SBIR Fast-Track: A Proprietary Argumentation Heuristic Framework for Structured Reasoning and Logic Evaluation in Presentation Authoring

The broader/commercial impact of this Small Business Innovation Research (SBIR) Fast-Track project introduces a technical innovation in cognitive science and human-AI collaboration that transforms software from a passive design tool into an active thinking partner. By capturing full-sentence arguments rather than fragmented bullet points, the technology ensures that the underlying logic of a presentation is sound before a single slide is designed. The primary market opportunity targets data-heavy sectors like marketing and risk analysis, where clear communication is a critical factor for commercial success. The business model follows a software-as-a-service approach, providing a durable advantage through a proprietary system that improves as it learns from expert feedback. This Small Business Innovation Research (SBIR) Fast-Track project addresses the technical challenge of translating rules for creating clear presentations into objective heuristics implemented by an automated system of coaches. Current AI Presentation platforms focus on aesthetic improvement, and when they critique or create content, they rely on generic writing coaching combined with rhetorical styles learned from ineffective presentations. In contrast, this project translates decades of proprietary research and active application into prescriptive rules for clear argumentation for presentations and embeds those rules in a novel multi-agent orchestration layer. The research objectives include the development of a proprietary evaluation engine that critiques user input to improve both clear reasoning (e.g., expressing claims as full sentences instead of bullets) and engaging rhetoric (for example, positioning an argument to match a goal that the audience cares about). The anticipated technical results include a validated framework for refining agent orchestration based on expert-led feedback loops and automatically rendered slides from a structured argument interface. This work advances the field of human-centered artificial intelligence by creating an architecture that prioritizes structured thinking over simple generative output, providing a new foundation for high-stakes organizational communication and advancing understanding of computer-supported collaborative work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2605171 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Steven Franconeri | Institution: BECISE LLC, EVANSTON, IL | Award Amount: $1,554,830 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2605171 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2605171.html