Oleg V. Pavlov

Artificial intelligence is a rapidly evolving technology whose capabilities are expanding quickly, leaving instructors across higher education to determine how to integrate it into teaching with little shared guidance. This paper reports on a Faculty Learning Community comprising seven faculty members from computer science, management information systems, systems engineering, economics, development studies, political science, and geography at a technological university in the northeastern United States. Through collaborative autoethnography, the group developed a convergence–divergence framework for AI-integrated pedagogy, visualized as a daisy: a shared core of AI literacy, ethical and risk awareness, and governance frameworks, surrounded by discipline-specific petals reflecting each field’s distinct conceptualization and application of AI. Seven disciplinary vignettes illustrate the framework in practice, revealing a common pattern across disciplines: AI tools offer possibilities and efficiencies while simultaneously obscuring risks and reproducing biases that require deliberate human attention. Drawing on ecological systems theory, the framework offers faculty a multi-level structure for designing AI-integrated courses that honor both shared foundations and disciplinary authenticity.

This teaching note presents a parsimonious system dynamics formulation of the Keynesian multiplier and illustrates how it can be deployed as a flight simulator in a college‐level macroeconomics course. Building on prior work that proposed a staged framework for integrating system dynamics into economics instruction, we focus here on Level I of that hierarchy: introducing feedback thinking through a causal loop diagram and enabling exploration of dynamic behavior using a simple pre‐built simulation. Three classroom pilots conducted by instructors without formal training in system dynamics demonstrate the feasibility and usability of this approach. Rather than replacing traditional textbook analysis, the causal diagrams and the flight simulator function as a structural debriefing of Keynesian multiplier representations in mainstream economics textbooks, making feedback loops, delays, and adjustment processes explicit.

Artificial intelligence is a rapidly evolving technology whose capabilities are expanding quickly, leaving instructors across higher education to determine how to integrate it into teaching with little shared guidance. This paper reports on a Faculty Learning Community comprising seven faculty members from computer science, management information systems, systems engineering, economics, development studies, political science, and geography at a technological university in the northeastern United States. Through collaborative autoethnography, the group developed a convergence-divergence framework for AI-integrated pedagogy, visualized as a daisy: a shared core of AI literacy, ethical and risk awareness, and governance frameworks, surrounded by discipline-specific petals reflecting each field's distinct conceptualization and application of AI. Seven disciplinary vignettes illustrate the framework in practice, revealing a common pattern across disciplines: AI tools offer possibilities and efficiencies while simultaneously obscuring risks and reproducing biases that require deliberate human attention. Drawing on ecological systems theory, the framework offers faculty a multi-level structure for designing AI-integrated courses that honor both shared foundations and disciplinary authenticity.

This study introduces systems thinking into undergraduate macroeconomics through pedagogical activities centered on causal diagrams and structural debriefing. Implemented in a classroom setting, the activities help students visualize and analyze economic relationships, enhancing comprehension of textbook models. Using design-based methodology, the authors developed lesson plans accessible to instructors without systems thinking expertise. Results show improved student understanding of complex macroeconomic concepts, contributing to the growing literature on systems thinking in economics education.

Higher education institutions (HEIs), such as colleges and universities, face many challenges and competitive pressures and must innovate. How can ideas from complexity and a systems approach help a HEI understand how it creates value and potentially drive business model innovation? This article provides a university-as-a-platform (UaaP) framework that integrates platform thinking and systems thinking to help HEIs think about value creation, innovation, and the design of new business models. Platform thinking is used to provide a platform conceptualization of the university and define the framework's main elements. A HEI creates value by facilitating the participation and value-creating interactions of several groups of participants. Systems thinking, deploying a causal loop diagram (CLD), maps the value creation mechanisms focusing on feedback loops within a HEI and across its boundary. We show that the UaaP framework can help HEIs understand their complex challenges, conceptualize and map how they create value, and identify opportunities for innovation, such as novel business models. We also explain how Artificial Intelligence (AI) feedback loops strengthen the quality of participation and interactions (quality effect) on platforms. Understanding that mechanism allows HEIs to derive additional value from AI transformation. The article contributes to the platform literature and the literature at the intersection of complexity and higher education. It outlines lessons for higher education leadership and identifies opportunities for future complex systems research at the intersection of education, platforms, and AI.

System dynamics is a methodology that is widely used in many academic fields. It explains the behavior of social and economic systems with models that capture complex causality and feedback effects. This paper discusses the opportunities and barriers for introducing feedback thinking and system dynamics models in the economics curriculum. We start by providing a pricing feedback model that illustrates some of the benefits that system dynamics can provide in enhancing economics education. Then we summarize the experiences of each of the authors in teaching system dynamics in economics educational programs. This includes different approaches to teaching economics with system dynamics that depend on the learning objectives, the preparation of students, and the background of the instructor. We also develop a four‐level course hierarchy for using system dynamics in economics teaching. We then point out the tradeoffs that instructors must consider as they introduce new pedagogies for delivering economics material. Finally, we provide concluding comments with some suggestions for future work. The expected audiences for this paper are instructors as well as graduate students who are considering academia as a profession.

A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional activities centered around causal diagrams that promote a deeper understanding of economic topics beyond the traditional lecture-based methods. Our results show that structural debriefing activities can be used effectively to introduce students to the causal diagrams that explain key economic relationships in the national income model, government-purchases multiplier and tax multiplier.

A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional activities centered around causal diagrams that promote a deeper understanding of economic topics beyond the traditional lecture-based methods. Our results show that structural debriefing activities can be used effectively to introduce students to the causal diagrams that explain key economic relationships in the national income model, government-purchases multiplier and tax multiplier.

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.