Overview

The thesis will investigate how AI-based assistants can guide innovators and entrepreneurs through the early innovation process. Generative AI systems such as ChatGPT are increasingly used in ad-hoc ways, but their potential as structured, process-oriented innovation guides remains underexplored. This project aims to design and evaluate an AI-based innovation process guide that supports users across multiple early-stage activities, such as problem framing, idea development, customer understanding, and initial business modeling.

Background

Generative AI systems are rapidly becoming part of everyday knowledge work, including innovation and entrepreneurship. Large language models can generate ideas, explain concepts, and provide feedback, making them promising candidates for supporting early-stage innovation. In startup contexts, founders often face the challenge of navigating complex and unfamiliar processes, such as identifying customer problems, validating assumptions, or structuring business ideas, often without access to continuous expert guidance.

Existing innovation frameworks (e.g., design thinking, lean startup) provide structured approaches but are typically applied through workshops, mentors, or static templates. AI-based assistants could act as always-available, adaptive process guides, dynamically steering innovators through innovation tasks and prompting reflection at critical moments. However, current research provides limited insight into how such AI-driven guidance should be designed, how structured guidance compares to unstructured AI use, and how it affects creative outcomes and user experience.

Open questions remain regarding the effectiveness of AI-led process guidance, the balance between structure and creative freedom, and the role of AI in shaping how innovators understand and enact innovation processes.

Potential Research Objectives

The overarching goal of this master thesis is to investigate how AI-based assistants can guide innovators through early-stage innovation processes and how such guidance affects creative outcomes and user experience.

Depending on the student’s interests, the thesis can focus on one or more of the following research directions:

(1) AI as a Step-by-Step Innovation Process Guide
This direction examines AI assistants that guide users through predefined innovation stages (e.g., problem definition, ideation, validation). The goal is to compare structured AI guidance with unstructured AI use or human-only approaches.

(2) AI-Supported Lean Startup and Customer Discovery
This direction focuses on how AI-based assistants can support activities such as hypothesis formulation, customer persona development, or interview preparation. The study can analyze whether AI-guided users develop more coherent and testable innovation concepts.

(3) Effects of Structured AI Guidance on Creativity and Completeness
This direction investigates trade-offs between structure and creativity. While structured guidance may improve completeness and clarity, it may also constrain exploration. The goal is to identify when and how AI guidance enhances or limits creative outcomes.

(4) Design Principles for AI-Based Innovation Guidance Systems
This design-oriented direction aims to derive and evaluate design principles for AI-based innovation process guides. Possible design features include adaptive questioning, reflection prompts, phase transitions, or explanations of why certain steps matter.

Your Profile

• You are interested in the emerging field of generative AI

• You are interested in creativity, innovation, and startup-related topics

• You enjoy empirical research (experiments, surveys, data analysis)

• You have experience with Python or are willing to learn

• You are highly motivated to work in a self-organized and goal-oriented manner and bring in your own ideas

• Very good English skills, as the thesis will be written in English

We offer an exciting research topic with strong relevance to both academia and practice, close supervision, and the opportunity to develop theoretical, methodological, and practical skills. If you are interested, please send a current transcript of records, a short CV, and a brief motivation (2–3 sentences) to Jonas Liebschner (jonas.liebschner∂kit.edu).