Co-Creating Ideas: Brainstorming Research Topics with GenAI

Co-Creating Ideas: Brainstorming Research Topics with GenAI
Source: Freepik - Drazen Zigic

One of the most practical — and remarkably simple — applications of Generative AI (GenAI) in academic work is its ability to support early-stage thinking. Whether preparing a research paper, essay, or seminar discussion, getting started with a topic can often be the most challenging step. With a well-crafted prompt, GenAI tools can quickly generate a wide range of structured ideas that offer direction and inspiration.

To illustrate its use, the collapse of FTX was chosen as a test case — a topic that combines issues in finance, technology, regulation, and public trust.

Prompt

Academic Brainstorming

Topic: [INSERT TOPIC HERE]

You are asked to generate a set of basic academic ideas related to the topic above. These ideas should be suitable as starting points for a research paper, essay, or seminar discussion. Please approach the topic broadly and creatively, considering potential research directions, relevant questions, conceptual angles, or analytical perspectives.

Your response should include clearly formulated ideas, written in full sentences or brief bullet points. Aim for 5–10 distinct points.

The ideas can relate to causes, effects, comparisons, controversies, methodological approaches, or any other angle you find intellectually promising.

Do not develop full arguments—focus instead on outlining possible paths of inquiry that could be explored further.

To test the prompt in practice, we used Grok 3 (https://x.ai/grok), which generated seven distinct ideas.

Output

Academic Ideas: The Collapse of FTX

Here are 7 distinct academic ideas related to the collapse of FTX, designed as starting points for research papers, essays, or seminar discussions:

  • The role of regulatory gaps in enabling FTX’s collapse: How did the absence of robust oversight in the cryptocurrency sector contribute to the mismanagement of funds and eventual downfall of FTX, and what lessons can be drawn for future regulatory frameworks?
  • Psychological and behavioral factors in investor trust: What psychological mechanisms led investors to place significant trust in FTX and its founder Sam Bankman-Fried, despite red flags, and how do these factors compare to historical financial scandals?
  • Comparative analysis with traditional financial failures: How does the collapse of FTX parallel or diverge from the Enron or Lehman Brothers collapses in terms of corporate governance failures, market impact, and public perception?
  • The impact on cryptocurrency adoption: Did the FTX collapse significantly alter public and institutional confidence in cryptocurrencies, and could it paradoxically accelerate the push toward decentralized finance as a response to centralized exchange vulnerabilities?
  • Ethical dimensions of effective altruism in business: Given Sam Bankman-Fried’s publicized commitment to effective altruism, how might the FTX collapse prompt a reevaluation of the ethical responsibilities of business leaders who tie profit-making to philanthropy?
  • Technological vulnerabilities in centralized crypto exchanges: What specific weaknesses in FTX’s operational infrastructure (e.g., security protocols, transparency mechanisms) exposed it to collapse, and how might blockchain technology evolve to address these issues?
  • Socioeconomic ripple effects: How did the FTX collapse disproportionately affect retail investors versus institutional players, and what does this reveal about wealth distribution and risk exposure in the crypto economy?

These ideas offer a mix of causal, comparative, ethical, and socioeconomic angles, providing fertile ground for further academic exploration.

Recommendation

The output generated by Grok 3 shows that the model is capable of identifying diverse and meaningful entry points into a complex topic. The seven ideas touch on a wide range of dimensions, from investor psychology and ethical considerations to systemic vulnerabilities and market consequences. The questions are clearly phrased and intellectually engaging, offering a solid foundation for deeper academic inquiry. The model’s ability to highlight both high-level issues and more focused case-specific aspects suggests that such prompts can be powerful tools in early-stage research planning. Overall, the result reflects both the flexibility of the prompt and the effectiveness of GenAI in supporting structured academic thinking.

The authors used Grok 3 [xAI (2025) Grok 3 (accessed on 21 March 2025), Large language model (LLM), available at: https://x.ai/grok] to generate the output.