Using generative AI-enhanced collaborative whiteboards in design thinking education significantly boosts student engagement and higher-order thinking skills, transforming how students approach creative problem-solving.
Objective: The main goal of this study was to investigate the impact of a Generative AI-enhanced Collaborative Whiteboard (GAICW) on student engagement and higher-order thinking skills (HOTs) in design thinking education. Specifically, the researchers aimed to examine whether GAICW could improve behavioral, emotional, and cognitive engagement while enhancing creativity, critical thinking, and problem-solving abilities compared to traditional collaborative platforms in a design thinking project-based learning context.
Methods: The researchers employed a quasi-experimental design with 65 management students (years 2-4) from a comprehensive university in Taiwan. Participants were divided into two groups: an experimental group (n=34) using GAICW (GAI-enhanced FigJam with Jambot) and a comparison group (n=31) using conventional FigJam without AI features. The study took place over six weeks in a design thinking project-based learning (DT-PBL) course focused on creating products or mobile applications to enhance occupational hygiene. The course followed the Double Diamond model with four stages: Discover, Define, Develop, and Deliver. Data were collected through pre- and post-test questionnaires measuring student engagement and higher-order thinking skills using validated 5-point Likert scales. Additionally, qualitative data were gathered through open-ended questionnaires to understand student perceptions. The experimental group utilized Jambot's features including "Teach me about this," "Give me," "Ideate!," "Summarize," "Organize into," and "Custom" functions to support various design thinking activities such as persona development, brainstorming, and idea organization.
Key Findings: The study revealed significant positive impacts of GAICW on multiple dimensions of learning. For student engagement, the experimental group showed statistically significant improvements across all three dimensions: behavioral engagement (F=7.898, p=0.007, partial η²=0.113), cognitive engagement (F=9.524, p=0.003, partial η²=0.133), and emotional engagement (F=8.969, p=0.004, partial η²=0.126), all with moderate effect sizes. Regarding higher-order thinking skills, GAICW significantly enhanced creativity (F=6.903, p=0.011, partial η²=0.100) and critical thinking (F=6.590, p=0.013, partial η²=0.096), both with moderate effect sizes. However, no significant difference was found in problem-solving skills between the groups (F=0.140, p=0.710, partial η²=0.002). Qualitative analysis revealed five key themes: Categorization and Organization (85% of participants), GAI-Assisted Ideation (80%), Visualization and Clarity (60%), Efficiency (50%), and Critical Thinking and Independence (45%). Students appreciated how GAICW helped them organize chaotic information, stimulated creative thinking, provided visual clarity for complex concepts, improved efficiency in brainstorming, while maintaining their critical judgment and independence.
Implications: These findings significantly contribute to the field of AI in education by demonstrating that integrating generative AI into collaborative learning environments can enhance both engagement and cognitive development. The study provides empirical evidence that GAICW serves as an effective cognitive scaffold, particularly during early design phases where students often face creative blocks and ambiguous problem definitions. The research offers a structured framework for educators to integrate GAI into design thinking curricula, transforming teaching approaches from passive instruction to dynamic facilitation. The findings suggest that AI tools can address persistent challenges in design education, such as linear thinking patterns, design fixation, and limited engagement, while fostering twenty-first-century skills including creativity, collaboration, communication, and critical thinking. The study also highlights the importance of maintaining student agency and critical thinking while leveraging AI assistance, providing insights for developing balanced AI-human collaborative learning environments.
Limitations: The study acknowledges several limitations that may affect the generalizability of findings. First, the relatively small sample size (65 students) and focus on a specific educational context (management students in Taiwan) may limit the applicability across diverse educational settings and demographic groups. Second, the six-week intervention duration may have been insufficient to fully capture the long-term effects of GAICW on higher-order thinking skills development. Third, the reliance on self-reported data for both quantitative and qualitative measures introduces potential bias, as students might provide socially desirable responses. The quasi-experimental design, while practical, lacks the randomization control of true experimental studies. Additionally, the study was conducted in a single institutional context with one instructor, which may limit the external validity of the findings across different teaching styles and institutional cultures.
Future Directions: The researchers suggest several avenues for future investigation to address current limitations and expand understanding of GAI in education. Future studies should incorporate larger sample sizes and diverse educational contexts to improve generalizability. Longitudinal research extending beyond six weeks would better reveal the sustained effects of GAICW on learning outcomes and skill development. Given the non-significant findings in problem-solving skills, future research should focus on developing and testing enhanced GAI features specifically designed to support implementation phases, iterative feedback mechanisms, and practical application of ideas. Researchers should also investigate the optimal balance between GAI assistance and student autonomy to ensure that AI tools enhance rather than diminish critical thinking and independent problem-solving abilities. Additional research directions include exploring how different GAI tools and features affect various stages of the design thinking process, investigating the role of instructor training and support in GAICW implementation, and examining the long-term retention of skills and knowledge gained through GAI-enhanced learning environments.
Title and Authors: "Enhancing student engagement and higher-order thinking in human-centred design projects: the impact of generative AI-enhanced collaborative whiteboards" by Yi-Lin Elim Liu, Tseng-Pin Lee, and Yueh-Min Huang.
Published On: April 30, 2025
Published By: Interactive Learning Environments (Taylor & Francis Group)