Interaction Methods in Generative AI Image Tools: A Review of Trends and Design Opportunities Across HCI and Industry

Paper Title

Interaction Methods in Generative AI Image Tools: A Review of Trends and Design Opportunities Across HCI and Industry

Publication Info

• Topic area: Interaction methods and interface design in generative AI image tools.
• Keywords: Generative AI, image generation, HCI, interaction methods, creative processes, tool functionalities, design workflows, prompt guidance, visual interaction.

Background and Problem

• Problem / challenge: Generative AI (GenAI) image tools heavily rely on text prompts, which are often difficult to craft effectively. There is limited synthesis of how these tools support interaction and creative workflows, particularly regarding refinement, evaluation, and precision control.
• Significance: Understanding and improving interaction methods in GenAI tools is critical for enhancing usability and supporting creative workflows in design practices.
• Motivation and related work: Prior work has explored prompt recommendation systems, multimodal interfaces, and text refinement tools but lacks a comprehensive review of interaction methods and their integration into creative workflows. This paper aims to fill that gap by analyzing both academic and commercial GenAI tools.

Solution

• Proposed approach: A systematic review of 37 GenAI image tools (28 academic, 9 commercial) using three analytical frameworks: interaction methods, creative processes, and tool functionalities.
• Novelty:
  1. Introduction of a structured design space for GenAI image tool interfaces.
  2. Identification of nine actionable design opportunities to address gaps in current tools.
  3. Cross-framework analysis of interaction methods, creative processes, and functionalities.
  4. Comparative evaluation of academic and commercial systems.
• Procedure and key techniques:
  • Systematic review of academic tools using the PRISMA framework and analysis of commercial tools based on industry reports and hands-on testing.
  • Development of three analytical frameworks: interaction methods (e.g., input types, control levels), creative processes (e.g., ideation, refinement), and tool functionalities (e.g., generators, editors).
  • Scoring tools on a 0–3 scale across framework dimensions and performing correlation analysis to identify patterns and gaps.

Results

• Concrete findings:
  • Text prompts dominate as input, but visual and attribute-based inputs are gaining traction, particularly in academic tools.
  • Limited support for precision control, parameter control, and structured evaluation workflows.
  • Strong emphasis on early-stage creative processes (ideation, exploration) but weaker support for refinement and evaluation.
• Advantage over baselines:
  • Academic tools lead in prompt guidance and visual-based inputs, while commercial tools emphasize parameter control and global adjustments.
• Experiments / evaluation:
  • Tools were evaluated across interaction methods (e.g., text-based, visual-based inputs), creative processes (e.g., ideation, refinement), and functionalities (e.g., generators, editors).
  • Correlation analysis revealed consistent alignments (e.g., text-based input with ideation) and gaps (e.g., limited integration of visual input with variation exploration).
• Limitations and future work:
  • Focus on 2D, screen-based tools excludes 3D, AR/VR systems.
  • Limited to tools published between January 2022 and July 2025.
  • Future work should expand to spatial interfaces, broader disciplinary sources, and user-centered evaluations.

Summary

This paper provides a systematic review of 37 GenAI image tools, analyzing interaction methods, creative processes, and tool functionalities. Text prompts remain the dominant input, but academic tools are exploring visual and attribute-based inputs. The study identifies gaps in precision control, parameter control, and evaluation workflows, with strong support for early-stage ideation and exploration. Nine design opportunities are proposed to enhance usability and creative support, including advanced visual interaction, simplified parameter control, and integrated workflows. These findings offer actionable insights for designing more effective GenAI image tool interfaces.