As artificial intelligence systems become increasingly embedded in everyday human-computer interaction contexts, user expectations regarding their social and emotional capabilities are gradually shifting from a primary focus on functional efficiency toward more complex dimensions such as empathetic experience, trust formation, and contextual sensitivity. Nevertheless, despite the notable progress achieved by large language models in terms of linguistic fluency, such systems often remain confined to surface-level simulations of empathy. In response to this limitation, the present study investigates an emotion-adaptive human-AI interface that integrates real-time affect recognition, dynamic user profiling, and context-aware response modulation within a unified framework. Through comparative user study encompassing task-oriented, social, and emotionally supportive scenarios, the results suggest that emotion-adaptive mechanisms may, to some extent, enhance users’ perceived empathy, trust, and engagement.
References
[1] Hao, Y. C., Chen, L. W., Li, J., Guo, Y., Su, X., Shu, M., Yan, C. H. (2021) Metal-organic framework membranes with single-atomic centers for photocatalytic CO2 and O2 reduction. Nature Communications, 12(1), 2682.
[2] Yin, M. (2025) Data quality control in semiconductor manufacturing through automated ETL processes and class imbalance handling techniques. Journal of Industrial Engineering and Applied Science, 3(6), 13-22.
[3] Liu, J., Wang, H., Sun, W., Liu, Y. (2022) Prioritizing autism risk genes using personalized graphical models estimated from single-cell rna-seq data. Journal of the American Statistical Association, 117(537), 38-51.
[4] Lin, A. (2025) Toward regulatory compliance in DAO governance: from regulatory rule engines to on-chain audit report generation. Journal of World Economy, 4(6), 12-20.
[5] Chen, Y. (2025) Interpretable automated machine learning for asset pricing in US capital markets. Journal of Economic Theory and Business Management, 2(5), 15-21.
[6] Yin, M. (2025) A data-driven approach for real-time bottleneck detection and optimization in semiconductor manufacturing using active period method and visualization. Academic Journal of Natural Science, 2(4), 19-26.
[7] Yin, M. (2025) Defect prediction and optimization in semiconductor manufacturing using explainable AutoML. Academic Journal of Natural Science, 2(4), 1-10.
[8] Chen, Y. (2025) A comparative study of machine learning models for credit card fraud detection. Academic Journal of Natural Science, 2(4), 11-18.
[9] Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., Du, B. (2019) Multistep flow prediction on car-sharing systems: a multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727-1740.
[10] Liu, Z. (2025) Reinforcement learning for prompt optimization in language models: a comprehensive survey of methods, representations, and evaluation challenges. ICCK Transactions on Emerging Topics in Artificial Intelligence, 2(4), 173-181.
[11] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and improvement methods of deep learning ability in blended learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.
[12] Wang, H., Li, Q., Liu, Y. (2022) Regularized Buckley-James method for right‐censored outcomes with block‐missing multimodal covariates. Stat, 11(1), e515.
[13] Wang, H., Li, Q., Liu, Y. (2023) Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.
[14] Gupta, K., Zhang, Y., Gunasekaran, T. S., Krishna, N., Pai, Y. S., Billinghurst, M. (2024) Caevr: Biosignals-driven context-aware empathy in virtual reality. IEEE Transactions on Visualization and Computer Graphics, 30(5), 2671-2681.
[15] Chen, Y. (2025) Artificial intelligence in economic applications: stock trading, market analysis, and risk management. Journal of Economic Theory and Business Management, 2(5), 7-14.
[16] Malchanau, A., Petukhova, V., Bunt, H. (2018) Towards integration of cognitive models in dialogue management: Designing the virtual negotiation coach application. Dialogue & Discourse, 9(2), 35-79.
[17] Chen, Y. (2025) Generative diffusion models for option pricing: a novel framework for modeling volatility dynamics in us financial markets. Journal of Industrial Engineering and Applied Science, 3(6), 23-29.
[18] Lin, A. (2025) Low-barrier pathways for traditional financial institutions to access Web3: compliant wallet custody and asset valuation models. Frontiers in Management Science, 4(6), 80-86.
[19] Wang, P., Wang, H., Li, Q., Shen, D., Liu, Y. (2024) Joint and individual component regression. Journal of Computational and Graphical Statistics, 33(3), 763-773.
[20] Jin, Y., Li, Z., Zhang, C., Cao, T., Gao, Y., Jayarao, P., Yin, B. (2024) Shopping mmlu: a massive multi-task online shopping benchmark for large language models. Advances in Neural Information Processing Systems, 37, 18062-18089.
[21] Chen, Y. (2025) Leveraging LSTM networks for vehicle stability prediction: a comparative analysis with traditional models under dynamic load conditions. Computing and Interdisciplinary Science, 1(2), 15-22.
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Su, Q. (2025) Designing Emotion-adaptive Human – AI Interfaces: An Empirical Study on Empathy, Trust, and Context-aware Interaction. Scientific Research Bulletin, 2(5), 10-17. https://doi.org/10.71052/srb2024/LJKK8501