Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs.

The platform business model has attracted significant attention in business research and practice. However, much of the existing literature studies commercial platforms that seek to maximize profit. In contrast, we focus on a platform for volunteers that aims to maximize social impact. This business model is called a platform for the common good. The article proposes a Causal Loop Diagram (CLD) model that explains how a platform for the common good creates value. Our model maps the key strategic feedback loops that constitute the core structure of the platform and explains its growth and performance through time. We show that multiple types of network effects create interlocking, reinforcing feedback loops. Overall, the article contributes towards a dynamic theory of the platforms for the common good. Moreover, the article provides insights for social entrepreneurs who seek to build, understand, and optimize platforms that maximize social value and managers of companies that seek to participate in such platforms. Social entrepreneurs should seek to leverage the critical feedback loops of their platform.

The approaching decline in the U.S. college-age population, sometimes referred to as a “demographic storm,” has been identified as an existential threat to the future of American colleges and universities. This article conducts a model-driven analysis of three plausible college-level responses to declining applications. It draws on systems theory to conceptualize a tuition-dependent college as a complex service system and to develop a system dynamics model that captures key causal interrelationships and multiple feedback effects between faculty, facilities, tuition revenue, financials, reputation, and outcomes. Simulations with the college model suggest that common solutions such as reducing faculty or adding campus facilities may improve the college’s short-term financial position, but they are insufficient to ensure its long-term viability. This model contributes to the research literature on the economics of higher education, and model-driven academic management and strategy. It also provides useful implications and insights that can inform policy-makers and college leaders.

Background. Research shows that learning and task performance improve when participants in management exercises understand the structure of the system they control. However, the majority of business simulators are black-boxes. Aim. This article introduces structural debriefing, which is a debriefing activity aimed at helping students learn about causal relationships, feedbacks, accumulations, and delays within a black-box simulation. Method. A structural debriefing can be prepared and facilitated by following the Structural Debriefing Protocol. Results. A pilot study was conducted in which undergraduate students participated in a structural debriefing of The LITTLEFIELD TECHNOLOGIES, a popular simulation for teaching principles of operations management. The students were able to complete all eight steps of a structural debriefing, but required considerable time (three academic terms) to do so. Not every instructional simulation will require all the steps or such a large time commitment. Conclusion. The successful completion of the pilot study demonstrates that structural debriefing is a useful debriefing technique. However, to be effective, the scope and format of a structural debriefing activity must suit practical and pedagogical considerations.