Publications

Journal Papers (9)

2025

October

1. CALL

   Breaking the boundaries of conventional vocabulary learning: the impact of vocabulary learning with intelligent interactive companion in CAVL environments on learning outcomes, motivation, and flow experience

   Yuxi Zhang, Haoming Wang^*, and Chunjia Bao

   Computer Assisted Language Learning, Oct 2025

   Abs DOI HTML

   

   Vocabulary acquisition is the foundation of language learning, and Computer-Assisted Vocabulary Learning (CAVL) environments offer new possibilities for enhancing learning efficiency. However, conventional CAVL environments often employ static learning content and limited interaction mechanisms, making it difficult to accommodate individual learner differences and cognitive needs, which leads to insufficient motivation and suboptimal memory retention. This study proposed an Intelligent Interactive Companion-assisted Vocabulary Learning (IIC-VL) approach, which was based on multi-agent technology and dynamically adjusted learning content according to learners’ proficiency levels, learning styles, and emotional states. An experimental design was adopted, with 100 students recruited from a high school and randomly assigned to an experimental group (n = 49) and a control group (n = 51). The experimental group used the IIC-VL approach for vocabulary learning, while the control group used the conventional CAVL approach. The intervention lasted for 12 wk, with two learning sessions per week, each lasting 45 min. The results showed that, compared to the control group, the experimental group demonstrated significant improvements in vocabulary tests (both receptive and productive), motivation for learning, and flow experience. Subsequent semi-structured interviews revealed students ’ positive attitudes and perceptions toward the IIC-VL approach. These findings not only provided empirical support for the application of multi-agent technology in CAVL environments but also offered a new practical direction for personalized learning paths in foreign language vocabulary instruction.

April

1. IJHCI

   Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy

   Chengliang Wang, Haoming Wang, Yuanyuan Li, and 3 more authors

   International Journal of Human–Computer Interaction, Apr 2025

   ESI Highly Cited & Hot Paper Abs DOI

   

   ESI Highly Cited & Hot Paper

   Generative artificial intelligence (GAI) advancements have ignited new expectations for artificial intelligence (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze the influencing factors of Chinese university students’ GAI technology usage intention. Regarding AI literacy, students’ cognitive literacy in AI ethics scored the highest (M = 5.740), while AI awareness literacy scored the lowest (M = 4.578). Students’ attitudes toward GAI significantly and positively influenced their usage intention, with the combined TPB framework and AI literacy explaining 59.3% of the variance. AI literacy and subjective norms positively influenced students’ attitudes toward GAI technology and perceived behavioral control, and attitude mediated the impact of AI literacy and subjective norms on GAI usage intention. Further, the interviews provide new insights for university management and educational leadership regarding the construction of an educational ecosystem under the application of GAI technology.

March

1. EAIT

   Impact of AI-agent-supported collaborative learning on the learning outcomes of University programming courses

   Haoming Wang, Chengliang Wang, Zhan Chen, and 3 more authors

   Education and Information Technologies, Mar 2025

   Abs DOI

   

   With the rapid development of artificial intelligence technology in the field of education, AI-Agents have shown tremendous potential in collaborative learning. However, traditional Computer-Supported Collaborative Learning (CSCL) methods still have limitations in addressing the unique demands of programming education. This study proposes an innovative AI-Agent-supported Collaborative Learning (AI-CL) approach, aimed at enhancing students’ programming learning experience and effectiveness through intelligent assistance from AI-Agents. The research developed an AI-Agent system based on large language models and employed a quasi-experimental design to investigate the effects of the AI-CL method on students’ learning achievement, self-efficacy, cognitive load, and learning interest. The experiment was conducted at a university in Shanghai, recruiting 45 undergraduate students who were randomly assigned to the AI-CL experimental group (n = 24) and the CSCL control group (n = 21). The experimental context was set in an ACM programming competition teaching environment, lasting for 6 weeks with 70-min sessions each week. Results showed that the AI-CL group significantly outperformed the CSCL group in learning achievement, self-efficacy, and learning interest. Regarding cognitive load, the AI-CL group demonstrated significantly lower mental effort compared to the CSCL group, while there was no significant difference in mental load. These findings not only provide new theoretical perspectives for the application of cognitive load theory and self-efficacy theory in AI-assisted learning environments but also offer strong practical guidance for higher education institutions to introduce AI-Agent-assisted collaborative learning models in programming courses.

