How to Use Technology to Refine Research Questions

76% of workers know they need AI skills to stay competitive. Yet most UX professionals are still creating personas manually, writing research questions from scratch, and spending hours on wireframe specs. Listen, if you're a UX professional in 2025: Stop: - Treating AI as a threat to your job - Writing every research question manually - Spending hours on repetitive documentation - Waiting for "the perfect time" to learn AI Instead: - Master prompt engineering frameworks (it takes 30 days) - Use the REFINE method for every AI interaction - Build a library of reusable prompt templates - Save 40% of your time on routine tasks Here's my battle-tested prompt framework for UX research: The REFINE Framework: - Role: "You are a senior UX researcher specializing in [industry]" - Expectations: Clearly state your desired outcome - Format: Specify exact output structure - Iterate: Plan to refine based on results - Nuance: Add project-specific context - Example: Provide concrete samples Real example that saved me 3 hours: "You are an experienced UX researcher conducting user interviews for a telehealth platform. Create 8-10 open-ended interview questions that will help uncover: - User motivations and pain points - Current workflows and processes - Feature preferences and expectations Format: Present as a bulleted list with follow-up probes for each main question. Context: Users are 55+ seniors new to digital health Target Users: Medicare patients managing chronic conditions" The AI generated better questions than my manual brainstorming - in 30 seconds. Why this matters to you: AI skills are demanded in 1 in 4 new tech jobs. UX professionals who master prompt engineering aren't just surviving - they're becoming the most valuable players on their teams. You already have the core skills: understanding user needs, clear communication, and iterative problem-solving. Prompt engineering just amplifies them. --- PS: What are your go-to prompts? 🤔 Follow me, John Balboa. I swear I'm friendly and I won't detach your components.

When I first heard about SciSpace’s new feature, Deep Review, I assumed it was just another AI tool among many. I casually gave it a try, but to my surprise, it turned out to be incredibly helpful. Here’s how: ✅ Intelligent Query Enhancement – Optimizes searches with better keywords and context. ➡ For example, if I search for "drought resistance genes," it automatically suggests more precise keywords like "drought-tolerance QTLs in maize." ✅ Precision-Based Questioning – Helps refine queries by prompting you to be more specific. ➡ For instance, when I searched for markers for anthracnose resistance in peppers, it asked if I needed species-specific or marker-type-specific results (e.g., SSR, SNP, QTLs). ✅ Query Transformation – Runs multiple refined searches to improve accuracy. ➡ Instead of a single broad search, it automatically breaks down queries into smaller, more effective searches, ensuring better coverage. ✅ Citations & References Traversal – Identifies hidden yet relevant research papers. ➡ If an important paper isn't directly in the top results, it traces citations and references to surface highly relevant but less obvious papers. ✅ Smart Filtering – Ensures only the most relevant papers are included, with the top 20 papers listed first. ➡ For example, if a query retrieves 200+ results, it prioritizes the most cited and contextually relevant studies instead of listing everything indiscriminately. ✅ Structured Insights – Categorizes papers for clarity. It also includes additional sections like methods studied or markers used or species studied for better organization. ➡ For instance, it groups studies based on experimental approaches, such as genome-wide association studies (GWAS) vs. linkage mapping. For me, this serves as a quick reference tool to identify markers linked to specific diseases. It not only saves time but also provides precise results. The curated list includes SSR, SNP, and QTL markers, which would otherwise take weeks to compile manually. Definitely a must-try for literature searches! 🎥 Check out my demo video here!👇🏻 Additionally, SciSpace is launching a new feature—SciSpace Browser Control, an AI-powered research assistant that helps you find and access papers beyond SciSpace. With Browser Control, you can search across multiple databases, including Google Scholar, PubMed, ScienceDirect, and more. This feature is coming soon! Useful links to access these features: Deep Review UTM link: https://lnkd.in/gUBWfjZG Browser control waitlist link: https://lnkd.in/ghfhf5qD Use these referral codes for discounted subscription: Other countries: DEEPDR40 — offers 40% off on advanced annual plan DEEPDR20 — offers 20% off on advanced monthly plan India: JAGDR20 — offers 20% off on both advanced monthly and annual plan #SciSpaceDeepReview #SciSpaceAIResearchAgent #SystematicLiteratureReview #AIinresearch

How to actually use ChatGPT as a Tool rather than a Co-author in the research process. 4️⃣ practical ways. ✔ Truth be told, AI tools, including ChatGPT, have cemented their place in the research workflow. ✔ There are practical ways researchers can enhance their work without compromising on quality or ethical standards. *************************** ✅ Refine Your Questions or Prompts to Be as Specific as Possible ✔ The quality of ChatGPT's responses heavily relies on the clarity and specificity of the input it receives ✅ Use ChatGPT's Outputs as a Starting Point, Not the Final Product ✔ View ChatGPT's contributions as preliminary drafts or initial insights rather than conclusive findings is important. ✅ Engage in an Iterative Process, Refining Your Queries Based on Previous Responses ✔ Interaction with ChatGPT should be viewed as an iterative dialogue rather than a one-off query. ✅ Combine ChatGPT's Insights with Expert Knowledge and Critical Analysis ✔ The most effective use of ChatGPT in research involves synthesizing its outputs with your own knowledge and analytical skills. ************************** ✔ The ultimate goal is to foster innovation and efficiency in research methodologies while upholding the principles of academic rigor and integrity. #academia #research #phd #graduateschool #mentoring