The study aims to investigate user engagement in the Generated Artificial Intelligence (GAI) marketing content within social media marketing. The increasing prominence of GAI content has sparked diverse reactions, particularly concerning the safety of personalized content and individuals' awareness of their data being utilized for customization. The main goal of this research is to understand the impact of GAI on user engagement in social media marketing, focusing on the utilization of customer data and preferences on social media platforms. The research unfolds as an empirical exploration, employing a quantitative approach and a structured questionnaire. In conclusion, the research implications emphasize the significance of transparent communication, strategic content creation, and a nuanced understanding of the relationship between awareness, personalization, and consumer engagement in the context of AI-generated content. Marketers can leverage these insights to refine their strategies, enhance user experiences, and build trust in the era of AI-driven marketing.

This paper aims to investigate the effect of AI-generated content (AIGC) when it starts to be applied in game development. AIGC in games refers to the generation of game content through artificial intelligence, a concept that has recently recieved a high level of attention due to the latest rapid developments in artificial intelligence, and in traditional research, AIGC can be categorized as an advanced approach to Procedural Content Generation (PCG), i.e., Deep Learning Method. Procedural Content Generation is the creation of game content through algorithms with limited or indirect user input. Its traditional approach has been widely used in games. Recently, however, the AIGC method has also started to be used by a large number of game companies, and its impact has exceeded expectations. A questionnaire survey of 40 game developers revealed a general interest in AIGC but also concerns. Further interviews explored the use of AIGC in game development and some of the problems it has encountered and predicted future trends in its development. The result of this study provide guidance on whether and how AIGC needs to be used in future game development.

This thesis explores the perception and trustworthiness of the users towards artificial intelligence (AI) -generated content on social media platforms. The study employs the Technology Acceptance Model (TAM) and Framing Theory as theoretical frameworks to understand the factors influencing user attitudes and behaviours towards AI-generated content. The research explores three main areas: user trust in AI-generated content, the ability to differentiate between AI-generated and human-generated content and the ethical implications of AI-generated content use. The research employed an online survey with 100 participants to collect quantitative data on their experiences and perceptions of AI-generated content. The findings indicate a range of trust levels in AI-generated content, with a general trend towards cautious acceptance. The results also reveal a gap between the participants' perceived and actual abilities to distinguish between AI-generated content, underlining the need for improved media literacy and awareness initiatives. The thematic analysis of the respondent's opinions on the ethical implications of AI-generated content underscored concerns about misinformation, bias, and a perceived lack of human essence. The study connects these findings with the TAM and Framing Theory, suggesting that perceived usefulness and the framing of AI-generated content significantly impact user trust and acceptance. This research contributes to the ongoing discourse on AI in media and communications, underlining the need for a more nuanced understanding and responsible AI ecosystem development. It highlights the crucial role of public perception, awareness, and ethical considerations in shaping the future of AI-generated content on social media platforms