In the AI era: A project-based digital storytelling framework for art and design education

Synthesis: As Generative AI increasingly automates technical production in the creative industries, this study asks how art and design education should evolve to preserve and cultivate the deeply human capacities—emotional resonance, cultural interpretation, and narrative meaning-making—that AI lacks. The authors propose and evaluate a Project-Based Digital Storytelling (PBL-DS) Pedagogies and Teaching Strategies that positions AI as a supportive resource inside a structured creative process rather than an autonomous source of creativity.

The model is grounded in an integrative theoretical framework: Constructivism learning (Vygotsky), Dewey's Experiential Learning, and Amabile's Componential Model of Creativity, operationalized through the four-stage digital storytelling production process (pre-production, production, post-production, distribution). It was implemented and studied as an embedded case study of the 15-week "Creative Shanzhou" capstone project, run by the School of Art and Design at a Chinese university in partnership with the local Cultural and Tourism Bureau, in which 426 final-year undergraduates (guided by 48 mentors, ~1:9 ratio) translated local cultural heritage into Multimodal AI narratives. The research used a case-based, multi-method design combining expert evaluation of creative products, pre/post creative-thinking Assessment, and qualitative project analysis.

Key Findings

• Creative product outcomes were strong overall, strongest in Creativity's originality dimension: expert ratings of the 92 digital storytelling works gave novelty the highest mean (M = 4.21, SD = 0.72), followed by effectiveness (M = 3.96) and wholeness (M = 3.68), indicating students generated original ideas more successfully than they integrated and refined them.
• Wholeness showed the most variability (43% of projects scored below 3.5), pointing to extended Scaffolding needs for integrating narrative, technical execution, and multimodal coherence.
• Creative thinking gains: In the 31-student Animation subgroup, figural TTCT scores rose from 77.23 pre-test to 94.68 post-test—a mean difference of 17.45, t(30) = 4.55, p < .001, Cohen's d = 0.82 (large effect), with good inter-rater reliability (overall ICC = 0.86).
• AI served as a mediated tool, not a creator: Across representative projects (e.g., The Dream of Shanzhou, 3D Journeys of Ancient Shanzhou, Mermaid Legacy), AI supported information organization, visual reference, ideation, and technical editing (e.g., Runway), while students retained ownership of topic selection, narrative interpretation, cultural meaning-making, and final creative decisions.
• Human intentionality drives quality: the top-scoring wholeness example (Mermaid Legacy, 5/5) attributed its integration to sustained human engagement with source material and iterative refinement rather than technical polish from AI tools.
• Real-world validation: the project culminated in a three-day public exhibition of all 92 works, and 31 works were subsequently selected by the Cultural and Tourism Bureau for practical implementation, affirming the model's authentic, community-engaged impact.

Implications for Practice

• Structure AI use within a creative process. Embed Generative AI across production stages for research, ideation, prototyping, and editing, but scaffold students to direct, evaluate, and integrate AI output against their own narrative and emotional goals—treating AI as an "inspiration engine" and "technical assistant," not an autonomous author.
• Anchor learning in authentic, community-engaged projects. Pair PBL with fieldwork and real-world audiences (here, local heritage and a Cultural and Tourism Bureau partner) to drive the intrinsic task Motivation that Amabile's model identifies as essential and to supply lived, firsthand material that AI cannot replicate.
• Prioritize extended scaffolding for integration. Given lower wholeness scores, dedicate additional support—weekly task statements, progress reviews, and iterative feedback loops—to narrative coherence, technical execution, and multimodal synthesis, not just idea generation.
• Maintain favorable mentor-to-student ratios (roughly 1:9 here) to provide consistent, personalized guidance, especially in developing students' critical and strategic use of AI as a complement to rather than substitute for human judgment.
• Use public presentation and critique as authentic assessment and as a driver of reflection on authorship, originality, and creative responsibility—core to AI literacy in creative fields.

Connected Concepts

• Project-Based Learning
• Storytelling in Education
• Generative AI
• Creativity
• Higher Education
• Experiential Learning
• Constructivism
• Authentic Assessment
• Human AI Collaboration
• Design Thinking
• Learner Agency
• Arts, Design and Media Education

Connected Articles

• MotiBo: The Impact of Interactive Digital Storytelling Robots on Student Motivation Through Self-Determination Theory — shares digital storytelling as a motivating pedagogical medium (with social robots), complementing this study's PBL-DS framing.
• Development and applications of Generative AI in architectural design studios — examines GenAI integration in a parallel design-discipline studio context, reinforcing how AI reshapes creative education.
• Enacting Constructive Conflicts with AI Agents to Enhance Reconsideration among Novice Interaction Designers — another design-education study of AI in collaborative creative learning, contrasting with this human-centered PBL-DS model.

Citation

In the AI era: A project-based digital storytelling framework for art and design education — Tian, Y., Tang, M., Li, G., & Dang, W. (2026). Computers and Education: Artificial Intelligence, 11, 100645.