Research on instructional factors acceptance in AIGC-assisted visual communication design courses : construction and validation of a VAM model

다국어 초록 (Multilingual Abstract)

With the widespread application of AIGC technology in visual communication design education, how to enhance students' acceptance in courses has become a key issue. However, existing research still lacks a systematic classification and empirically verifiable theoretical framework for instructional factors in AIGC-assisted design courses, making it difficult to effectively guide teaching design and intervention. To address this gap, this study adopts a mixed-methods approach. Candidate instructional factors were extracted through a literature review, an open-ended questionnaire （N=46）, and semi-structured interviews with experts （N=12）. Based on a sample of 1,200 students, a questionnaire survey was conducted. Exploratory factor analysis （EFA） and confirmatory factor analysis （CFA） were used to construct and validate the model, while independent samples t-tests and one-way ANOVA were employed to examine subgroup differences. The results show that instructional acceptance factors in AIGC-assisted visual communication design courses can be summarized into five dimensions: Instructional and Resource Support （IRS）, Creativity and Interdisciplinary Innovation （CII）, Technical Functionality and Usability （TFU）, Privacy and Limitations （PL）, and Social Impact and Perception （SIP）. The model exhibits good construct validity, with high loadings for items such as teaching feedback, creative thinking, efficiency, data privacy, and campus support. Group differences analysis reveals that female students score significantly higher than males in the dimensions of teaching support and creativity; both academic year and AIGC usage duration show a positive gradient effect across all dimensions, while mere usage frequency has no significant effect. Although this study proposes and validates the VAM model, the sample was drawn mainly from several universities in mainland China, and the data rely solely on student self-reports, which may be subject to multiple biases, without measuring actual design processes or outcomes. Future research should incorporate behavioral logs, portfolio analysis, and longitudinal designs to examine the model's causal validity and cross-context stability. In summary, the proposed VAM five-dimensional model integrates instructional support, creativity, technical usability, privacy ethics, and social cognition, refines the acceptance structure of AIGC-assisted visual communication design courses, provides measurement instruments that can be used for teaching assessment, and offers an important reference for differentiated instruction, platform adaptation, and ethical governance in such courses.

목차 (Table of Contents)

• I. Introduction 1
• 1. Background and Motivation 1
• 2. Research Objectives, Significance, and Scope 4
• 3. Research Methods 7
• 4. Research Content 12
• II. literature review 15
• 1. Visual Communication Design 15
• 2. Artificial Intelligence Generated Content 25
• 3. The Necessity of AIGC in Assisting Visual Communication Design 27
• 4. The adoption of the technology and its associated theoretical foundations 32
• III. Constructing Dimensions and Indicators 41
• 1. Research Method 41
• 2. User group Research 54
• 3. Expert Interview 63
• IV. Research Sample and Data Findings 68
• 1. Research Samples and Data Collection 71
• 2. Reliability Analysis 74
• 3. Validation of the Measurement Model 76
• 4. Confirmatory Factor Analysis 80
• 5. Group Differences 92
• V. Discussion 102
• 1. Discussion of the Results 102
• 2. Demographic Differences 128
• 3. Research Recommendations 136
• VI. Conclusion 148
• 1. Research Findings 148
• 2. Limitations 151
• 3. Future Research 153
• References 157
• Appendix 171
• 국문초록 181
• Acknowledgements 183