A Pilot Educational Framework for AI-Enhanced Digital Manufacturing and Reflective Skill Development

In digital manufacturing education, integrating artificial intelligence with structured reflective learning may accelerate novice designers’ creative development. The study introduces an AI-Enhanced Reflective Design Framework (AERDF), which integrates AI-driven feedback, scaffolded reflective journaling, and iterative digital fabrication tasks to support reflective learning in design education. To investigate this, fifty undergraduate students (18–24 years) participated in a two-week intensive pilot program featuring three iterative design cycles of 3D-printed chaotic attractors and 2D tessellation patterns. Each cycle combined automated AI-driven analysis of aesthetic and structural metrics with peer-led reflection sessions and prototype refinement. Artifact quality ratings increased from a mean of 3.2 to 3.9 (22 % improvement), design fluency rose by 30 % and originality by 25 %, while personalization scores correlated strongly with the proportion of peer suggestions incorporated (r = 0.62). Self-reported gains expanded from three core domains in Week 1 to eighteen distinct improvement categories in Week 2, and reflective challenges evolved from four broad barriers to eighteen nuanced metacognitive obstacles. Preliminary regression analyses suggest that initial peer-review prompts and journal entries in Week 1 yielded moderate gains in perceived effectiveness (slope ≈ 0.413, R² ≈ 0.207), whereas the full two-week combination—adding AI feedback and advanced metacognitive prompts—produced stronger improvements in this pilot context (slope ≈ 0.612, R² ≈ 0.923). These results demonstrate that embedding AI-augmented feedback within a scaffolded reflective framework yields statistically suggestive trends in creative performance and self-awareness, offering a scalable model for fostering innovation, adaptability, and critical thinking in design curricula.