# Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load

arXiv: https://arxiv.org/abs/2601.10696 · HTML: https://mlsanity.com/paper/2601.10696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 82 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces](https://mlsanity.com/paper/2309.14459/) (2023)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [The Effects of Generative AI on Design Fixation and Divergent Thinking](https://mlsanity.com/paper/2403.11164/) (2024)
- [Who is using AI to code? Global diffusion and impact of generative AI](https://mlsanity.com/paper/2506.08945/) (2025)
- [AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers](https://mlsanity.com/paper/2502.14747/) (2025)
- [Generative AI Models for Different Steps in Architectural Design: A Literature Review](https://mlsanity.com/paper/2404.01335/) (2024)
- [ID.8: Co-Creating Visual Stories with Generative AI](https://mlsanity.com/paper/2309.14228/) (2023)
- [DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation](https://mlsanity.com/paper/2411.16301/) (2024)
- [Directive, Metacognitive or a Blend of Both? A Comparison of AI-Generated Feedback Types on Student Engagement, Confidence, and Outcomes](https://mlsanity.com/paper/2510.19685/) (2025)

## Abstract (arXiv)

Generative AI (GenAI) is increasingly adopted in design education, yet evaluating its educational value through final outcomes provides an incomplete picture. This study compares two ecologically plausible workflows in an architectural conceptual design task: GenAI-assisted image generation and ArchDaily-based precedent search. The comparison concerns complete workflows rather than the isolated contributions. Thirty-six students completed a two-phase design task, first designing independently and then revising with their assigned workflow. Eight judges rated design performance, while participants reported task-specific and general creative self-efficacy and cognitive load after each phase. Difference-in-differences analyses showed no significant overall differences between the GenAI and precedent-search workflows in design performance, cognitive workload, or task-specific creative self-efficacy. Beyond these null overall effects, three patterns were observed. General creative self-efficacy showed a significant relative decline under the GenAI workflow. A subgroup analysis suggested higher revision-phase performance among novice students using GenAI than among those using precedent search (F (1,32) = 4.303, p = 0.046). However, this exploratory interaction should be interpreted cautiously due to low rating reliability, small subgroup cells, and imprecise estimation. Third, exploratory prompt analyses suggested that iterative, task-specific prompting strategies (CD3, CD6) were associated with cognitive load reductions at the uncorrected level, but neither association survived multiple-comparison correction. Overall, the GenAI workflow did not produce uniform gains. Its educational value may depend on pedagogical framing, learner characteristics, and human-AI interaction structure, underscoring the need to preserve creative agency and develop prompt literacy.