthcare, education, and public service domains.

Keywords

Remote sensing , UAV , Photogrammetry , DEM , Camera Calibration

References

• Nicolescu, L., & Tudorache, M. T. (2022). Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. Electronics, 11(10), 1579. Doi: https://doi.org/10.3390/electronics11101579.
• Brandtzaeg, P. B., & Følstad, A. (2018). Chatbots. Interactions, 25(5), 38–43. Doi: https://doi.org/10.1145/3236669.
• Piccolo, L. S., Mensio, M., & Alani, H. (2018). Chasing the Chatbots. Internet Science. In Lecture Notes in Computer Science, 157-169. Doi: https://doi.org/10.1007/978-3-030-17705-8_14
• Følstad, A., & Skjuve, M. (2019). Chatbots for customer service: user experience and motivation. In 1st international conference on conversational user interfaces, 1-9. Doi: https://doi.org/10.1145/3342775.3342784
• Følstad, A., Skjuve, M., & Brandtzaeg, P. B. (2019). Different chatbots for different purposes: towards a typology of chatbots to understand interaction design. In Lecture notes in computer science, 145–156. Doi: https://doi.org/10.1007/978-3-030-17705-8_13.
• Tong, B., Kuo, T. T., & Lin, C. (2024). Visualization-oriented Natural Language Interfaces for Grafana Dashboard. 2024 International Conference on Consumer Electronics-Taiwan (ICCE- Taiwan), 465–466. Doi: https://doi.org/10.1109/icce-taiwan62264.2024.10674281.
• Zhang, W., Wang, Y., Song, Y., Wei, V. J., Tian, Y., Qi, Y., Chan, J. H., Wong, R. C., & Yang, H. (2024). Natural Language Interfaces for tabular data querying and Visualization: a survey. IEEE Transactions on Knowledge and Data Engineering, 36(11), 6699–6718. Doi: https://doi.org/10.1109/tkde.2024.3400824.
• Tian, Y., Cui, W., Deng, D., Yi, X., Yang, Y., Zhang, H., & Wu, Y. (2024). ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language. IEEE Transactions on Visualization and Computer Graphics, 31(3), 1731–1745. Doi: https://doi.org/10.1109/tvcg.2024.3368621.
• Chen, S., Chen, L., Hu, B., Sun, S., Wang, Y., Wang, H., & Gao, W. (2023). User-Centric Access Network (UCAN) for 6G: motivation, concept, challenges, and key technologies. IEEE Network, 38(3), 154–162. Doi: https://doi.org/10.1109/mnet.135.2200587.
• Bu, L., Chen, C., Ng, K. K., Zheng, P., Dong, G., & Liu, H. (2020). A user-centric design approach for smart product-service systems using virtual reality: A case study. Journal of Cleaner Production, 280, 124413. Doi: https://doi.org/10.1016/j.jclepro.2020.124413.
• Wang, K. (2024). From ELIZA to ChatGPT: A brief history of chatbots and their evolution. Applied and Computational Engineering, 39(1), 57–62. Doi: https://doi.org/10.54254/2755- 2721/39/20230579.
• Bansal, G., Chamola, V., Hussain, A., Guizani, M., & Niyato, D. (2024). Transforming Conversations with AI—A Comprehensive Study of ChatGPT. Cognitive Computation, 16(5), 2487–2510. Doi: https://doi.org/10.1007/s12559-023-10236-2.
• Radanliev, P. (2024). Artificial intelligence: reflecting on the past and looking towards the next paradigm shift. Journal of Experimental & Theoretical Artificial Intelligence, 1–18. Doi: https://doi.org/10.1080/0952813x.2024.2323042.
• Singh, S. K., Kumar, S., & Mehra, P. S. (2023). Chat GPT & Google Bard AI: A Review. 2023 International Conference on IoT, Communication and Automation Technology (ICICAT). Doi: https://doi.org/10.1109/icicat57735.2023.10263706.
• Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022). Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design. International Journal of Human-Computer Studies, 161, 102788. Doi: https://doi.org/10.1016/j.ijhcs.2022.102788.
• Chaves, A. P., & Gerosa, M. A. (2020). How should my chatbot interact? A survey on Social Characteristics in Human–Chatbot Interaction Design. International Journal of Human-Computer Interaction, 37(8), 729–758. Doi: https://doi.org/10.1080/10447318.2020.1841438.
• Nguyen, Q. N., Sidorova, A., & Torres, R. (2021). User interactions with chatbot interfaces vs. Menu-based interfaces: An empirical study. Computers in Human Behavior, 128, 107093. Doi: https://doi.org/10.1016/j.chb.2021.107093.
• Ren, R., Castro, J. W., Acuña, S. T., & De Lara, J. (2019). Evaluation Techniques for Chatbot Usability: A Systematic Mapping study. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1673–1702. Doi: https://doi.org/10.1142/s0218194019400163.
• Siswanto, T., Shofiati, R., & Hartini, H. (2018). Acceptance and Utilization of Technology (UTAUT) as a Method of Technology Acceptance Model of Mitigation Disaster website. IOP Conference Series Earth and Environmental Science, 106, 012011. Doi: https://doi.org/10.1088/1755- 1315/106/1/012011.
• Mosleh, S. M., Alsaadi, F. A., Alnaqbi, F. K., Alkhzaimi, M. A., Alnaqbi, S. W., & Alsereidi, W. M. (2024). Examining the association between emotional intelligence and chatbot utilization in education: A cross-sectional examination of undergraduate students in the UAE. Heliyon, 10(11), e31952. Doi: https://doi.org/10.1016/j.heliyon.2024.e31952.
