The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order to automatically improve themselves. How a learning environment should adapt to students is dependent on the pedagogical approach. In this thesis, we contribute to adaptive and self-improving learning environments that support students during inductive reasoning activities. Inductive teaching is a pedagogical approach that yields very beneficial outcomes for students, but has not received sufficient technological support. Using inductive reasoning, students infer general rules or concepts from observations. This pedagogical approach is motivated by the idea that learning outcomes will increase when students construct the knowledge by themselves. First, we contribute to student modeling and self-improvement for learning environments. We share the results of a study that we conducted using data collected in several large classrooms. We develop a mechanism that collects data, estimates the progress of students and predicts in real-time their future progress. The goal of our approach is to aggregate the predictions to support adaptive decisions by teachers in classrooms. Moreover, we share the results of a second study with MOOC students. We use a generative model, based on a Semi-Markov Chain, to model and simulate sequences of actions taken by students on the MOOC platform. Additionally, based on observations of students from arbitrary Bayesian models, we propose and analyse a self-improving algorithm relying on Thompson Sampling optimisation that maximizes students learning outcomes over time. Second, we contribute to the analysis of inductive teaching and individual differences in inductive reasoning. We report results on an experiment with 222 students solving tasks of categorisation of images. Our study revealed that students individually differ in how they choose to classify examples based on feature differences between the examples. We define the individual differences of students as their inductive bias. This result motivates the importance of adaptivity in the selection of examples during inductive learning activities. Additionally, we analyse how much students change their inductive bias when confronted to negative feedback. We find that students are influenced by the feedback, but that this influence decays after a short period of time. Third, we contribute multiple algorithms that constitute together the necessary components of an adaptive and self-improving learning environment for inductive teaching. Notably, we designed algorithms for a learning environment to extract representations of students' biases from data, estimate and trace individual students' biases, and to optimally select personalised examples for an inductive learning activity. Finally, we conclude by describing the concept of probabilistic testing as a promising assessment mechanism for inductive learning environments. We provide preliminary observations and theoretical results for the use of probabilistic testing in the context of adaptive inductive teaching. In particular, by using probabilistic tests, a learning environment can estimate more quickly students' inductive biases.