Collaborative knowledge construction with generative AI: Exploring argumentative co-writing processes through n-gram and cluster analysis

Since the beginning of CSCL research, collaborative writing has been playing a pivotal role as a tool for learning and knowledge construction. In the study presented here, we ask to what extent large language models may not only assist individuals in their writing processes but also serve as a collaboration partner. For this purpose, we analyzed the writing process of individuals supported by ChatGPT. We introduce the use of recurring n-grams as a means for textual uptake, that is, the extent and granularity with which human writers adopt and adapt AI-generated text. Based on the overlaps between the ChatGPT output and participants’ final texts, we identified clusters of text reproducers, integrators, and reconstructors. Participants in these clusters differed not only in their subjective contributions and authorship, but also in their prior use of ChatGPT and their affinity of technology interaction. Referring to the conceptualization of interindividual interactions as uptake events, we suggest that n-grams are adequate means to analyze the uptake process in AI-supported human writing. This may offer further opportunities to understand AI-supported writing not as a mere individual activity but as processes of knowledge transformation and co-construction.