Formal learning theory constitutes an attempt to describe and explain the phenomenon of language acquisition. The considerations in this domain are also applicable in philosophy of science, where it can be interpreted as a theory of empirical inquiry. The main issue within this theory is to determine which classes of languages are learnable and how learnability is affected by e.g. restricting the learning functions, modifying the informativeness of the incoming data and changing the conditions of success of the learning process. All those directions focus on various properties of the process of conjecturechange over time. Treating "conjectures" as beliefs, we link the process of conjecture-change to doxastic update. Using this approach, we reconstruct and analyze the temporal aspect of learning in the context of temporal and dynamic logics of belief change.The aim of connecting Learning Theory (LT) and modal logics of belief change is two-fold. By analyzing the temporal doxastic structure underlying formal learning theory, we provide additional insight into the semantics of inductive learning. By importing the ideas, problems and methodology from Learning Theory, logics of epistemic and doxastic change get enriched by new learning scenarios, i.e. those based not only on incorporation of new data but also on generalization, but they also gain new concepts and new problematic perspectives.We will proceed as follows. In Sections 1 and 2 we introduce the basic formal notions of learning theory and modal logics of belief change. In Section 3 we propose a reduction of the learnability task to a generalized problem of DETL model checking. Furthermore, we prove a DETL representation result corresponding to an important theorem from Learning Theory, that characterizes learnability, namely Angluin's theorem. Then we step back and place notions of learning theory and doxastic temporal logic in a common perspective in order to compare them (Section 4). We focus both on the properties of agents and fine-grained notions of belief and knowledge. In Section 5 we consider an extension of the classical learning theoretic framework by introducing more agents and extending the protocols to include a possibility of communication between the agents. In the end we discuss consequences, possible extensions and profits that our work brings.