Vol. 1 No. 1 (2025)

Artificial intelligence generated content (AIGC) has been found to play a crucial role in the field of design, where its significance in various creative clusters has been increasingly recognized. However, the lack of in-depth understanding of creators’ AIGC learning experiences and sentiments in the design area has become an obstacle to the further development of targeted educational programs and industry-relevant initiatives. It remains unclear what specific clusters of AIGC design learning creators are focusing on and what kinds of attitudes, positive or negative, they hold towards these different clusters within the field. To bridge these gaps, this study collected 9992 posts and comments related to AIGC design learning on a Chinese social media platform called Xiaohongshu. A mixed-method approach was applied by combining word cloud, sentiment analysis, co-word analysis, and social network analysis. Using word cloud and sentiment analysis, this research aimed to uncover the key perceptions and sentiment orientations creators focused on in their expression. Social network analysis and co-word matrix were used to identify central concepts, their connections, and clusters of related terms. The result differentiates creators’ perceptions into the following clusters: tools and technical foundations for AIGC design, application domains of AIGC design, cultural elements used in AIGC, semantic nuances in AIGC terminology, AIGC design methods, creativity and innovation in AIGC design, future-oriented perspectives in AIGC design for professional development, and AIGC design ethics. Moreover, it presents the tendencies and proportions of creators’ attitudes in the above clusters in four main sentiment categories: positive, moderately positive, moderately negative, and negative. This study is expected to have implications in providing practical guidance for educators to optimize AIGC design teaching strategies, thereby better meeting learners’ emotional needs and increasing their willingness to learn AIGC design knowledge, as well as helping the industry develop better AIGC-designed products.

As artificial intelligence (AI) reshapes global education systems, African higher education institutions (HEIs) face pressure to adopt and govern AI ethically and effectively. This study investigates five questions: (1) What is the status of AI governance in African HEIs? (2) How ready are institutions to adopt AI policy? (3) What ethical and operational risks are emerging? (4) How do institutional and national AI strategies align? and (5) What future-proof governance framework can be proposed? Using a desk-based meta-synthesis, the study analyzes over 30 publicly available institutional, national, and regional documents. The analysis is guided by Resource Dependence Theory, Diffusion of Innovation, and Complexity Theory. No human subjects were involved, and international ethical standards were followed; PRISMA guidelines were deemed not applicable due to the qualitative scope. Findings reveal wide disparities in policy development and readiness. South Africa, Nigeria, and Rwanda are early adopters, aligning institutional policies with national digital strategies (RQ1 & RQ4). Most institutions remain in aspirational phases, limited by infrastructure and human capacity (RQ2). Where policies exist, they emphasize academic integrity and responsible use, though enforcement is uneven and impact largely unevaluated (RQ3). Cross-national alignment varies, with regional frameworks lacking strong enforcement mechanisms (RQ4). Evidence suggests that institutions with structured policies invest more in training and faculty engagement (RQ2 & RQ5). The study proposes a phased, ethically grounded governance framework tailored to Africa’s educational context (RQ5). It contributes new insights into readiness differentials, governance diffusion, and policy convergence, offering a foundation for inclusive, future-oriented AI policy in African higher education.

Artificial Intelligence (AI), first proposed at the Dartmouth Conference in 1956, has achieved remarkable achievements with near 70 years' development. With the continuous development of AI, especially the rise of generative AI (GAI) since ChatGPT, also called Large Language Models (LLMs), it has transformed educational landscapes globally [1]. Recently, the integration of AI/GAI in education (AIED) has become focused point, mainly introducing AI to create better personalized learning experience, propose adaptive assessment methods, and construct human-like tutoring systems [2]. In the future, AIED is expected to further promote the digital and intelligent transformation of education.