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Bingyao Li

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Open access Jul 2026

Unpacking the dual-path trust mechanism driving students' continuance intention toward campus GenAI: a hybrid SEM-machine learning approach within the S-O-R framework

As generative artificial intelligence agents become embedded in higher education, understanding students' continuance intention (CUI) is critical. To address the underexplored mechanisms regarding how campus AI environments are associated with user trust, this study adopts the Stimulus-Organism-Response (S-O-R) framework to investigate a dual-path trust mechanism: AI System-like Trust (AST) and AI Human-like Trust (AHT). Through a hybrid methodology combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and non-parametric Random Forest classification, this study analyzes university students' CUI toward campus GenAI. The findings demonstrate that Information Quality (IQ) and Service Quality (SQ) are positively associated with both AST and AHT pathways. Conversely, Facilitating Conditions (FC) and Performance Expectancy (PE) do not exhibit independent linear associations within the simultaneous structural estimation. Rather than indicating an empirical absence of functional value, this configuration uncovers a holistic evaluation mechanism among digital natives, where basic functional affordances operate as baseline hygiene constraints whose variance is subsumed by quality attributes. Notably, while both AST and AHT exert concurrently significant linear driving forces on CUI within the structural path framework, the exploratory machine learning analysis clarifies that under recursive partitioning, AHT exhibits a higher relative predictive weight. Furthermore, a localized asymmetry operates within the linear mediation channels, where SQ selectively mobilizes AHT over AST to drive continuous usage. Post-hoc inspections imply that PE operates through a non-linear threshold dynamic, transitioning into an active predictive catalyst primarily within the positive spectrum of user perception. Our study contributes to campus AI services evaluation by highlighting that within the contemporary GenAI ecosystem, students' evaluative focus prioritizes relational and epistemic quality over functional accessibility. Campus application administrators and developers are advised to prioritize content rigor and responsive, empathetic interaction design to cultivate the relational trust relevant for sustained engagement.

Bingyao Li, T. Yu · 0 citations
Review Jul 2026

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer. Although artificial intelligence methods have advanced flood prediction and model-error correction, most existing studies have not explicitly represented the tacit expert rules, review checkpoints, and workflow constraints that connect model outputs to operational warning decisions. To address this issue, we propose HydroAgent, a skill-orchestrated agent framework that embeds Large Language Models (LLMs) into a model-driven flood forecasting workflow, where each skill encodes explicit rules to bound LLM reasoning. We validated its effectiveness using five state-of-the-art LLMs in the South Yamhill River basin. Our results demonstrate that prior judgment captures observed peak flow and flood volume within 5% tolerance in 10 and 11 out of 14 events, with 5-fold cross-validation over 129 events yielding Pearson correlations of 0.62 and 0.84. Building on a high-baseline scheme library (average KGE 0.890), the guided scheme selection further improves KGE by 0.023-0.154, with simulated peak flow and flood volume falling within the prior judgment ranges for 14 and 13 out of 14 events. All five tested LLMs successfully execute the HydroAgent workflow with comparable judgment accuracy (40%-80%), while showing moderate performance variation and substantial cost differences. HydroAgent does not aim to replace human forecasters; instead, it translates their tacit expertise into an auditable and reproducible workflow, streamlining analytical steps and supporting more informed decision-making. This skill-orchestrated paradigm demonstrates how explicit rule boundaries can guide language model reasoning to complement physically based simulation in next-generation flood forecasting.

Qingyi Yang, S. Qiu, Bingyao Li et al. · 0 citations