• Zhai, C., Wibowo, S., & Li, L. D. (2024). Evaluating the AI dialogue System’s intercultural, humorous, and empathetic dimensions in English language learning: A case study. Computers and Education Artificial Intelligence, 7, 100262. Doi: https://doi.org/10.1016/j.caeai.2024.100262.
• Le, M. T. H., Tran, C. M., Pham, T. T. M., Pham, C. N. L., & Nguyen, D. P. (2024). AI chatbot: increasing emotional efficacy in tourism through anthropomorphic theory of acceptance model. International Journal of Tourism Anthropology, 9(4), 300–331. Doi: https://doi.org/10.1504/ijta.2024.143397.
• Abdul, Z. K., & Al-Talabani, A. K. (2022). Mel Frequency Cepstral Coefficient and its Applications: A Review. IEEE Access, 10, 122136–122158. Doi: https://doi.org/10.1109/access.2022.3223444.
• Pawar, M. D., & Kokate, R. D. (2021). Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients. Multimedia Tools and Applications, 80(10), 15563–15587. Doi: https://doi.org/10.1007/s11042-020-10329-2.
• Uysal, M. P. (2024). A formal and integrated approach to engineering machine learning processes: A method base for project management. Turkish Journal of Engineering. Doi: https://doi.org/10.31127/tuje.1527734
• Chowdhury, J. H., Ramanna, S., & Kotecha, K. (2025). Speech emotion recognition with light weight deep neural ensemble model using hand crafted features. Scientific Reports, 15(1). Doi: https://doi.org/10.1038/s41598-025-95734-z.
• Balam, S. K., Jain, R., Alaric, J. S., Pattanaik, B., & Ayele, T. B. (2023). Renewable energy integration of IoT systems for smart grid applications. 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), 374–379. Doi: https://doi.org/10.1109/icesc57686.2023.10193428
• Pentari, A., Kafentzis, G., & Tsiknakis, M. (2024). Speech emotion recognition via graph-based representations. Scientific Reports, 14(1). Doi: https://doi.org/10.1038/s41598-024-52989-2.
• Meng, H., Yan, T., Yuan, F., & Wei, H. (2019). Speech emotion recognition from 3D Log-Mel spectrograms with deep learning network. IEEE Access, 7, 125868–125881. Doi: https://doi.org/10.1109/access.2019.2938007.
• Al-Farhani, L. H., Alqahtani, Y., Alshehri, H. A., Martin, R. J., Lalar, S., & Jain, R. (2023). IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City. Security and Communication Networks, 2023, 1–10. Doi: https://doi.org/10.1155/2023/3171334
• Venkatesh, N., Morris, N., Davis, N., & Davis, N. (2003). User acceptance of information Technology: toward a unified view. MIS Quarterly, 27(3), 425. Doi: https://doi.org/10.2307/30036540.
• Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. Doi: https://doi.org/10.1007/s10796-017- 9774-y.
• Awasthi, S., Srivastava, P. K., Kumar, N., Ojha, R. P., Pandey, P. S., Singh, R., Gehlot, A., Priyadarshi, N., Jain, R., & Bakare, Y. B. (2023). An epidemic model for the investigation of multi‐malware attack in wireless sensor network. IET Communications, 17(11), 1274–1287. Doi: https://doi.org/10.1049/cmu2.12622
• Jain, R., & Varshney, M. (2024). Group Key Management Protocols for Non-Network: A survey. Iraqi Journal for Electrical and Electronic Engineering, 20(1), 214–225. Doi: https://doi.org/10.37917/ijeee.20.1.21.
• Venkatesh, N., Thong, N., & Xu, N. (2012). Consumer Acceptance and use of Information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. Doi: https://doi.org/10.2307/41410412.
• Jain, R., & Varshney, M. (2023). A Trust-Based Group key management protocol for Non- Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 483–489. Doi: https://doi.org/10.17762/ijritcc.v11i6s.6956.
• Jain, R., Bakare, Y. B., Pattanaik, B., Alaric, J. S., Balam, S. K., Ayele, T. B., & Nalagandla, R. (2023). Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks. Sustainable Engineering and Innovation ISSN 2712-0562, 5(2), 161–176. Doi: https://doi.org/10.37868/sei.v5i2.id210
• Koruk, T., & Bilgin, T. T. (2025). Unveiling trends: a comprehensive analysis of state funded projects of Türkiye through content analysis and text mining. Turkish Journal of Engineering, 9(2), 237-249. Doi: https://doi.org/10.31127/tuje.1538068
• Ethiraj, N., Sivabalan, T., Sofia, J., … Harika, D. (2025). A comprehensive review on application of machine intelligence in additive manufacturing. Turkish Journal of Engineering, 9(1), 37-46. Doi: https://doi.org/10.31127/tuje.1502587
• Özkurt, C. (2024). Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. Turkish Journal of Engineering, 8(4), 656-665. Doi: https://doi.org/10.31127/tuje.1471445
• Sıngh, S., Kumar, K., & Kumar, B. (2024). Analysis of feature extraction techniques for sentiment analysis of tweets. Turkish Journal of Engineering, 8(4), 741-753. Doi: https://doi.org/10.31127/tuje.1477502