Published July 7, 2025 | Version v1
Conference paper Open

Optimizing Directional Stimulus Prompting Through Human Feedback: A Structured Approach to AI-Powered Scaffolding

  • 1. ROR icon University of Hong Kong

Description

Abstract

This paper introduces Optimizing Directional Stimulus Prompting Through Human Feedback (oDSP-HF), a structured approach to AI-powered scaffolding that enhances LLM-driven educational support. By refining ‘Directional Stimulus Prompting’ (DSP) through user interaction, oDSP-HF enables LLMs to generate adaptive, reflective hints rather than direct answers. This approach was applied in the system prompting of two AI agents—Aiza and Alice, designed to support Academic English writing and computational thinking, respectively—demonstrating its practical applications in education.

Files

Optimizing Directional Stimulus Prompting Through Human Feedback.pdf

Files (475.7 kB)

Additional details

Related works

Is published in
Conference proceeding: 10.25442/hku.29476520 (DOI)

Dates

Available
2025-07-08
Online

References

  • Chen, B., Zhu, X., & Díaz del Castillo H, F. (2023). Integrating generative AI in knowledge building. Computers and Education: Artificial Intelligence, 5, 100184. https://doi.org/10.1016/j.caeai.2023.100184
  • Hicks, M. T., Humphries, J., & Slater, J. (2024). ChatGPT is bullshit. Ethics and Information Technology, 26(2), 38. https://doi.org/10.1007/s10676-024-09775-5
  • Hijón-Neira, R., Connolly, C., Pizarro, C., & Pérez-Marín, D. (2023). Prototype of a Recommendation Model with Artificial Intelligence for Computational Thinking Improvement of Secondary Education Students. Computers (Basel), 12(6), 113. https://doi.org/10.3390/computers12060113
  • Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv [cs.CL]. https://doi.org/10.48550/arXiv.2106.09685
  • Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T.,…Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  • Khosravi, H., Viberg, O., Kovanovic, V., & Ferguson, R. (2023). Generative AI and Learning Analytics. Journal of Learning Analytics, 10(3), 1-6. https://doi.org/10.18608/jla.2023.8333
  • Li, Z., Peng, B., He, P., Galley, M., Gao, J., & Yan, X. (2023). Guiding Large Language Models via Directional Stimulus Prompting. arXiv [cs.CL]. https://doi.org/10.48550/arXiv.2302.11520
  • Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023). Reasoning with Language Model Prompting: A Survey. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), 61(1), 5368-5393. https://doi.org/10.18653/v1/2023.acl-long.294
  • Zhang, L., Ergen, T., Logeswaran, L., Lee, M., & Jurgens, D. (2024). SPRIG: Improving Large Language Model Performance by System Prompt Optimization. arXiv [cs.CL]. https://doi.org/10.48550/arXiv.2410.14826
  • Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z.,…Wen, J.-R. (2023). A Survey of Large Language Models. arXiv [cs.CL]. https://doi.org/10.48550/arXiv.2303.18223