Most AI alignment strategies start from a comfortable yet fragile assumption: that human preferences are fixed targets to be inferred and optimized. This clashes with decades of empirical evidence in psychology and behavioral economics—what we want is not set in stone, but layered, dynamic, and largely constructed through interaction with the tools we use. Tools that today are increasingly large language models, recommendation systems, and digital assistants designed to accompany us for months or years, personalizing to our habits.

The research group that formalized the concept of Constructive Alignment moves from here to dismantle the traditional approach. Rather than treating alignment as an inference-and-satisfaction problem, they recast it as a control problem over evolving preference trajectories. The idea is that every interaction with an AI system does more than serve a current need; it alters—even imperceptibly—the person's evaluative states, steering attention and, over time, reinforcing certain values at the expense of others.

In operational terms, AI becomes an agent that co-determines the horizons of what we consider desirable. The framework, drawing on control theory and constructivist social sciences, models preferences as layered state variables, influenced both by system actions and by interface design and usage context. So it is no longer about ‘aligning AI behavior’ in a narrow sense, but about governing how AI itself shapes the evolution of human judgments, ensuring value trajectories that remain coherent, epistemically grounded, resistant to manipulation, and capable of preserving self-determination under uncertainty.

For those watching the deployment landscape, especially in on-premise or hybrid contexts where data sovereignty is a non-negotiable requirement, this shift in perspective is disruptive. When a system runs locally, within an organization’s controlled infrastructure, the responsibility for how that system interacts with its users can no longer be outsourced to a cloud provider. Models served on-premise create prolonged feedback loops: every generated response, every recommended piece of content, helps reshape the preferences of their users. In regulated sectors (healthcare, finance, public administration) it is no longer enough to ask ‘is the model accurate?’; one must ask ‘what long-term effect is it having on how employees or citizens form their evaluations?’.

Constructive Alignment thus offers a lens for evaluating design choices: from the frequency with which an assistant proposes decision-making shortcuts, to the transparency with which it signals uncertainty, to its ability to preserve spaces for unmediated reflection. It is no coincidence that the most compliance-conscious organizations are already beginning to integrate preference-dynamics audits into model validation processes, alongside traditional quality benchmarks. The ultimate goal, according to the authors, is not just well-behaved AI, but an ecology of interactions in which people can develop and revise their own values reflectively, without being trapped in externally driven preference paths.