AI-Enabled Venture Pitch Evaluation: A Scalable Platform for Experiential Entrepreneurship Education

Experiential entrepreneurship education frequently requires students to develop venture ideas and present them through investor-style pitch decks. Such activities simulate real entrepreneurial processes and help students understand how investors evaluate entrepreneurial opportunities. However, providing meaningful and timely feedback for student pitches remains challenging. Authentic assessment often involves multiple evaluators to simulate venture capital panels, yet coordinating judges, aligning scoring standards, and consolidating feedback can be time-consuming and difficult to scale, particularly in large classes.

This project introduces an AI-enabled Venture Pitch Evaluation Platform designed to support scalable and customizable assessment of student venture pitches. The web-based platform allows students to submit pitch decks and receive structured evaluation through a flexible system that combines simulated evaluators with real human reviewers.

A central feature of the platform is the ability for instructors or users to design evaluator personas. Users can specify characteristics such as background, expertise, and demographic attributes—for example, “male American business school professor and part-time angel investor” or “female Chinese experienced venture capitalist.” An embedded algorithm translates these narrative descriptions into evaluation parameters, generating simulated evaluators with different scoring tendencies and decision weights. This allows courses to simulate diverse investor perspectives commonly found in entrepreneurial ecosystems.

The platform also enables instructors to control the evaluation process. Instructors can determine how many evaluations each pitch deck receives, define customized evaluation criteria, and design both quantitative scoring metrics and qualitative feedback prompts. In addition, the system supports the integration of real human evaluators, enabling hybrid evaluation models that combine expert feedback with AI-assisted assessment. To facilitate ease of use, the platform includes default settings and preset evaluator personas, allowing instructors to quickly deploy common evaluation scenarios such as venture capital panels, angel investor reviews, or accelerator-style judging.

By transforming pitch evaluation into a configurable digital infrastructure, the platform enables scalable multi-perspective feedback, reduces instructor grading workload, and helps students better understand how different stakeholders evaluate entrepreneurial opportunities. The system also generates structured evaluation data and analytics that can support teaching improvement and research on entrepreneurial judgment. At the HKU Teaching and Learning Festival, this project demonstrates how AI can enhance experiential entrepreneurship education by simulating realistic evaluation environments while maintaining flexibility and scalability.