Forming learners' science concepts and conceptual change entails adaptive epistemic beliefs to support a high degree of interactivity within a coherent knowledge structure. Adaptive epistemic beliefs are characterized by beliefs that knowledge is uncertain and should be justified through experimentation or multiple sources dependent upon the task contexts. Thus, assessing and evaluating learners' adaptive epistemic beliefs is a complex process that requires laborious analysis of learner artifacts based on reliable and valid coding schemes. This article aims to describe new ways of assessing and applying technologies that can measure and foster adaptive epistemic beliefs. We propose new strategies for a theoretically-based human-and-machine symbiotic Learning Analytics (LA) framework. The application of this LA framework may facilitate the development of real-time detecting and representation of the individual and collective epistemic belief networks as well as diagnosing and providing appropriate scaffolds to promote adaptive epistemic beliefs via the design of personalised pedagogical feedback with experts' input. The heuristic application of technology infrastructure may propel a movement for more tangible and personalised learning in science education. The current gaps of using AI-based emerging technologies in science learning and implications for science education are discussed to advance science education in new directions.