Beyond expert knowledge toward idea innovation: potential and challenges of a generative AI-supported jigsaw method

Abstract Recent societal issues require higher education institutions to engage students in interdisciplinary knowledge creation beyond knowledge acquisition. While the jigsaw method is expected to facilitate novice learners’ engagement in interdisciplinary collaborative knowledge creation, authoritative expert materials limit learners’ agency beyond the given knowledge. To overcome this constraint, this study proposes the Knowledge Expansive Jigsaw Method, which integrates Generative AI (GenAI) as a resource for expanding ideas beyond expert materials. Using a two-cycle design-based research approach with 70 freshmen using a conjecture map as our framework, we investigated how the implemented GenAI-supported jigsaw method promotes students’ ideation processes. Specifically, students designed illustrations of the “Future classroom of our university in 2050.” The instructional design introduced an expansion phase after a cognitive trigger and positioned GenAI as both a possibility engine and a co-designer. Students’ discourse, GenAI usage records, and final presentations were analyzed using the Knowledge-Building Discourse Explorer (KBDeX) and rubric-based evaluation. KBDeX analysis confirmed the co-occurrence of AI-inspired and human-generated idea expansion across groups. Comparison of the outcomes did not demonstrate improvements after design refinement in Cycle 2; whereas outcomes in Cycle 1 were diverse, those in Cycle 2 converged at an emerging level. The results suggested both the potential—the proposed design consistently promoted idea expansion beyond expert knowledge—and the challenge—high-level idea innovation were rare under the GenAI-supported jigsaw method. Future studies should focus on refining AI systems, instructional designs toward idea innovation and analysis to identify and validate other promising processes critical for idea innovation.