| Abstract: |
For decades, education R&D has delivered mostly incremental change, consistent with Tyack and Cuban's observation in Tinkering Towards Utopia (1995) that systems tend to assimilate new technologies. Some innovation initiatives aim to engineer "planned serendipity"--structuring teams and infrastructure so that unexpected insights are more likely to arise and propagate (Michael H. Levine, personal communication, 2025). Today's environment is different: generative AI, platform-level data, and new research infrastructures are creating strategic surprises--rapid shifts that outpace traditional sensing and response. We propose re-architecting education R&D for agility, responsiveness, and breakthrough potential. Drawing on ARPA-style (federal Advanced Research Projects Agencies) (NASEM, 2017) models (and our work with SEERNet and Advanced Education Research and Development Fund (AERDF), we contrast legacy approaches with a surprise-ready architecture along four dimensions: (1) framing commitments, (2) problem definition, (3) project/talent model, and (4) focal outcomes. We illustrate with six "ripe problem" examples that compress the observe-orient-decide-act loop in schools, and we conclude with role-specific recommendations for researchers, developers, funders, and systems leaders. Our goal is a field that not only studies change, but orchestrates the conditions--capable people and enabling environments--under which transformative learning improvements can emerge. |