Overview

The thesis will investigate the role of AI-based assistants in supporting human creativity during the early phases of innovation and entrepreneurship. Generative AI tools like ChatGPT have shown promise in enhancing idea generation and creative writing, but their impact on the ideation process in a startup context is not fully understood. This project aims to gain insights into how AI assistants can effectively support creative problem-solving and ideation, and to identify best practices and potential pitfalls when using such tools in the innovation/startup domain.

Background

Recent advances have made it possible to distribute generative AI (Gen-AI) systems to a wide range of users. AI-based assistants such as ChatGPT, Claude, or Gemini are increasingly used in everyday life and professional contexts. As their capabilities grow rapidly, these systems are becoming embedded in knowledge work, decision-making, and creative processes. One particularly promising application is the support of creativity in early-stage innovation, where individuals and teams must generate, refine, and evaluate novel business ideas under high uncertainty.

Recent research suggests that human–AI teams can outperform humans alone in creative tasks such as idea generation and problem reframing, including the creation of new business ideas. At the same time, emerging evidence indicates that the way humans collaborate with AI matters greatly. Different interaction modes (e.g., AI as idea generator, critic, or facilitator) may lead to different creative outcomes, affect team dynamics, or influence how creative and confident users feel. Moreover, while AI assistance may increase idea quantity and speed, it may also introduce risks such as idea homogenization, reduced ownership, or diminished creative self-efficacy.

Despite growing interest, we still lack a systematic understanding of how AI-based assistants should be used and designed to effectively support creativity in early startup phases. In particular, open questions remain regarding optimal collaboration strategies, effects on team-based ideation, user experience and creative confidence, and the design of AI-based creativity support systems.

Potential Research Objectives

The overarching goal of this master thesis is to investigate how AI-based assistants (especially large language models such as ChatGPT) can support creativity in early-stage innovation and startup ideation. The thesis will empirically examine how different forms of AI support influence creative outcomes, collaboration processes, and user experience.

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

(1) Optimal Collaboration Strategies
This direction investigates how individuals should collaborate with AI-based assistants during ideation. Different strategies—such as AI as idea generator, AI as sounding board, or AI as structured facilitator—may lead to different creative outcomes. The goal is to identify which strategies work best for startup ideation and under which conditions (e.g., user expertise).

(2) Impact on Team Brainstorming
This direction focuses on team-based creativity. It examines how the integration of AI assistants into group brainstorming affects idea quality, diversity, and team processes. Possible conditions include no AI, shared AI access, or individual AI access per team member. The goal is to understand how AI changes group dynamics and whether it enhances or constrains collective creativity.

(3) User Experience and Creative Confidence
This direction centers on the human experience of co-creating with AI. While AI may improve creative output, it may also affect enjoyment, perceived ownership, and creative self-efficacy. The goal is to study how AI support influences these psychological factors and to identify interaction styles that foster both high-quality ideas and positive creative experiences.

(4) Design of AI-Based Creativity Support Systems
This direction takes a design-oriented perspective. It investigates how AI-based creativity support systems should be designed to effectively support early-stage innovation. Possible features include structured ideation workflows, reflection prompts, diversity-enhancing mechanisms, or evaluation support. The goal is to derive and empirically evaluate design principles for human-centered AI creativity tools.

Methodology

The thesis will primarily rely on empirical methods, ideally using lab-based or online experiments. Participants will work on startup-related ideation or creative problem-solving tasks under different AI support conditions. Creative outputs can be evaluated using established creativity metrics (e.g., novelty, usefulness, diversity) through human ratings and/or LLM-based assessments. In addition, surveys may be used to capture user experience, perceived creativity, confidence, and attitudes toward AI. Depending on the chosen direction, a lightweight prototype or interface variation may also be implemented and evaluated.

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)