Generative AI for Creative Learning Content Creation

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

This study explores the potential of Generative AI (GenAI) to stimulate creativity among students in a project-based learning (PBL) context, with a specific focus on a design course. Drawing on Self-Determination Theory (SDT), the research delves into the effects and mechanisms through which GenAI fosters creativity. The study involved 22 third-year students enrolled in the ‘Product Design and Culture’ class, using qualitative methods. Results indicate that GenAI tools provided diverse design inspirations and novel perspectives. However, attitudes towards GenAI varied, with some students expressing concerns about the originality, design rationality, and creative depth limitations of AI-generated content. Despite these concerns, GenAI enhanced students’ autonomy, intrinsic motivation, and active learning in the PBL context. Cognition and prompt inputs influenced their sense of competence. Furthermore, a GenAI-enabled PBL environment facilitated interaction with both peers and AI.

This exploratory study investigates how students experience the use of Generative AI (GenAI) in a project-based Agile course within higher education. Through a combination of interviews and survey, we examine how GenAI tools support learning, decision-making, and team dynamics. Our findings point out that students used GenAI primarily for understanding and applying Agile practices, validating decisions, and fostering teamwork. For some students, GenAI served as a scaffold for applying theory to practice, and boosted their confidence. We conclude that GenAI can be effectively integrated into project-based learning, provided that students receive guidance on responsible use and evaluation. We offer implications for teaching strategies and contribute to emerging discussions on GenAI in high education.

Visual content shared on social media often includes sensitive elements that can threaten personal privacy. While privacy protection tools--some of which are powered by the state-of-the-art generative AI (Gen-AI) technologies--have been increasingly developed to address such visual privacy concerns by identifying sensitive elements in visual content and suggesting or applying modifications to process the visual content, the success of these tools depends on how well they meet users' nuanced needs and preferences. In this study, we conducted semi-structured interviews with 18 individuals who have either experienced or caused privacy violations in shared visual content in the past to gather first-hand perspectives on stakeholders' privacy concerns, their preferences for how to address these concerns, and their attitude toward the use of generative AI for privacy protection. Our findings highlight that sensitive elements are often not limited to direct identifiers but include contextual combinations and external information that can lead to unintended inferences. Decisions about whether and what to modify are shaped by concerns about privacy effectiveness, content value, content meaning, and emotional or social relevance, while choices around how to modify are influenced by recognition difficulty, visual content integrity, contextual consistency, atmosphere, and usability of modification methods. Participants saw Gen-AI as a promising tool for lowering editing barriers and enhancing creative control but also raised concerns about data usage, manipulation, and transparency. Importantly, we identify tensions between uploaders and depicted individuals, emphasizing the need for shared consent mechanisms and user-centered design in privacy protection. We conclude by discussing design implications for context-aware, flexible, and ethically responsible privacy tools.

