Lirui Guo

I am a Postdoctoral Research Fellow in Human–Robot Interaction at Monash University, working with Monash Robotics and the ARC Industrial Transformation Training Centre (ITTC) in Optimal Ageing. I submitted my PhD thesis in Civil and Environmental Engineering at Monash University in August 2026; it is currently under examination. My doctoral research was based in the Institute of Transport Studies, supervised by Dr. Wynita Griggs and Dr. Michael Burke.

My doctoral research focused on user interaction with conversational AI in shared autonomous vehicles. Across a Predict–Interact–Measure–Control workflow, I studied psychological factors shaping user acceptance, open-domain interaction with LLM-enabled agents, automated measurement of conversational responses, and adaptive control of AI response attributes.

My current research extends this work from conversational human–AI interaction into embodied human–robot interaction, with a broader interest in adaptive, human-centred intelligent systems for optimal ageing.

Research Interests

Human–AI Interaction & Human–Robot Interaction: Human-centred interaction with conversational and embodied intelligent systems, including user preferences, trust, safety, and autonomy.
Adaptive Intelligent Systems: Measurement, modelling, and adaptive regulation of interactive AI behaviour in closed-loop, user-aware systems.
Intelligent Mobility & Optimal Ageing: Applications across autonomous mobility, assistive and social robotics, and technologies supporting optimal ageing.

news

September 2026	Joined Monash Robotics and the ARC ITTC in Optimal Ageing as a Postdoctoral Research Fellow in Human–Robot Interaction.
Feb 18, 2026	Mental Health First Aid (MHFA) Training – Free Workshops for Monash Students
Aug 11, 2025	AITPM newsletter: AI In Education \| Insights from an Academic Partner

latest posts

Dec 11, 2025	📓 Hybrid Thesis Formatting Guide for Word (Using PDF Instead of Screenshots)
Feb 18, 2026	⚙️ The Digital Toolkit: Resources for Personal Use
Feb 25, 2026	🖥️ PowerPoint Templates for Academic Presentations

selected publications

TRF

Evaluating the effectiveness of a behavior change wheel-informed intervention to reduce risky riding behaviors among delivery riders

F. Ye, C. Wang, Z. Zhang, T. Zhu, H. Dou, and Lirui Guo

Transportation Research Part F: Traffic Psychology and Behaviour, 2026

Bib HTML Other Collaborations

@article{ye2026evaluating,
  title = {Evaluating the effectiveness of a behavior change wheel-informed intervention to reduce risky riding behaviors among delivery riders},
  author = {Ye, F. and Wang, C. and Zhang, Z. and Zhu, T. and Dou, H. and Guo, Lirui},
  journal = {Transportation Research Part F: Traffic Psychology and Behaviour},
  volume = {120},
  pages = {103663},
  year = {2026},
  publisher = {Elsevier},
  doi = {10.1016/j.trf.2026.103663},
  url = {https://doi.org/10.1016/j.trf.2026.103663},
  research_tag = {Other Collaborations},
  research_url = {/projects/#other-collaborations},
}

A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles

Lirui Guo, Michael G. Burke, and Wynita M. Griggs

Technological Forecasting and Social Change, 2025

Bib PDF HTML PhD Research

@article{guo2025framework,
  title = {A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles},
  author = {Guo, Lirui and Burke, Michael G. and Griggs, Wynita M.},
  journal = {Technological Forecasting and Social Change},
  volume = {212},
  pages = {123960},
  year = {2025},
  publisher = {Elsevier},
  doi = {10.1016/j.techfore.2024.123960},
  url = {https://doi.org/10.1016/j.techfore.2024.123960},
  research_tag = {PhD Research},
  research_url = {/projects/#phd},
}

Proactive framework for evaluating retrieval-augmented generation-based learning assistants in engineering education

Linxin Hua, Lirui Guo, Nan Zheng, Y Lu, J Xu, and J Deng

Computer-Aided Civil and Infrastructure Engineering, 2025

Bib PDF HTML AI in Education

@article{hua2025proactive,
  title = {Proactive framework for evaluating retrieval-augmented generation-based learning assistants in engineering education},
  author = {Hua, Linxin and Guo, Lirui and Zheng, Nan and Lu, Y and Xu, J and Deng, J},
  journal = {Computer-Aided Civil and Infrastructure Engineering},
  year = {2025},
  publisher = {Wiley},
  doi = {10.1111/mice.70063},
  url = {https://doi.org/10.1111/mice.70063},
  research_tag = {AI in Education},
  research_url = {/projects/#ai-in-education},
}