Hybrid intelligence feedback systems in design thinking development: Stage‐specific insights on pedagogical effects and characteristics of generative AI and instructors
Fostering design thinking relies on instructional feedback, yet traditional instructor‐led methods face practical limitations. Although generative AI (GenAI) may offer promising solutions based on its advantages, existing studies predominantly characterise it as a performance‐enhancing task assistant rather than as a feedback‐providing pedagogical agent for the intrinsic growth of design thinking. The study compares the pedagogical effects on students' design thinking and students' perceptions of feedback systems by GenAI and human instructors. A within‐class randomised experimental design was conducted with 80 undergraduates. Results indicate no significant overall difference in learning gains, but reveal respective stage‐specific strengths: GenAI proved more effective during the ‘empathise’ stage, associated with superior timeliness and privacy, while human instructors excelled in the ‘prototype’ stage, valued for their contextual anchoring. Comparable effects yet complementary functions were observed in other stages, reinforcing GenAI's augmentative role instead of substitution. Findings collectively demonstrate that the effectiveness and strengths of feedback systems are not static and uniform but dynamically aligned with the learning demands across educational settings. Practical insights for designing hybrid intelligence feedback systems and theoretical implications were finally discussed. Instructional feedback is crucial for fostering design thinking; however, traditional instructor‐led feedback is often constrained by issues of scalability, timeliness and consistency. Generative AI (GenAI) shows promise to serve as an additional feedback agent, but its role has been explored more as a performance‐enhancing task assistant rather than a feedback‐providing pedagogical agent for the intrinsic growth of design thinking. The study demonstrates that GenAI leads to greater learning gains in the ‘empathise’ stage, while human instructors are more effective in the ‘prototype’ stage. The study underscores their comparable effect while serving distinct and complementary roles at ‘define’, ‘ideate’ and ‘reflect’ stages. The study highlights their respective stage‐specific strengths and associates them with learning gains across the design thinking process. The study provides practical insights for designing hybrid feedback systems that strategically orchestrate GenAI and human instructors to maximise pedagogical impact. Educational policy should promote the design of hybrid feedback systems that strategically align the inherent characteristics of different feedback agents with the stage‐specific learning demands across various educational contexts. Curriculum development should emphasise building students' relevant competencies (e.g., feedback literacy, AI competency, etc.), equipping them to seek, evaluate and synthesise input from multiple agents effectively.