  We propose a learning analytics-based methodology for assessing the
collaborative writing of humans and generative artificial intelligence. Framed
by the evidence-centered design, we used elements of knowledge-telling,
knowledge transformation, and cognitive presence to identify assessment claims;
we used data collected from the CoAuthor writing tool as potential evidence for
these claims; and we used epistemic network analysis to make inferences from
the data about the claims. Our findings revealed significant differences in the
writing processes of different groups of CoAuthor users, suggesting that our
method is a plausible approach to assessing human-AI collaborative writing.
