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Prime design goals for next-generation wireless networks to support emerging applications are spectral efficiency and low operational cost. Among a gamut of technical solutions, cognitive approaches have long been perceived as a catalyst for the above goals by facilitating the coexistence of primary and secondary users by means of efficient dynamic spectrum management. While most available techniques today are essentially opportunistic in nature, a truly cognitive device needs to exhibit a certain degree of intelligence to draw optimum decisions based on prior observations and anticipated actions. Said intelligence however, comes along with high complexity and poor convergence, which currently prevents any viable deployment of cognitive networks. We thus introduce an emerging and largely unexplored concept of docitive networks, where nodes effectively teach other nodes with the prime aims of reducing cognitive complexity, speeding up the learning process, and drawing better and more reliable decisions. To this end, we review some important concepts borrowed from the machine learning community for both centralized and decentralized systems, in order to position the emerging docitive with known cognitive approaches. Finally, we validate introduced concepts in the context of a primary digital television system dynamically coexisting with IEEE 802.22 secondary networks. For this scenario, we demonstrate the superiority of various unprecedented docitive over known opportunistic/cognitive algorithms.