User-Centered Prompt Strategy Evaluation for Large Language Model Interaction
DOI:
https://doi.org/10.66238/ijrss.v6i2.117Keywords:
Large Language Models, Prompt Engineering, Human-Computer Interaction, Usability EvaluationAbstract
The integration of large language models into everyday computing environments has fundamentally transformed human-computer interaction, shifting the paradigm from deterministic command-line operations to probabilistic natural language discourse. As these models become ubiquitous, the strategies users employ to communicate their intents, commonly known as prompt engineering, have emerged as a critical determinant of system efficacy and user satisfaction. This paper presents a comprehensive, user-centered evaluation of various prompt strategies utilized in large language model interactions. By shifting the analytical focus from purely computational benchmarks to human-centric usability metrics, this research systematically investigates how different prompting techniques impact cognitive load, task completion efficiency, and overall user trust. Through a meticulously designed empirical study involving diverse participant demographics and multifaceted task scenarios, we construct a taxonomy of user intents and map them to optimal prompting frameworks. The findings reveal that while highly structured, multi-step prompt strategies yield superior technical outputs, they inadvertently impose substantial cognitive burdens on novice users, thereby reducing interaction fluidity. Conversely, adaptive prompting systems that dynamically scaffold user inputs demonstrate significant potential in bridging the gap between technical complexity and user accessibility. This study ultimately advocates for the integration of adaptive assistance mechanisms within user interfaces, providing actionable guidelines for developers to enhance the accessibility and reliability of conversational artificial intelligence agents in generalized domains.
References
1. Zhu, R., Ma, Z., Wu, J., Gao, J., Wang, J., Lin, D., & He, C. (2025, April). Utilize the flow before stepping into the same river twice: Certainty represented knowledge flow for refusal-aware instruction tuning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 26157-26165).
2. Wang, X., Suzuki, Y., Li, C. M., & Tanaka, S. (2025, June). Micropatterned PDMS damping layer integration to enhance the fidelity of piezoelectric MEMS speakers without SPL penalty. In 2025 23rd International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) (pp. 455-458). IEEE.
3. Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L. (2023). Secure transmission scheme based on joint radar and communication in mobile vehicular networks. IEEE transactions on intelligent transportation systems, 24 (9), 10027-10037.
4. Ou, J., Guo, J., Jiang, S., Wang, Z., Chen, Y., Xue, R., & Tian, W. (2025, June). Low-Rank Decomposition Assisted Quantization and Inference Compensation for Quality Large Language Model Inference. In 2025 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE.
5. Pei, Z., Huang, Q., & Wang, S. (2026). When LLMs develop languages: Symbolic communication for efficient multi-agent reasoning.arXiv preprint arXiv:2606.29354.
6. Li, Y., Bai, X., Liu, S., & Zhang, L. (2026). RepBench: Compiling benchmarks into capability representations for large language models. arXiv. https://arxiv.org/abs/2607.28008
7. Xu, X., Han, C., Zhang, Y., Tu, W., & Yang, Y. (2024, April). Curricular contrastive regularization for speech enhancement with self-supervised representations. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 10486-10490). IEEE.
8. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
9. Xie, Z., Liao, R., Wang, Z., Chen, C., Hua, X. S., & Luo, X. (2026, July). GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training. In Findings of the Association for Computational Linguistics: ACL 2026(pp. 10281-10293).
10. Zhu, R., Jiang, X., Wu, J., Ma, Z., Song, J., Bai, F., ... & He, C. (2025, April). GRAIT: gradient-driven refusal-aware instruction tuning for effective hallucination mitigation. In Findings of the Association for Computational Linguistics: NAACL 2025 (pp. 4006-4021).
11. Li, Y., Yang, Z., Huang, Y., Liu, X., & Qi, G. (2026, March). C³TG: Conflict-aware, Composite, and Collaborative Controlled Text Generation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 38, pp. 31834-31842).
12. Yao, Y., Xiao, W., Miao, P., Chen, G., Yang, H., Chae, C. B., & Wong, K. K. (2025). UAV-relay-aided secure maritime networks coexisting with satellite networks: robust beamforming and trajectory optimization. IEEE Transactions on Wireless Communications.