February

1. JECR

   MetaClassroom: A new paradigm and experience for programming education

   Chengliang Wang, Xiaojiao Chen, Yifei Li, and 3 more authors

   Journal of Educational Computing Research, Feb 2025

   Abs DOI

   

   This study explored the impact of MetaClassroom, a virtual immersive programming learning environment designed based on the three-dimensional learning progression (3DLP) concept, on students ’ multidimensional development. Utilizing a quasi-experimental research design, this study compared students ’ programming learning achievements (PLA), self-regulated learning (SRL) skills, learning beliefs, and learning motivation in MetaClassroom with those in traditional classroom settings. The findings revealed that MetaClassroom significantly enhanced students ’ PLA and SRL skills, particularly in subdimensions including Metacognitive Skills, Persistence, and Seeking Help. Additionally, MetaClassroom positively impacted students ’ learning beliefs and learning motivation, demonstrating its potential in optimizing knowledge acquisition, application processes, and fostering students ’ higher-order thinking skills. By integrating the 3DLP concept into targeted programming learning environments, MetaClassroom created an innovative immersive learning ecosystem, that bridged theory and practice, offering students a comprehensive and engaging platform to develop both foundational knowledge and practical skills. This study not only validated the effectiveness of MetaClassroom in improving students ’ programming learning performance and learning experience, but also introduced a new paradigm that integrated teaching, learning, and assessment. The paradigm offers new insights and directions for both programming education and broader educational practices, paving the way for future developments in educational technology.

2. 国家智慧教育平台持续使用的影响因素与推进策略

   徐显龙, 陈文慧, 王浩名, and 1 more author

   中国教育信息化, Feb 2025

   Abs HTML

   "国家智慧教育平台是推动中小学校教育数字化转型的重要抓手,然而现阶段平台使用还存在资源应用浅层化、使用覆盖不均衡等挑战,深入研究持续使用平台的影响因素显得尤为重要.为此,首先以技术影响因素框架为理论支撑,围绕推进组织技术使用的动力层、推动层和反应层提出中小学校持续使用平台的推进模型.接着,基于该模型展开中小学校使用平台的影响要素分析,结果表明政府支持、校级领导态度、技术复杂性、技术的相对优势对学校技术使用均有正向影响,同时,现阶段的技术使用中尚存在开展的教学活动较单一、技术使用与现阶段工作不够适配、缺乏有效实时的技术反馈三个问题.最后,提出以校级领导为示范组织深度使用平台、以各类政策为基准构建技术使用环境、从师生实况出发将技术有机融入教学、将教学实效作为实践反馈的重要内容的推进策略"

2024

December

1. Annotated emotional image datasets of Chinese university students in real classrooms for deep learning

   Chengliang Wang, Haoming Wang, Zihui Hu, and 1 more author

   Data in Brief, Dec 2024

   DOI

   

February

1. HSSC

   What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study

   Yuhui Jing, Haoming Wang, Xiaojiao Chen, and 1 more author

   Humanities and Social Sciences Communications, Feb 2024

   ESI Highly Cited & Hot Paper Abs DOI HTML

   

   ESI Highly Cited & Hot Paper

   The emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners ’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners ’ effectiveness in applying ChatGPT to solve programming problems. Learners ’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners ’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners ’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.

2023

September

1. EAIT

   Bibliometric mapping techniques in educational technology research: A systematic literature review

   Yuhui Jing, Chengliang Wang, Yu Chen, and 3 more authors

   Education and Information Technologies, Sep 2023

   Abs DOI HTML

   

   Bibliometric mapping is widely used in educational technology research to visualize research field development (e.g. the current status and trend). However, there has been limited research examining the present state, challenges, and potential applications of bibliometric mapping techniques in the field of educational technology. In an effort to bridge this knowledge gap, this study conducted a systematic review of 71 articles on bibliometric mapping sourced from 24 journals specializing in educational technology. This review serves as a comprehensive analysis (i.e. a meta-review) of existing reviews in the field. The review focused on productivity, research topics, and research norms. The following key findings were obtained in the study: (1) The field has seen rapid development, with notable author groups, core journals, and major publishing countries; (2) Bibliometric mapping is mainly used for quantitative analysis in five research topics, including specific journals, emerging technologies, learning environments, online and distance learning, and subject concepts; (3) Statistics and evaluations of tools including Bibliometrix package of R language and VOSviewer, databases such as WoS and Scopus, as well as the procedural aspects of the research undertaken, were all considered. Additionally, the study investigated suggested benchmarks for these methods – investigating the availability of recognized reporting guidelines like PRISMA or others, as well as the presence of specific criteria for determining what should be included or excluded when utilizing bibliometric mapping techniques. The study proposed standards for the application of bibliometric mapping techniques in research, accompanied by relevant examples. This study not only reveals the specific value and current applications of bibliometric mapping techniques in educational technology research but also provides clarity on the specific methodologies for their implementation and identifies potential areas for further exploration. Finally, this study puts forth a number of recommendations aimed at fostering the reasoned utilization of bibliometric mapping techniques in the field of educational technology.

