Abstract Purpose This study explores the patterns of critical thinking in human-AI co-creation tasks and how they relate to creative performance, aiming to clarify the relationships between these patterns and creative performance and deepen understanding of the cognitive processes. Design/methodology/approach The dataset comes from human-AI dialogue data for two level creativity tasks, text summarization and creative writing, on the Hugging Face platform. A critical thinking coding scheme is developed to analyze critical thinking skills, then lag sequential analysis (LSA) and frequent sequence mining (FSM) are used to identify the critical thinking patterns. Statistical methods are employed to explore associations between these patterns and creative performance. Findings Six task-dependent critical thinking patterns are identified in human-AI co-creation: Explanatory consolidation (EC), analytical structuring (AS), evaluative specification (ES), evaluative decomposition (ED), evaluative regulation (ER) and inferential self-loop (IS). Whereas low and medium-order thinking skills (patterns EC, AS, ES) dominated the low-creativity task, the high-creativity task incorporated low, medium and high-order skills (patterns ES, ED, ER, and IS). Furthermore, these patterns show associations with creative performance in the creative writing task: the ER pattern supports comprehensive creativity, the ED and ES patterns facilitate partial creativity, the IS pattern acts as a limiting factor. Research limitations Reliance on merged large-scale datasets analyses may constrain interpretation of human-AI co-creation, and future studies should validate these findings through experimental approaches and single-dataset replication. Practical implications The findings can help educators design tasks at different levels of creativity to elicit students’ critical thinking, and help writers design personalized prompts that enhance specific dimensions of creativity (originality or elaboration) in line with their goals. Originality/value By adapting frameworks of critical thinking to human-AI co-creation, the study contributes to a comprehensive understanding of critical thinking patterns, compares these patterns across different creative tasks, and uncovers their associations with creative performance.
Chen et al. (Sat,) studied this question.