Computational Curriculum Studies
Pathways for Learning in the Information Age
As part of our AI innovation strategy, OpenEvo aims to support the emergence of an ethical and critical approach to the nascent field of Computational Curriculum Studies, the interdisciplinary study of the use of computational concepts and information technologies to describe, analyze, represent, and improve the design of learning pathways in educational contexts.
Our theory of systems transformation
We propose to clarify the nascent field of Computational Curriculum Studies (CCS): an interdisciplinary endeavor spanning curriculum studies, educational design research, and computational sciences and dedicated to critically studying the transformation of curriculum representations from static, top-down products, to participatory, flexible, and transparent computational processes. By converting curricula from static documents into dynamic, human-and-machine-readable models, we can empower local stakeholders with computational tools to make the underlying logic of their educational systems visible, malleable, and open to participatory redesign.
Figure 1. Our theory of systems transformation. By leveraging foundational work in CCS, we may be able to better shape processes of machine-culture coevolution in curriculum design and implementation.
Our theory of system transformation (Fig. 1) is informed by a model of machine-culture coevolution (Brinkmann et al., 2023). We suggest that by introducing new computational tools (the „machine“) into the existing practices of curriculum design (the „culture“), a cycle of reciprocal change is initiated. The tools may change how stakeholders think about and enact curricula; in turn, their values and insights may shape the ongoing, collaborative development of the tools. With proper support, this feedback loop could be a powerful engine for transformative change.
Computational modelling
We are working to build the theoretical and technical foundations for Computational Curriculum Studies. Current technologies enable a computational restructuration of knowledge (sensu Wilensky & Papert, 2010), a process already occurring implicitly as educators use AI. This offers both potential and peril for the cultural evolution of schooling systems (sensu Brinkmann et al., 2023). Our work will clarify these opportunities and risks, making this process more explicit, reflective, and values-driven.
Key focal areas include:
We are working to review and prototype methods for representing curricula as prerequisite networks (Aldrich, 2015), where concepts are nodes and dependencies are edges. This allows for the analysis of curricular structures of knowledge and the generation of alternative learning pathways.
We will explore the „information bottleneck“ method (Tishby et al., 2000) as a formal model for curriculum design, balancing the breadth of knowledge with the depth of conceptual understanding. This provides a novel theoretical basis for tackling curriculum overload. We will clarify the opportunities and constraints of this degree of formalization in curriculum design across data-rich and data-scarce schooling environments.
Drawing on emerging discourse in data science, we will develop protocols for making stakeholder „Theories of Schooling“ more Findable, Accessible, Interoperable, and Reusable (FAIR; Van Lissa et al., 2025). This involves designing survey instruments, interview protocols and curriculum scope and sequence planning activities that can elicit these models and translate them into machine-readable formats aligned with the Common Educational Data Standards (CEDS; https://ceds.ed.gov/). This represents a foundational step for empowering participatory computational curriculum design.
We will apply emerging frameworks for socio-technical systems (Smaldino et al., 2025), conceptualizing educational systems as information architectures, to understand how different computational tools shape the flow of information and power within a curriculum design process (in relation to machine-culture coevolution, Brinkmann et al. 2023).
Participatory design-based research
We are working to ground this broader theoretical work in the lived realities and needs of school stakeholders. This area focuses on building the social infrastructure and participatory methods necessary for equitable curriculum design. A central challenge in curriculum reform is the gap between policy and practice; teachers are often skeptical of radical changes they had no part in creating. Our highly participatory approach directly addresses this by positioning educators, policy makers, and even students, as co-designers.
Key focal areas include:
We will pilot research methods (surveys, ethnographic observation, participatory workshops) in diverse field sites to characterize local ToS, standards, and implementation practices.
In collaboration with teacher education programs, we will co-design and prototype simple computational tools that allow teachers to visualize their current curriculum structure and experiment with alternatives. This work will be guided by principles of Responsible Design Science Research (Herwix, 2024).
We will formalize partnerships with key organizations and field site contacts. This involves co-developing context-specific collaboration models that respect local autonomy and expertise, and planning the governance structure for the broader Networked Improvement Community.
We will identify the core competencies educators and policymakers need to engage meaningfully in computational curriculum design, and begin developing professional development approaches to build this capacity.
Prototypes for tools and methods in CCS
Interdisciplinary Structures of Knowledge
As part of our Computational Curriculum Studies (CCS) project, we are working to develop methodologies related to the analysis of curriculum policy texts as interdisciplinary structures of knowledge that can be productively analyzed using computational methods.
Semantic spaces
As part of our Computational Curriculum Studies (CCS) project, we are working to develop methodologies related to the analysis of curriculum policy texts as semantic spaces – spaces of related meaning – that can be analyzed computationally to reveal opportunities and challenges for interdisciplinary and locally relevant curriculum design processes.
OpenEvo Network Explorer
As part of our Computational Curriculum Studies (CCS) project, we are working to develop tools to enable researchers, educators, and students to navigate the landscape of resources within our OpenEvo system for the design of educational innovations.