February

1. Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022

   Yuhui Jing, Leying Zhao, Keke Zhu, and 3 more authors

   Sustainability, Feb 2023

   

Conference Papers (6)

2025

November

1. EMNLP2025

   Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics

   Sheng Jin, Haoming Wang^*, Zhiqi Gao, and 3 more authors

   In Findings of the Association for Computational Linguistics: EMNLP 2025, Nov 2025

   Abs DOI HTML

   

   Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics. Addressing the fragmented issues in teaching process modeling and the limitations of agents performance in simulating diverse educational participants, AAS constructs the Zero-Exp strategy, employs a continuous “experience-reflection-optimization” cycle, grounded in a dual memory base comprising experience and knowledge bases and incorporating short-term and long-term memory components. Through this mechanism, agents autonomously evolve via situated interactions within diverse simulated school scenarios. This evolution enables agents to more accurately model the nuanced, multi-faceted teacher-student engagements and underlying learning processes found in physical schools. Experiment confirms that AAS can effectively simulate intricate educational dynamics and is effective in fostering advanced agent cognitive abilities, providing a foundational stepping stone from the “Era of Experience” to the “Era of Simulation” by generating high-fidelity behavioral and interaction data.

July

1. AIED2025

   Prediction of Online Mathematics Test Efficiency Based on Stacked Integrated Models: A Case Study of NAEP Data

   Chengliang Wang, Haoming Wang^*, Yutong Lai, and 2 more authors

   In International Conference on Artificial Intelligence in Education, Jul 2025

   Abs DOI HTML

   

   This study based on data from the National Assessment of Educational Progress (NAEP) in the United States, developed an online mathematics test efficiency prediction model using stacked ensemble learning. The research compared the performance of two machine learning algorithms—Random Forest and Gradient Boosting—and combined them to construct a stacked ensemble model. Through ten-fold cross-validation, the model achieved an average F1 score of 74.92% and a recall rate of 77.45%. Feature importance analysis based on SHAP (SHapley Additive exPlanations) indicated that question type played a dominant role in predicting student test efficiency, while learning behavior features had a relatively small impact. This study innovatively integrated stacked ensemble learning with explainable artificial intelligence techniques, enhancing prediction accuracy while providing reliable support for educational decision-making.

June

1. ISLS2025

   The Future Landscape of Education: Constructing an Analytical and Application Framework for Generative Artificial Intelligence in Higher Education

   Zihan Xiao, Chengliang Wang, Yutong Lai, and 2 more authors

   In International Society of the Learning Sciences, Jun 2025

   Abs DOI HTML

   

   The rapid evolution of artificial intelligence technologies is reshaping the paradigm of educational technology applications. Against this backdrop, the application of AI agents in the field of education has emerged as a cutting-edge topic in academia. However, existing studies lack a systematic theoretical framework. To address this research gap, this study employs bibliometric methods to analyze relevant literature from the Scopus database spanning 2004 to 2024, constructing a comprehensive knowledge map of AI agent research in education. Through multidimensional analytical approaches, including co-occurrence analysis, clustering analysis, and burst analysis, the study systematically examines the developmental trajectory and evolutionary pathways of AI agents in education. Overall, this research presents a clear framework of existing studies on AI agents in education, offering deep insights into the intelligent transformation of educational technology.

2. ISLS2025

   Knowledge Mapping and Trend Analysis of AI Agents in Education: A Bibliometric Study from 2004-2024

   Haoming Wang, Zihan Xiao, Yutong Lai, and 2 more authors

   In Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025, pp. 1404-1408, Jun 2025

   Abs DOI HTML

   

   The rapid evolution of artificial intelligence technologies is reshaping the paradigm of educational technology applications. Against this backdrop, the application of AI agents in the field of education has emerged as a cutting-edge topic in academia. However, existing studies lack a systematic theoretical framework. To address this research gap, this study employs bibliometric methods to analyze relevant literature from the Scopus database spanning 2004 to 2024, constructing a comprehensive knowledge map of AI agent research in education. Through multidimensional analytical approaches, including co-occurrence analysis, clustering analysis, and burst analysis, the study systematically examines the developmental trajectory and evolutionary pathways of AI agents in education. Overall, this research presents a clear framework of existing studies on AI agents in education, offering deep insights into the intelligent transformation of educational technology.

May

1. CITERS2025

   AI-Agent School: Construction and Evaluation of an Educational Simulation Environment Based on LLM-Driven Agents

   Haoming Wang, Chengliang Wang, and Chunjia Bao

   In , May 2025

   Abs HTML

   

   This study constructs the AI-Agent School (AAS) simulation environment, aiming to leverage LLM-driven agents to optimize teaching strategies and enhance both teaching and learning outcomes. Additionally, the Zero-Exp strategy is introduced for knowledge base accumulation, significantly improving the capabilities of AI-agents. Experimental results show that students in AAS achieved an overall score of 78.9, outperforming the human teacher control group. This research provides insights and practical references for exploring innovative applications of AI in education.

2024

June

1. ISLS2024

   Constructing an action model for educational systems in response to pandemics: A textual grounded analysis

   Yufan Chen, Haoming Wang, Xiaojiao Chen, and 2 more authors

   In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 979-982, Jun 2024

   DOI