The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25)

Collocated with AAAI-25 

The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25) is a venue for papers describing highly innovative realizations of AI technology. The objective of the conference is to showcase successful applications and novel uses of AI. The conference will use technical papers, best practice papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of AI applications; and to promote an interchange of ideas between basic and applied AI and the discourse on the actual deployment of AI in practice. The general goal of the conference is to teach people the challenges and solutions to accomplishing something useful in the real world, as opposed to describing a new algorithm.

Deployed Highly Innovative Applications of AI​ 

Papers submitted to this track must describe deployed applications with measurable benefits that include an innovative use of AI technology. Applications are defined as deployed once they are in production use by their final end-users and the in-use experience can be meaningfully collected and reported. The study may evaluate either a stand-alone application or a component of a complex system.  

XCotton: Advancing AI-enabled Hardware/software Integrated System for Foreign Fiber Cleaning

Adaptive Merchant-Centric Risk Control via Unbiased Decision-Making and Dynamic Optimization in E-Commerce

CVE-LLM : Ontology-assisted automatic vulnerability evaluation using large language models

AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction

Knowledge Tagging with Large Language Model based Multi-Agent System

Developing Generative Recommender Systems for Government Subsidy Pro-grams with a New RQ-VAE Model: Wello & the Korean Government Case

IMQC: A Large Language Model Platform for Medical Quality Control

A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial

OnAIR: Applications of The NASA On-Board Artificial Intelligence Research Platform

Exploring the Efficacy of Multi-Agent Reinforcement Learning for Autonomous Cyber Defence: A CAGE Challenge 4 Perspective

BoolXAI: Explainable AI using Expressive Boolean Formulas

Emerging Applications of AI 

Emerging applications papers ‘bridge the gap’ between basic AI research and case studies of deployed AI applications, by discussing efforts to apply AI tools, techniques, or methods to real-world problems in novel ways. 

ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement

GCF: Estimating unobserved demand using Graph Causal Forecasting

SNAP: Semantic Stories for Next Activity Prediction

Hotspotter: A Generalizable Pipeline for Automated Detection of Subtle Volcanic Thermal Features in Satellite Images

Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach

Structured Document Generation for Industrial Equipment

An Application-Agnostic Automatic Target Recognition System Using Vision Language Models

Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms

Automating the Expansion of Instrument Typicals in Piping and Instrumentation Diagrams (P&IDs)

ECLAIR: Enhanced Clarification for Interactive Responses

Innovative Inter-disciplinary AI Integration 

This track is devoted to the integration of AI components with the focus on how the orchestration of methods from different AI silos requires the adaptation of existing technologies to allow them to work together well for application of AI in practice. 

From Crayons to Code: \AI-Driven Insights into Child’s Mental Health Through Drawings

The POWER of Ikigai: Optimizing Life Fulfillment with an Integrated User Simulator and Adaptive Hobby Recommender

AI Incidents and Best Practices 

Papers in this track analyze the factors related to AI incidents and the best practices for preventing or mitigating their recurrence. 

Lessons for Editors of AI Incidents from the AI Incident Database

To Err is AI : A Case Study Informing LLM Flaw Reporting Practices

Evaluation and Incident Prevention in an Enterprise AI Assistant

IAAI-25 Co-Chairs

Jan R. Seyler (Festo, Germany) 

Serdar Kadioglu(Brown University, USA)

Sean McGregor (UL Research Institutes, USA)

IAAI-25 Outreach Chair

Eren Kurshan (Princeton University, USA)