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1. From Reflection to Action Following the previous stage of reflection (10/6–11/2), I realised that my research had entered a new turning point.In Unit 3 and the early stage of Unit 4, my focus was mainly on how audiences emotionally respond to AI-generated visuals.However, as my analysis deepened, a more intriguing question emerged: When AI…

1. Initial Plan and Research Intention At the beginning of October, I created a four-week action plan that aimed to move my Unit 4 project smoothly from research review to intervention testing.The plan outlined a clear linear path — building the framework, designing the experiment, conducting the test, and analysing the results. However, as the…

1. Rediscovering My Question At the beginning of Unit 3, my central research question was: “How can fashion brands ensure that AI-generated visuals maintain — rather than weaken — emotional connection with consumers?” This question arose from my curiosity and concern about the growing adoption of AI within fashion’s creative production.While AI imagery offers efficiency…

1. Gu, C., Jia, S., Lai, J., Chen, R. and Chang, X. (2024) Reference Gu, C., Jia, S., Lai, J., Chen, R. and Chang, X., 2024. Exploring consumer acceptance of AI-generated advertisements: From the perspectives of perceived eeriness and perceived intelligence. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), pp.2218–2238. doi:10.3390/jtaer19030108. Available at: https://www.mdpi.com/0718-1876/19/3/108…

1. Research Evolution Throughout the development of this research, I gradually realised that the challenges AI brings to brand visual communication are far more complex—and far more fundamental—than I initially imagined. At the beginning, my observations came from a practitioner’s perspective: AI-generated visuals often exhibited unstable colour shifts, stylistic drift, and inconsistent atmospheres. However, as…

I recently came across an article by Rindfleisch and O’Hern, who proposed a Co-creation model encompassing both user-contributed content and user-selected content. This framework emphasizes that consumers are not merely passive recipients but active collaborators in shaping brand visual experiences (Rindfleisch & O’Hern, 2015). In the second intervention, I examined the boundaries that brands should…

https://24042236.myblog.arts.ac.uk/files/2025/08/AI视觉内容与情感链接测试AIVisualsEmotionalConnectionTest报告2.docx 1. Sample Overview The sample was relatively diverse but skewed toward young to middle-aged men with moderate fashion interest. 2. Key Findings 品牌代表性（Q5） 情感诉求（Q6） 四个维度（Q7-Q10） Image A consistently led across all four dimensions, particularly realism and warmth. 披露后的态度转变（Q12） 影响因素排名（Q14） Consumers cared most about real models and transparency. 可接受的人工智能应用（Q15） Consumers tend to accept AI in…

BackgroundIn Intervention 1, I found that when brands adhere to certain boundaries while using AI tools, they can enhance efficiency while mitigating the negative effects on emotional connection with consumers. Building on this finding, I designed Intervention 2 to identify which visual elements most significantly affect consumers’ emotional acceptance and to define the emotional boundaries…

Research Question How can fashion brands ensure that AI-generated visual content preserves, rather than undermines, consumers’ emotional connection to the brand? 理由 This first intervention aimed to test whether consumers’ emotional acceptance of AI-generated advertising could shift rapidly when exposed to specific visual elements. Prior research suggests that consumer acceptance of AI advertising is shaped…

In my interviews with fashion practitioners, they highlighted that one of the biggest challenges when using AI generation tools is ensuring visual consistency, particularly in terms of brand tonality, colour accuracy, and emotional expression. The topic of emotional expression especially drew my attention, as I had already noticed instances where consumers found it difficult to…