As Python based VR tools, Vizard and SightLab natively integrate with LLM AI Systems
Read on to find out how you can use our built-in AI functions and templates for your own VR experiments.
Overview
Generative AI has leapt from novelty to indispensable lab gear: avatars that reason, stimuli that appear on command, analytics that label gaze data for you. The question for scientists is no longer if AI belongs in VR studies, but where it measurably boosts rigor. This issue spotlights the SightLab VR and Vizard integration points—controllable conversational agents, instant 3-D model builders, adaptive trial logic—already wired into the WorldViz tool-chain. Use them to swap spectacle for statistical power and launch reproducible experiments faster.
The Role of AI in VR Research
AI enhances VR research across several key domains. Here are some tools and features that can be leveraged, including built-in examples and templates you can use out of the box with WorldViz software.
1 | Custom GPT Assistant for SightLab
Generate complete experiment code from simple prompts
A unique and powerful tool included with SightLab is the Custom GPT Assistant, specifically trained on the comprehensive SightLab and Vizard codebases, APIs, and detailed experiment design documentation. This assistant significantly streamlines the development process by:
Deeply understanding the SightLab API and the broader Vizard framework, enabling rapid and accurate code suggestions and debugging.
Assisting with advanced experimental designs by providing tailored Python scripts, optimized parameter configurations, and experimental logic guidance.
Instantly generating and customizing scripts using built-in methods such as sightlab.addSceneObject, sightlab.startTrial, and sightlab.showRatings, drastically reducing coding overhead.
Offering quick documentation lookups, parameter suggestions, and ready-to-use experiment templates, enhancing productivity for both novice researchers and seasoned programmers.
By leveraging this GPT-powered assistant, researchers save considerable time on technical setup and troubleshooting, allowing them to focus more fully on the creative and analytical aspects of their VR experiments. Researchers can now use “vibe coding” to generate fully working VR experiments in a very short amount of time.
Build customized intelligent avatars and converse directly with LLMs
SightLab includes several frameworks to support AI-driven conversational agents:
Real-Time Conversational Interaction - Participants can talk naturally to avatars powered by ChatGPT, Claude, Gemini with vision, or offline Ollama-hosted models like LLaMA or DeepSeek.
Custom Agent Design - Agents can have specific personalities, emotional states, and contextual memory, adapting their responses based on prior interaction history.
Multi-Agent Support - Run scripts like multi_agent_interaction.py to create dynamic scenes where two or more AI agents converse and respond to the user in real time.
Speech Recognition & High-Fidelity Voices - Supports speech-to-text input and a wide selection of synthesized voices via OpenAI, ElevenLabs, or offline TTS options.
AR Compatibility - Works seamlessly in passthrough augmented reality on devices like Meta Quest Pro, Quest 3, and Varjo.
Assist with Collaborative VR Lessons - Add an AI Agent to a virtual lesson to assist in specialized topics.
Environmental and Avatar Customization - Fully compatible with ReadyPlayerMe, Mixamo, Rocketbox, and more. Modify animations, positions, morph targets, or lighting.
Vision Capabilities - Gemini agents can "see" the VR environment via screenshots and respond accordingly—ideal for agents that adapt to spatial context.
Perform studies with customizable avatars - Easily changing characteristics such as race, gender, voice, and other attributes to measure nuanced participant reactions, perceptions, and behaviors.
Write detailed, repeatable text scripts performed by AI voice models
The Scripted Avatar Example allows avatars/agents to read prewritten scripts from text files using OpenAI or ElevenLabs text-to-speech. Avatars can be customized and randomized per trial, with full integration into SightLab's gaze and interaction tracking system.
Design branching studies that adapt to user interactions, enabling virtual agents to respond dynamically based on predefined parameters. Include modifiable conditions such as race, gender, and demeanor, and extract interaction data for detailed analysis.
4 | Educational AI Interaction Tool & Object Identification
Enrich VR / MR environments with customizable LLM descriptors
The Educational Interaction Tool in SightLab integrates advanced conversational AI models such as Chat GPT and Claude, enabling immersive, interactive educational scenarios. Researchers can:
Automatically tag scene objects, which can trigger detailed audio explanations, and allow users to ask AI-driven follow-up questions.
