AI Entrepreneurship Incubator
MOOC CURATE
About this MOOC:
- This MOOC (Massive Open Online Course) explores the intersection of AI and entrepreneurship. Developed by a consortium of Higher Education Institution lecturers, AI and entrepreneurship experts, and industry leaders, the course blends academic insight with real-world business needs.
- The goal is simple: to equip you with the skills and knowledge to apply entrepreneurial tools and AI solutions to real business challenges.
- The target group is Students, Non-academic staff and Academic staff
- This MOOC is organized by Haaga Helia University of Applied Sciences, Technical University of Kosice, Université Côte d’Azur and University of Münster.
Learning outcomes:
Through active participation in this course, participants will be able to:
- Describe key concepts related to business models, value propositions, market analysis, sales, marketing, startup financing, and intellectual property rights.
- Explain how AI tools can support entrepreneurial decision-making, market research, and business development.
- Discuss the ethical, legal, and strategic implications of AI in entrepreneurship.
- Apply AI-driven tools to validate business models, create value propositions, analyze market trends, optimize sales and marketing, explore startup financing options, and evalute potential issues with Intellectual Property Rights.
- Assess business ideas and refine them based on feedback and iterative development.
- Evaluate the feasibility, scalability, and impact of AI-driven business innovations.
Course content:
Module 1. Value Proposition Workload: 8h
Module 2. Business Model Workload: 9h
Module 3. Market Research, Competition and Sizing Workload: 10h
Module 4. Sales, Customer Understanding and Pitching Workload: 11h
Module 5. Marketing: Positioning, Journey Design, and Metrics Workload: 10h
Module 6. Sources of Finance for Startups Workload: 9h
Module 7. Intellectual Property and Trademark Workload: 15h
Explores the intersection between AI and entrepreneurship
Funding

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 101035809.
Duration of the project: 1.11.2023-30.4.2026