The current method to implement motivation is some kind of artificial reward. Deepmind's DQN for example is driven by the score of the game. The higher the score, the better. The AI learns to adjust its actions to get the most points and therefore the most reward. This is called reinforcement learing. The reward motivates the AI to adapt its actions, so to speak.
In a more technical term, the AI wants to maximize utility, which depends on the implemented utility function. In the case of DQN, this would be maximizing the score in the game.
The human brain functions in a similar fashion, although a little more complex and often not as straight forward. We as humans usually try to adjust our actions to produce a high output of dopamine and serotonin. This is in a way similar to the reward used to control AIs during reinforcement learning. The human brain learns which actions produce the most amount of those substances and finds strategies to maximize the output. This is, of course, a simplification of this complex process, but you get the picture.
When you talk about motivation, please don't mix it up with consciousness or qualia. Those are not required for motivation at all. If you want to discuss consciousness and qualia in AI, that's a totally different ball game.
A child isn't curious for the sake of curiosity. It gets positive reinforcement when exploring because the utility function of the child's brain rewards exploration by releasing rewarding neurotransmitters. So the mechanism is the same. Applying this to AI means defining a utility function that rewards new experiences. There is no inner drive without some kind of reinforcing reward.