This study presents a comparative analysis of user engagement with generative AI, using ChatGPT traffic data for March 2024 as a case study of one widely adopted tool. Using World Bank data and statistical methods, including ANOVA, it compares usage across continents. While statistical tests showed no significant differences in total visits or global traffic share, descriptive results revealed meaningful disparities in engagement intensity. Chatbot-based platforms, particularly ChatGPT, have dominated other generative AI tools due to their versatility in tasks such as content creation, learning, coding, and communication. These findings suggest a trend toward broader access but also highlight persistent structural barriers, such as device access, digital skills, and infrastructure, that shape real-world usage. Therefore, promoting digital literacy, improving infrastructure, and localizing AI tools remain essential to achieving more equitable global engagement with generative AI.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Beyond the industrialization of culture, the cultural industry is created in a digital form by using generative AI as a creative tool. For the purpose of realizing a cultural state, the Cultural Industry Promotion Act, enacted in 1999, has been faithful to the legislative purpose of promoting the cultural industry for the industrialization of culture. In the meantime, not only the creation of culture but also the consumption methods of consumption have been diversified. In particular, the creation of content by generative AT is reaching a situation where the paradigm for creation and consumption can be changed. It is necessary to think about the digital ecosystem, not just digital transfonnation, and in line with this, essential changes in the cultural industry are expected. This is why legislation for sustainable growth of the cultural industry in the deepening period beyond the digital transition period is required. It will focus on the current status of cultural industry legislation such as the Cultural Industry Promotion Act and the Content Industry Promotion Act, the digital transformation system, its status as a basic law, and overlapping content-related regulations. In summary, the limitation of the Cultural Industry Promotion Act is that it focuses on the typification of traditional cultural industries. In addition, the limitation is that it is limited to roles as individual laws rather than roles as basic laws. Although the Cultural Industry Promotion Act is a basic law, it has been confirmed that there are various limitations such as the lack of a digital transfonnation response system such as status and system and the rise of AI as a basic law. Based on these limitations, I would like to present an improvement direction. In particular, it was also confirmed that although it is a limitation of the Cultural Industry Promotion Act, much of the Cultural Industry Promotion Act overlaps with the Content Industry Promotion Act. To this end, we would like to present the consistency of the two laws by reviewing the overlapping matters of the Cultural Industry Promotion Act and the Content Industry Promotion Act.

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.

This study explored students’ perspectives on the benefits and challenges of integrating Generative AI (GenAI) tools in teaching, learning, and assessment within five colleges under the Royal University of Bhutan (RUB). The study is timely and relevant as educators and students in Bhutan increasingly adopt GenAI tools, reflecting a broader global trend. The study outlines three primary objectives. First, it seeks students’ perspectives regarding the integration of GenAI tools in teaching, learning, and assessment in higher education, highlighting the GenAI tools used, the benefits they offer, and the challenges they may impose. Second, the study aspires to provide practical recommendations for educators and academics in higher education institutions for the effective integration of GenAI tools. Finally, the study aims to provide recommendations to relevant stakeholders and policymakers for the adoption or expansion of AI initiatives within the precincts of RUB colleges. This study employed a qualitative approach as qualitative research allows for a deeper understanding of human behaviour and experiences. A purposive sampling technique was used to ensure that participants were selected based on their potential to provide valuable insights relevant to the research objectives. Data were collected through semi-structured focus group interviews, with each group comprising six members (three male and three female). A total of 180 students participated across 30 focus group interviews conducted in five colleges, with six focus group interviews in each college. The data collected were transcribed, coded, and categorised into themes for analysis. To maintain confidentiality, the researchers used a systematic labelling system for both focus groups and individual students during the data analysis process, enabling a comprehensive and structured interpretation of the findings. The findings revealed key benefits of GenAI, such as providing quick and accessible information, fostering personalised learning, and offering emotional support. However, the findings also revealed challenges in terms of the reliability of AI-generated content, the potential hindrance to creativity and critical thinking, and the risk of social and emotional disconnect between students and their learning communities due to overreliance on GenAI. The study therefore recommends raising awareness and improving understanding of GenAI tools among students and establishing comprehensive policy frameworks to promote their ethical use across RUB colleges while upholding high standards of academic quality.

This study examines user perceptions of Generative AI (GenAI) technologies like ChatGPT in academic writing, focusing on engagement, motives, benefits, challenges, and integration. Gen AI tools like ChatGPT is a transformer trained in two stages: unsupervised pre-training and supervised fine tuning, to predict the next most probable word in a sequence to generate expected content. A survey of 206 respondents from diverse backgrounds identified four key roles of GenAI: facilitator of thoughts, reliable content creator, supportive brainstorming partner, and content augmenter. SPSS Version 20 was used to analyze data validity, reliability, normality, clustering, and hypothesis testing. Survey data was processed using principal component analysis with varimax rotation. The hypothesis testing was carried out using the non-parametric Kruskal-Wallis test with the help of p-significance. Statistical tests revealed no significant linkage between user qualification or category and GenAI usage for academic purposes. However, trust in GenAI's accuracy showed a gender bias, with males more likely to trust its outcomes than females. Interestingly, despite age often influencing technology adoption, no generational differences were found in GenAI usage. It suggests that its ease of use and operability make it accessible across age groups. The findings highlight the broad acceptance of GenAI in academia while underscoring trust-related gender differences.