Adapt interactions dynamically based on conversation history and user behavior, enhancing immersive learning experiences.
This tool integrates seamlessly into SightLab’s VR/XR environments and is ideal for developing collaborative immersive E-Learning lessons and interactive training modules with the Multi User Sightlab and the E-Learning Lab.
5 | Dynamic Content Generation (Live or Pre-Rendered)
Build VR content with GenAI for 3D models, 360 images, and more
Generative AI (GenAI) technologies significantly expand creative possibilities for researchers by dynamically generating and customizing experimental stimuli and environmental elements. SightLab integrates several built-in real-time content generation tools and supports seamless integration with leading GenAI platforms.
SightLab Integrated Tools:
3D Model Spawner: Leverages Meshy and MasterpieceX APIs to allow researchers and participants to create custom, interactive 3D models instantly from text or spoken prompts (e.g., "a glowing mushroom"). These generated assets include built-in gaze and grab interaction capabilities, which are automatically logged for analysis.
AI Image Spawner: Utilizes OpenAI’s DALL·E 3 to dynamically produce high-quality textures and images from descriptive prompts (e.g., “a mountain at sunset”) for immediate application onto 360 media or panoramic backgrounds in the VR environment. This is particularly valuable in emotional and perceptual research contexts.
GenAI VR Content Possibilities:
Modality
Example Service
Application Ideas
360° Panoramas
Blockade Labs Skybox, Midjourney “–pano”
Generate immersive background scenes for environmental studies, mood induction, or spatial navigation tasks.
Audio Generation
ElevenLabs, OpenAI TTS, Suno v3
Create custom narration, dynamic audio cues, music, or realistic soundscapes tailored specifically to the experimental scenario.
Video
Runway Gen-3, OpenAI Sora
Produce video sequences or animations to project onto screens within the VR environment or use as dynamic stimuli in tasks involving attention and perception.
NeRF / Radiance Fields
Luma Field Editor
Develop highly realistic spatial renderings for interactive walkthroughs and complex spatial tasks.
Image to 3D
Kaedim, Meshy
Automatically convert images or conceptual sketches into detailed 3D models for use in object recognition and memory tasks.
HD Textures
Adobe Firefly 3
Dynamically alter object appearance for perceptual tasks involving texture discrimination or visual acuity assessments.
SightLab's dynamic content generation capabilities, when combined with powerful AI-driven tools, significantly streamline experiment creation, provide unmatched creative flexibility, and enable researchers to explore innovative experimental designs with unprecedented ease and efficiency.
With these AI-powered tools, researchers can effortlessly generate custom 3D assets and have them integrated into SightLab scenes within minutes, either in real time or by quickly rendering online and importing them directly.
Check out these other AI tools to supercharge research
Artificial Intelligence (AI) tools enhance scientific research by streamlining literature reviews, data analysis, and content generation. Popular tools include:
Semantic Scholar: AI-driven literature search tool summarizing papers and recommending related content.
Consensus: Utilizes GPT-4 and NLP to analyze scholarly content and deliver expert-curated insights.
ChatPDF: Interactive AI platform for querying information directly from PDF documents.
Scite Assistant: Provides citations and assists in validating arguments by analyzing published research.
Elicit: AI assistant supporting idea generation, research organization, and presentation preparation.
Research Rabbit: Creates personalized research paper collections, visualizes scholarly networks, and recommends relevant publications.
SciSpace: Simplifies manuscript submission, peer review, and publication processes, accelerating research dissemination.
Maintaining Academic Integrity: When using AI tools, researchers should ensure ethical usage by appropriately citing sources, paraphrasing, proofreading AI-generated content, and clearly understanding each tool’s purpose to avoid plagiarism and uphold rigorous academic standards.
Conclusion
WorldViz’s software represents a convergence of AI and VR in the service of behavioral science, psychology, education, and beyond. With tools that allow real-time model generation, intelligent agents, and integrated coding support through a custom GPT, researchers now have a flexible and powerful ecosystem for running cutting-edge immersive studies.
