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Abstract
Prompt- and image-driven 3D generation can lower the interaction barrier to digital asset creation, yet model-level advances do not establish whether non-specialists can operate a complete authoring workflow. This study designed and evaluated a web-based human–AI prototype integrating a React interface, FastAPI services, ComfyUI workflow orchestration, asynchronous job processing, persistent asset storage, and browser-based 3D visualization. We implemented four modes: Text-to-3D, Text-to-3D with ControlNet, Single Image-to-3D, and Multi Image-to-3D. Evaluation combined 14 requirement-derived black-box scenarios, cross-viewer GLB compatibility checks in three viewing environments, 35 completed generation jobs, task-based observation with seven participants completing 84 task executions, System Usability Scale (SUS) responses from a separate group of 12 students, and one expert assessment of implementation and output quality. All 14 functional scenarios produced the expected outcomes, and the tested GLB outputs opened in the integrated viewer, Blender, and 3dviewer.net. Mean server-side turnaround was 66.15 s; an exploratory Kruskal–Wallis test indicated differences among the observed workload distributions (H(3) = 10.52, p = .015, ε² = .243), but did not support causal or pairwise speed claims. All observed tasks were eventually completed, but only 61 of 84 executions (72.62%) were independent; Tasks 5 (enter Studio) and 10 (find the generated model in My Projects) each required assistance for five of seven participants. Mean SUS was 81.67 (SD = 9.25; 95% CI [75.79, 87.55]), while generated-model quality received a diagnostic expert score of 3.50/5. The study contributes an end-to-end human–AI authoring architecture and a multidimensional evaluation logic that separates functional conformance, deployment turnaround, cross-viewer compatibility, observed interaction, perceived usability, and output quality. Findings support the feasibility of accessible web-based generative 3D authoring while identifying discoverability and geometric-quality constraints.
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Author Contributions
Ray Fadly Hadi: Conceptualization; Methodology; Software; Investigation; Data Curation; Formal Analysis; Visualization; Writing – Original Draft. Randi Proska Sandra: Supervision; Validation; Writing – Review & Editing. Sri Rejeki: Writing – Review & Editing. All authors have read and approved the final version of the manuscript.
Acknowledgments
The authors thank the expert for validation and the Informatics Study Program, Department of Electronics Engineering, Faculty of Engineering, Universitas Negeri Padang, for academic and technical support.
View statement in PDF · PDF page 14Funding
This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.
View statement in PDF · PDF page 14Competing Interests
The authors declare no competing interests.
View statement in PDF · PDF page 14Ethics Approval
The study involved non-sensitive software-usability activities. According to the institutional requirements reported in the research record, formal ethical clearance was not required for this evaluation; participation was voluntary, and no medical, biological, or psychological intervention was conducted.
View statement in PDF · PDF page 14Informed Consent
Informed consent was obtained from all participants before task-based usability testing or completion of the System Usability Scale, as applicable.
View statement in PDF · PDF page 14Consent for Publication
Not applicable. The article does not report identifiable participant images or confidential personal records.
View statement in PDF · PDF page 14Data Availability
Aggregate evaluation results are reported in the article. The corresponding author may provide anonymized participant-level records, de-identified job measurements, and supporting system documentation upon reasonable request, subject to institutional and privacy requirements.
View statement in PDF · PDF page 14Generative AI Disclosure
During manuscript preparation, the authors used ChatGPT (OpenAI) and Claude (Anthropic) for language refinement. These tools were not used to generate, modify, or analyze the research data, usability scores, performance measurements, or expert assessments. All AI-assisted text was checked against the thesis record and cited sources, and the authors accept full responsibility for the final manuscript. Preprint and Prior Dissemination Statement This article is derived from the first author's undergraduate thesis and is not under consideration elsewhere.
View statement in PDF · PDF page 15References
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Ray Fadly Hadi, Randi Proska Sandra, Sri Rejeki