13. Lv, Z., Wang, Q., Lyu, T., & Dong, J. (2026). A Deep Learning-Based Model for User Emotion Recognition and Behavior Guidance Design: EMoGuideNet-An Emotion-Aware Multimodal Framework for Enhancing Intelligent User Interaction. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-30.
14. Xu, X., Wang, Y., Xu, D., Peng, Y., Zhang, C., Jia, J., & Chen, B. (2022, May). Vsegan: Visual speech enhancement generative adversarial network. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7308-7311). IEEE.
15. Sang, Y. (2025, October). AutoCrit: A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought. In 2025 6th International Conference on Machine Learning and Computer Application (ICMLCA) (pp. 1177-1180). IEEE.
16. Wei, W. D., Fang, H. B., Liu, Y. D., Shi, J. X., Kwok, J., & Li, Y. F. (2026). Activation Compression in LLMs: Theoretical Analysis and Efficient Algorithm. arXiv preprint arXiv:2605.01255.
17. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of EMNLP.
18. Guo, J., Shuang, K., Zhang, K., Liu, Y., Li, J., & Wang, Z. (2023, June). Learning to imagine: distillation-based interactive context exploitation for dialogue state tracking. In Proceedings of the AAAI conference on artificial intelligence (Vol. 37, No. 11, pp. 12845-12853).
19. Zhou, J., Shuang, K., An, Z., Guo, J., & Loo, J. (2023). Improving document-level event detection with event relation graph. Information Sciences, 645, 119355.
20. Han, C., Zheng, W., & Tang, X. (2025, November). Debate-to-detect: Reformulating misinformation detection as a real-world debate with large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 15125-15140).
21. Tang, J., Yuan, X., Liu, J., Yu, J., Dong, X., Chen, L., ... & Yin, J. (2026). SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition. arXiv preprint arXiv:2604.20146.
22. Gao, Z., Qu, Y., & Han, Y. (2025). Cross-Lingual Sponsored Search via Dual-Encoder and Graph Neural Networks for Context-Aware Query Translation in Advertising Platforms. arXiv preprint arXiv:2510.22957.
23. Xu, X., Tu, W., Yang, Y., Li, J., Zhang, Y., & Chen, H. (2026). Contribution-aware dynamic multi-modal balance for audio-visual speech separation.IEEE Transactions on Multimedia.
24. Yao, Y., Xiao, W., Miao, P., Chen, G., Yang, H., Chae, C. B., & Wong, K. K. (2026). UAV-RHS-enabled full-duplex ISAC covert system: Robust beamforming and trajectory optimization. IEEE Transactions on Communications.
25. Xiao, Q., & Zhou, K. (2026). MACD: Model-Aware Contrastive Decoding via Counterfactual Data. arXiv preprint arXiv:2602.01740.
26. Xu, X., Tu, W., Yang, Y., Li, J., & Zhang, Y. (2025). Interactive target positive and negative features modeling for monaural speech enhancement.IEEE Transactions on Audio, Speech and Language Processing,33, 4856-4869.
27. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
28. Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization.
29. Yang Y, Yang X, Jiang Y, et al. GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems[J]. arXiv preprint arXiv:2602.15776, 2026.
30. Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of NAACL-HLT.
31. Zhang, Y., Zhang, Y., Zhao, M., Feng, S., & Cheung, Y. M. (2026). How to Ask the AI: A User Perspective Survey for Large Language Model Prompting.IEEE Transactions on Artificial Intelligence.
32. Tang, J., Yang, Y., Yu, J., Wang, Z. X., Liang, H., Yao, L., & Yin, J. (2025, November). Unco: Uncertainty-driven collaborative framework of large and small models for grounded multimodal ner. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 7644-7662).
33. Liu, H., Ding, L., & Henao, R. (2026, July). Learning to Control Summaries with Score Ranking. In Findings of the Association for Computational Linguistics: ACL 2026(pp. 35336-35359).
34. Sang, Y. (2025, August). Robustness of fine-tuned llms under noisy retrieval inputs. In 2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) (pp. 417-420). IEEE.
35. Sang, Y. (2025, July). Towards explainable rag: Interpreting the influence of retrieved passages on generation. In 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) (pp. 397-400). IEEE.
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