  The emergence of large-language models (LLMs) that excel at code generation
and commercial products such as GitHub's Copilot has sparked interest in
human-AI pair programming (referred to as "pAIr programming") where an AI
system collaborates with a human programmer. While traditional pair programming
between humans has been extensively studied, it remains uncertain whether its
findings can be applied to human-AI pair programming. We compare human-human
and human-AI pair programming, exploring their similarities and differences in
interaction, measures, benefits, and challenges. We find that the effectiveness
of both approaches is mixed in the literature (though the measures used for
pAIr programming are not as comprehensive). We summarize moderating factors on
the success of human-human pair programming, which provides opportunities for
pAIr programming research. For example, mismatched expertise makes pair
programming less productive, therefore well-designed AI programming assistants
may adapt to differences in expertise levels.
