Mastering Research in the GenAI Age

In today’s digital era, GenAI is transforming the way we approach research, learning, and teaching. To truly harness its power, it’s essential to stay in control and accountable for your work.

Generative AI’s real strength lies in its ability to engage in dynamic, real-time discussions that evolve with your input. The quality of what you get out depends heavily on two things: well-crafted prompts and detailed feedback during the conversations.

This blog post is brought to you by members from the CEI’s “Fostering Digital Competency in the GenAI-GenZ Era: A Student-Driven Community of Practice”. Our team explores how students and researchers can use GenAI responsibly and effectively in academic contexts.

Through our work, we aim to identify the most effective ways to integrate AI into the research process to maximize its potential. We noticed that while many students rely heavily on AI tools, they often lack awareness of how to use them effectively, which limits the potential benefits these tools can provide. Our goal is to bridge this knowledge gap and ensure students can use AI more wisely and efficiently in their academic pursuits.

Crafting the right prompt is essential for productive collaboration with AI. Here are four principles to guide your interactions and ensure high-quality, academically sound outputs.

4 Key Principles for Writing Effective Prompts

Precision Targeting

Be specific. Tailored prompts generate tailored responses.

Clear Context

Provide background. Context helps AI align with your goals and audience.

Iterative Refinement

Don’t stop at your first result. Build on it with follow-up prompts.

Ethical Guardrails

Stay within academic boundaries. Use AI to assist, not replace, your thinking.

How GenAI can aid the literature review process?

We outline how AI tools streamline the entire literature review process, from keyword generation to information extraction, saving researchers significant time while producing comprehensive results. When AI handles tedious tasks like searching and summarizing, researchers can focus on critical analysis and meaningful connections, turning an exhausting process into an efficient and effective workflow.

Step 1: Generate keywords using Perplexity

Use AI to extract relevant terminology and synonyms from academic literature.

Perplexity Output Example

Step 2: Conduct primary search using Web of Science

Use the AI-generated keyword strings to search academic databases.

Web of Science Output Example

Step 3: Build literature database (using Zotero)

Organize references using Zotero. Install the Zotero Connector to save sources directly from your browser.

Zotero Output Example

Step 4: Summarize Papers (using Qwen-Long API Python Program)

Use the Qwen-Long Python script to extract key insights from each paper. You can find the code for our program on GitHub.

We decided to use the Qwen-Long API because it’s a budget-friendly option that can handle various types of data (like text and images) and large amounts of information at once. Of course, if you prefer, you can easily swap in other large language model APIs instead.

Qwen API Output Example

Why Our Approach Works

• Scalability: Processes large document batches in parallel, ideal for literature reviews.
• Flexibility: Modular design allows easy integration with other LLM APIs (not limited to Qwen) and custom prompt templates (user_prompt_path configuration).
• Independent R&D: Enables long-term maintainability and rapid iteration.
• Customizability: Adjustable workflows and output formats to fit diverse research needs.

Acknowledgements

We sincerely appreciate CEI’s “Fostering Digital Competency in the GenAI-GenZ Era: A Student-Driven Community of Practice,” funded by the Fund for Innovative Technology-in-Education (FITE). The Community of Practice (CoP/CEI) platform is essential for student innovation, providing guidance and resources to students with research ideas while facilitating collaboration among those with shared interests to achieve better outcomes together. For more information, please contact ceicop@ust.hk.

– By Mingyu Li, Yu Guo, Xinjie Huang (Department of Management)

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