Author

Tian-Yi Bai

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Open access Sep 2026

From System Characteristics to Online Learning Satisfaction: An Outcome-Oriented Learning Experience Pathway for AI-Based E-Learning Systems in Higher Education

Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs.

Jia-Yuan Guo, Jiu-Yang Ren, Zhao-Lin Lu et al. · Systems · 0 citations

#artificial intelligence Preprint Sep 2026

IWC-Bench: Evaluating Web Application Generation from a Software Testing Perspective

Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automated evaluation remains challenging. Static benchmarks can credit functionality that exists in source code but is unreachable at runtime. Interactive benchmarks exercise the application, yet incomplete exploration can cause them to miss implemented functionality and confound application defects with agent execution failures. To address these limitations, we propose IWC-Bench, an interactive benchmark for evaluating web application generation from a software testing perspective. IWC-Bench instruments each generated application and uses code coverage to guide an agent in exploring its functionality through user-simulated interactions. It then abstracts the interaction trace into a state-transition graph and evaluates the application along three dimensions: visual aesthetics, usability, and requirement alignment. By separating exploration from scoring, IWC-Bench collects runtime evidence without constraining exploration to predefined acceptance criteria. IWC-Bench comprises 369 real-world user requirements and 5,088 acceptance criteria. Evaluation of 17 frontier LLMs reveals distinct strengths across the three dimensions, with no model leading on every dimension. On 197 validated sessions sampled from an internal arena, IWC-Bench achieves 85.3\% agreement with human preferences, with agreement generally increasing as the score difference between paired applications grows. Further experiments show that coverage guidance improves exploration coverage and the model rankings remain stable when the judge model is replaced.

Chen-Xu Liu, Zi-Lu Zou, Pei-Zhong Gao et al. · 0 citations