Example Topic: The Curriculum Overload Paradox
Humans keep accumulating more and more knowledge. It might, therefore, make sense that the school curriculum keeps growing as well. Unfortunately, for many teachers and students, this growth in content has led to curriculum overload.
The rise of AI seemingly adds yet another topic to the already overloaded stack of topics that all students must study. Yet, a different perspective – both conceptually and technologically – may suggest a different interpretation.
New techniques in CCS, such as the use of Knowledge Graphs (KGs) to represent learning pathways, combined with the strategic use of LLMs (e.g. ChatGPT), may enable new directions in curriculum design in which, paradoxically, an increase in the complexity of curriculum (as representations of knowledge structures) may be capable of leading to a decrease in the complexity experienced by teacher and students – due to the potential to more efficiently – and more flexibly – manage cognitive load through personalized learning supports.
The prototype tools and methods linked above may provide incredible new opportunities for innovations, however, interdisciplinary educational design research will have to clarify the potential value proposition.
References
The scope of CCS is potentially vast, and this initial concept site can not yet map the full space. Below we offer a few helpful references to orient interested researchers to relevant contexts for future development.
Aldrich, M. B., et al. (2015). The curriculum prerequisite network: Modeling the curriculum as a complex system. Biochemistry and Molecular Biology Education, 43(5), 317-327.
Boyette, A. H., & Hewlett, B. S. (2017). Autonomy, Equality, and Teaching among Aka Foragers and Ngandu Farmers of the Congo Basin. Human Nature, 28(3), 289–322. https://doi.org/10.1007/s12110-017-9294-y
Boyette, A. H., & Hewlett, B. S. (2018). Teaching in Hunter-Gatherers. Review of Philosophy and Psychology, 9(4), 771–797. https://doi.org/10.1007/s13164-017-0347-2
Brinkmann, L., Baumann, F., Bonnefon, J. F., Derex, M., Müller, T. F., Nussberger, A. M., … & Rahwan, I. (2023). Machine culture. Nature Human Behaviour, 7(11), 1855-1868. https://doi.org/10.1038/s41562-023-01742-2
Eirdosh, D., Prasetijo, A., Aprilia, C., Greenfield, P. M., Lavi, N., Muchukunnu, A., … & Rothstein-Fisch, C. (2025). Learning to navigate change: Case studies in education across cultural boundaries. In A Field Guide to Cross-Cultural Research on Childhood Learning: Theoretical, Methodological, Practical, and Ethical Considerations for an Interdisciplinary Field (pp. 267-308). Open Book Publishers. https://doi.org/10.11647/obp.0440.08
Eirdosh, D., & Hanisch, S. (2023). A Community Science Model for Inter-disciplinary Evolution Education and School Improvement. In A. du Crest, M. Valković, A. Ariew, H., Desmond, P. Huneman, & T. A. C. Reydon (Eds.), Evolutionary Thinking Across Disciplines: Problems and Perspectives in Generalized Darwinism (pp. 125–146). Springer International Publishing. https://doi.org/10.1007/978-3-031-33358-3_7
Fogarty, L., Kandler, A., Creanza, N., & Feldman, M. W. (2024). Half a century of quantitative cultural evolution. Proceedings of the National Academy of Sciences, 121(48), e2418106121. https://doi.org/10.1073/pnas.2418106121
Herwix, A. (2024). Toward a Responsible Design Science Research Ecosystem for the Digital Age: A Critical Pragmatist Perspective (Doctoral dissertation, Universität zu Köln).
Kroupin, I., & Zeng, T. C. (2024, August 13). From Richer to Leaner cultures and minds: How depth of standardization as an evolutionary dynamic drives changes in technology, social structure, and cross-cultural cognitive variation. https://doi.org/10.31234/osf.io/mxfcv
OECD. (2020). Curriculum Overload. A way forward. OECD Publishing. https://doi.org/10.1787/3081ceca-en
Sertić Perić, M., & Draženović, K. (2025). Assessing biology teachers’ satisfaction with a shift from a thematic to conceptual curriculum approach: Implications for science education reform. Journal of Biological Education, 59(2), 282–294. https://doi.org/10.1080/00219266.2024.2326440
Smaldino, P. E., Russell, A., Zefferman, M. R., et al. (2025). Information architectures: a framework for understanding socio-technical systems. npj Complexity, 2(1), 13.
Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint physics/0004057.
Tweedie, J., Pelly, F., Wright, H., & Palermo, C. (2025). Exploring the adoption of concept-based curricula: Insights from educators and implications for change. Advances in Health Sciences Education, 30(1), 223–237. https://doi.org/10.1007/s10459-024-10346-y
Tyack, D., & Tobin, W. (1994). The „grammar“ of schooling: Why has it been so hard to change? American Educational Research Journal, 31(3), 453-479.
Van Lissa, C. J., Peikert, A., Ernst, M., Van Dongen, N., Schönbrodt, F. D., & Brandmaier, A. M. (2025, March 9). To be FAIR: Theory Specification Needs an Update. https://doi.org/10.31234/osf.io/t53np_v1
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. Constructionism, 17(2010), 1-15.