This article articulates the nuanced challenges of integrating Generative AI (GenAI) into educational settings, aiming to dispel overly simplistic narratives driven by unwarranted enthusiasm or unfounded apprehensions. It introduces a conceptual framework for the application of GenAI within higher education, delineating four key strategies that leverage the dual roles of educators—as both creators and designers—while positioning GenAI as either a facilitative agent (creature) or a utilitarian tool. The identified strategies—Interactive Co-Creation, Adaptive Design, Learning Scaffold, and Efficient Structuring—underscore GenAI’s potential to revolutionize teaching methodologies by enabling personalized education, enhancing content quality, and expediting course development processes. Emphasizing GenAI’s capacity to cater to diverse student needs, simplify educational content creation, and foster engaging learning environments, the model provides educators with a roadmap for integrating GenAI into their instructional practices to harness the full potential of educational technology.Generative AI; educator’s role; instructional design

This study aims to analyze the influence of project-based learning management on the learning creativity of class XI students at SMKN 1 Keruak in the 2023/2024 academic year, reviewed from 2 management functions, namely planning, and evaluation, using the hypothesis that planning and evaluation of project-based learning have an effect on students' learning creativity at SMKN 1 Keruak. The data for this study were obtained from the results of respondent responses (questionnaire distribution), where the questionnaire was filled out by 100 class XI students at SMKN 1 Keruak, taken from a population of 132 people. The research method used is a survey method with a quantitative approach. The statistical analysis used in this study is multiple linear regression analysis and assisted by using the SPSS 15 program. The results of this study indicate that: (1) Project-based learning planning has an effect on students' learning creativity, as evidenced by the regression coefficient of 0.184 or 18.4%; (2) Project-based learning evaluation has an effect on students' learning creativity, as evidenced by the regression coefficient of 0.266 or 26.6%; (3) Project-based learning planning and evaluation has an effect on students' learning creativity, as evidenced by the determinant coefficient of 0.375 or 37.5%. ABSTRAKPenelitian ini bertujuan untuk menganalisis pengaruh manajemen pembelajaran berbasis proyek terhadap kreativitas belajar peserta didik kelas XI di SMKN 1 Keruak tahun ajaran 2023/ 2024, ditinjau dari 2 fungsi manajemen, yaitu perencanaan dan evaluasi, dengan menggunakan hipotesis bahwa perencanaan dan evaluasi pembelajaran berbasis proyek berpengaruh terhadap kreativitas belajar peserta didik di SMKN 1 Keruak. Data penelitian ini diperoleh dari hasil tanggapan responden (penyebaran angket), dimana angket diisi oleh peserta didik kelas XI di SMKN 1 keruak sejumlah 100 sampel yang diambil dari populasi sejumlah 132 orang. Metode penelitian yang digunakan adalah metode survey dengan pendekatan kuantitatif. Analisis statistik yang digunakan dalam penelitian ini adalah analisis regresi linier berganda dan dibantu dengan menggunakan program SPSS 15. Hasil penelitian ini menunjukkan bahwa: (1) Perencanaan pembelajaran berbasis proyek berpengaruh terhadap kreativitas belajar peserta didik, dibuktikan dengan hasil koefisien regresi sebesar 0,184 atau 18,4%; (2) Evaluasi pembelajaran berbasis proyek berpengaruh terhadap kreativitas belajar peserta didik, dibuktikan dengan hasil koefisien regresi sebesar 0,266 atau 26,6%; (3) Perencanaan dan evaluasi pembelajaran berbasis proyek berpengaruh terhadap kreativitas belajar peserta didik, dibuktikan dengan hasil koefisien determinan sebesar 0,375 atau 37,5%.

Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.