Evaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC) DOI
Qilin Xuan

Опубликована: Апрель 18, 2025

Abstract In recent years, the rapid development of artificial intelligence (AI) technology has highlighted growing potential Artificial Intelligence Generated Content (AIGC) in field education. university-level English instruction, traditional teaching models often fail to meet increasing demand for oral practice and contextual communication skills among students. This study, grounded theoretical framework digital scenario-based leveraging AIGC technology, designed implemented a model tailored conversation instruction higher Through an empirical investigation into outcomes—including dimensions such as student learning performance, communicative competence improvement, instructional satisfaction—the findings demonstrate that AIGC-driven significantly enhances students' comprehensive language application while stimulating their interest active engagement learning. Moreover, this study identifies technical bottlenecks pedagogical challenges encountered during implementation, proposing optimization strategies providing valuable insights intelligent evolution university teaching.

Язык: Английский

Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy DOI
Chengliang Wang, Haoming Wang, Yuanyuan Li

и другие.

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Июль 29, 2024

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

Язык: Английский

Процитировано

37
A meta-analysis of learners’ continuance intention toward online education platforms DOI
Jian Dai,

X. Zhang,

Chengliang Wang

и другие.

Education and Information Technologies, Год журнала: 2024, Номер 29(16), С. 21833 - 21868

Опубликована: Май 4, 2024

Язык: Английский

Процитировано

15
Deconstructing University Learners' Adoption Intention Towards AIGC Technology: A Mixed‐Methods Study Using ChatGPT as an Example DOI Open Access
Chengliang Wang, Xiaojiao Chen, Zhebing Hu

и другие.

Journal of Computer Assisted Learning, Год журнала: 2025, Номер 41(1)

Опубликована: Янв. 15, 2025

ABSTRACT Background ChatGPT, as a cutting‐edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and equity. Existing studies have not fully explored learners' intentions adopt artificial intelligence generated content (AIGC) technology, highlighting need for deeper insights into factors influencing adoption. Objectives This study aims investigate higher education adoption towards AIGC with focus on understanding underlying reasons future prospects its application education. Methods The research divided two phases. First, an exploratory analysis involving practical activities interviews develops action decision framework Second, confirmatory using fuzzy‐set qualitative comparative 233 valid questionnaires identifies six configurations associated high intentions, emphasising roles of AI literacy perceived behavioural control. Results Conclusions reveals key adoption, including importance It provides actionable educators learners prepare effectively integrate ensuring equitable adaptive practices.

Язык: Английский

Процитировано

5
Acceptance of or resistance to facial recognition payment: A systematic review DOI Open Access
Teng Yu, Chengliang Wang,

晴子 渡辺

и другие.

Journal of Consumer Behaviour, Год журнала: 2024, Номер 23(6), С. 2933 - 2951

Опубликована: Авг. 1, 2024

Abstract With increasing evidence supporting the use of biometric identification methods for authentication, this study aims to enhance our understanding factors influencing acceptance and resistance facial recognition payment (FRP) systems. To provide a comprehensive review these factors, we conducted systematic literature (SLR) empirical studies. We examined 22 key research articles from an initial pool 1372 publications, identifying 37 that influence consumer or FRP. These were categorized into usage‐related aspects, attitudes evaluations, user‐related traits, privacy security concerns, other factors. Our findings reveal most frequently cited include performance expectancy, effort perceived usefulness, ease use. are crucial in contexts where FRP can increase productivity by providing prompt information effective assistance. This proposes collective model determinants resistance, integrating theoretical frameworks findings. The emphasizes context‐dependency user acceptance, highlighting importance addressing both technological psychological It incorporates usage characteristics, which mediated evaluations. proposed provides framework FRP, guiding service providers developing strategies adoption, with future needed refine assess further.

Язык: Английский

Процитировано

10
Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model DOI
Lei Du, Bingbing Lv

Education and Information Technologies, Год журнала: 2024, Номер unknown

Опубликована: Июнь 13, 2024

Язык: Английский

Процитировано

8
A systematic literature review on the application of generative artificial intelligence (GAI) in teaching within higher education: Instructional contexts, process, and strategies DOI
Peijun Wang, Yuhui Jing, Shusheng Shen

и другие.

The Internet and Higher Education, Год журнала: 2025, Номер unknown, С. 100996 - 100996

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Unveiling learners’ intentions toward influencer-led education: an integration of qualitative and quantitative analysis DOI
Xiaojiao Chen, Teng Yu, Jian Dai

и другие.

Interactive Learning Environments, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

1
MetaClassroom: A New Paradigm and Experience for Programming Education DOI Creative Commons
Chengliang Wang, Xiaojiao Chen, Yifei Li

и другие.

Journal of Educational Computing Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

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

Язык: Английский

Процитировано

1
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies DOI Creative Commons
Ruiqi Deng,

Mingyu Jiang,

Xiao Yu

и другие.

Computers & Education, Год журнала: 2024, Номер unknown, С. 105224 - 105224

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

6
Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed Learning in AI-Enhanced Educational Environments DOI Creative Commons
Di Wu,

Shuling Zhang,

Zhiyuan Ma

и другие.

Systems, Год журнала: 2024, Номер 12(9), С. 332 - 332

Опубликована: Авг. 29, 2024

This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive environments. The advent of AI has revolutionized environments, offering unprecedented opportunities for personalized and adaptive education. Generative supports teachers delivering smart education, enhancing acceptance technology, providing personalized, experiences. Nevertheless, application higher education is underexplored. explores how these AI-driven platforms impact abilities, focusing on key teacher support, strategies, technology acceptance. Through a quantitative approach involving surveys 306 undergraduates, we identified motivation, technological familiarity, quality interaction. findings reveal mediating roles self-efficacy motivation. Also, confirmed that improvements support strategies within AI-enhanced environments contribute to increasing self-efficacy, acceptance, contributes uncovering can inform design more effective educational technologies enhance student autonomy outcomes. Our theoretical model research deepen understanding applying while important contributions managerial implications.

Язык: Английский

Процитировано

5