The only known path to general intelligence is that taken by humans. Adapting elements of this path to achieving artificial general intelligence (AGI) has become a common area of interest. We address the role of human teachers in this process, using the concept of the zone of proximal development (ZPD). We explore the range of possible human-teacher interactions, including those modeled closely on humans, those involving accessing and changing the AGI learners internal representations, and tighter integrations amounting to human-AI hybrid learning system (HAIHLS). In such a system, a human teacher scaffolds an untrained subsystem by producing the outputs desired from a fully trained version. Those outputs both train that subsystem and provide more useful information to the remainder of the cognitive system. This aid enables all subsystems to learn within the context of the richer behavior and cognition possible with the aid of the human subsystem.
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