Unsupervised Environment Design (UED) formalizes the problem of adaptive curricula in underspecified environments through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential and curates an adaptive curriculum, ultimately enhancing the student's ability to handle unseen scenarios. Existing UED methods primarily rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to shape the training curriculum. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty — a critical element for enhancing an agent's generalization ability. Directly quantifying environment novelty is especially challenging due to the underspecified nature of environment parameters in UED, which entails there is a one to many mapping between an instantiation of environment parameters and the actual environments corresponding to that set of parameter values. Due to this, existing approaches to measuring novelty based on environment parameters face significant hurdles. To address these challenges, this paper introduces the GMM-based Evaluation of Novelty In Environments (GENIE) framework. GENIE offers a scalable, domain-agnostic, and agent (student) policy-aware approach to quantifying environment novelty by using Gaussian Mixture Models. By augmenting regret-based UED algorithms with novelty-based objectives, GENIE facilitates effective exploration across the state-action space while progressively increasing curriculum complexity. We provide empirical evaluations that demonstrate GENIE providing SOTA results on zero-shot out-of-distribution performance across all the UED benchmarks in the literature.