An Artificial Intelligence-Generated Content-Enabled Personalized Design Approach for Proactive User Interaction in an Immersive Environment
Bibliographic record
Abstract
Abstract Rapid advancement of artificial intelligence and immersive technologies is revolutionizing various sectors, notably product design. However, the traditional personalized design process, which depends on predefined elements with limited user input, often results in products that do not fully align with individual preferences and lack substantial user engagement. To fill this gap, the emergence of artificial intelligence (AI)-generated content (AIGC) presents a significant opportunity for mass personalization through natural language interactions. Inspired by this paradigm, this article proposes an AIGC-enabled personalized product design approach, which integrates a configuration retrieval model with a fine-tuned text-to-3D generative model (TAPS3D model), enabling users to create personalized products within an immersive environment. While the current system requires approximately two minutes for 3D shape generation, this level of responsiveness is considered suitable for concept exploration in early-stage design workflows, where rapid iteration is prioritized over instantaneous feedback. Furthermore, a case study is conducted focusing on the design of personalized steering wheels to demonstrate the feasibility of this methodology. Furthermore, the effectiveness of the proposed approach in improving user experience is evaluated using a comparative experiment with the traditional configuration system. The findings indicate that our proposed AIGC-enabled personalized design system effectively enhances personalization, facilitates user engagement, improves the interaction experience, and increases user satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".