Despite growing global interest in Artificial Intelligence (AI) in education, limited empirical research has examined how teacher education institutions in developing-country and resource-constrained contexts can sustainably integrate AI into their curricula. This gap is particularly significant because many existing AI integration frameworks assume levels of technological infrastructure, institutional capacity, and digital readiness that may not reflect the realities of higher education institutions in the Global South. Against this backdrop, this study explored institutional readiness, curriculum integration pathways, and implementation strategies for Artificial Intelligence within teacher education programmes in Namibia. Guided by an interpretivist paradigm, a qualitative exploratory design was employed, using semi-structured interviews with 25 teacher educators across six university campuses. Data were analysed thematically using Braun and Clarke’s framework. The findings indicate that AI integration extends beyond technological adoption and requires coordinated curriculum transformation, institutional preparedness, and pedagogical redesign. Key integration pathways include embedding AI within faculty courses, curriculum-wide integration of AI competencies, simulation-based learning, and support for online teaching environments. Institutional readiness, particularly in terms of infrastructure, faculty capacity, and curriculum alignment, emerged as a critical determinant of implementation. While participants highlighted benefits such as improved instructional efficiency, enhanced teacher capacity, and increased learner autonomy, they also expressed concerns regarding overreliance on technology. By integrating Diffusion of Innovation (DOI) and the Technology Acceptance Model (TAM), this study advances a multi-level framework linking institutional and individual dimensions of AI adoption in teacher education. This study contributes context-specific insights to AI curriculum transformation in the Global South and provides practical implications for curriculum design, institutional strategy, and policy development.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
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