Using customized GPT to develop prompting proficiency in architectural AI-generated images
Project Overview
The document examines the application of customized generative AI models, specifically GPT, in architectural education to improve students' skills in prompt engineering for generating AI-driven images. It emphasizes the growing significance of prompt engineering as a crucial skill due to the increasing prevalence of generative AI tools in the field. The research highlights the effectiveness of structured prompting guides and the use of AI personas in fostering critical thinking and enhancing communication skills among architecture students regarding architectural concepts. Findings indicate that personalized interactions with AI not only bolster students' confidence but also lead to better articulation of design ideas, ultimately resulting in improved outcomes in prompt generation. This study underscores the transformative potential of generative AI in education, particularly in developing essential competencies that prepare students for the evolving demands of their professions.
Key Applications
Customized GPT models for prompting proficiency in AI-generated images
Context: Architecture education, targeting architecture students at various educational levels (bachelor's, master's, doctoral)
Implementation: Mixed-methods experimental design with control and experimental groups utilizing structured guides and AI personas
Outcomes: Significant improvements in word count, prompt similarity, and student confidence; enhanced critical thinking skills
Challenges: Steep learning curve for students; potential for confusion with AI feedback; need for clearer definitions of prompting parameters
Implementation Barriers
Technical
Participants faced difficulties with AI feedback, which was sometimes perceived as ambiguous or misaligned with their design intentions.
Proposed Solutions: Improve explainability of AI feedback by attaching rationale and allowing two-way interaction for students to query the feedback.
Cultural
Participants expressed a desire for more culturally diverse image outputs and clearer definitions of architectural terminology.
Proposed Solutions: Incorporate culturally inclusive training data and multilingual support to broaden accessibility.
Educational
Students experienced vocabulary limitations, word count constraints, and challenges related to insufficient background knowledge for articulating complex architectural concepts.
Proposed Solutions: Provide databases of sample keywords and real-time word count indicators to enhance usability.
Project Team
Juan David Salazar Rodriguez
Researcher
Sam Conrad Joyce
Researcher
Julfendi
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Juan David Salazar Rodriguez, Sam Conrad Joyce, Julfendi
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai