AI Skill Report Card
Designing Applied Research Studies
Quick Start13 / 15
Given a research topic, produce a structured research design in this order:
- Problem statement + research gap (1 paragraph)
- Research question(s) and hypotheses
- Methodology (experimental design OR assessment framework)
- Variables, parameters, and measurement methods
- Expected outcomes and validation criteria
Example invocation: "Design a study on optimizing microalgae cultivation for biofuel" → produces the five sections above with domain-specific detail (species selection, light/CO2/nutrient parameters, lipid yield metrics, statistical design like RSM/DOE).
Recommendation▾
Add a brief 'bad output' contrast example to illustrate common failure modes (e.g., unweighted maturity model vs. weighted) for stronger examples section
Workflow14 / 15
Progress:
- Step 1: Classify the topic into domain type (lab-experimental vs. organizational-assessment)
- Step 2: Define the research gap — what's unknown or unoptimized
- Step 3: Formulate measurable research question(s)
- Step 4: Select methodology template matching domain type
- Step 5: Specify variables (independent/dependent/controlled) or framework dimensions
- Step 6: Define measurement/validation methods and success criteria
- Step 7: Identify risks, limitations, confounders
- Step 8: Draft expected contribution (scientific or practical)
Domain type A — Lab/experimental (chemical technology & biotechnology): Use for biosensors, materials, bioprocesses. Structure:
- Materials/system under study (e.g., aptamer sequence, target pathogen, nanoparticle type, algae strain)
- Independent variables (concentration, temperature, shear rate, light intensity, nutrient ratio)
- Dependent variables (sensitivity/LOD, viscosity/storage modulus, lipid content/biomass yield)
- Design of experiments: full/fractional factorial, Response Surface Methodology (RSM), Box-Behnken, or Central Composite Design for optimization problems
- Analytical methods: electrochemical/optical detection for biosensors; rheometry (steady shear, oscillatory) for suspensions; GC-MS/gravimetric for lipid content
- Statistical validation: ANOVA, R², regression models, reproducibility (n≥3 replicates)
Domain type B — Organizational/managerial (economics & technical management): Use for maturity models, factor analysis, implementation studies. Structure:
- Unit of analysis (enterprise, process, department)
- Maturity/assessment dimensions (e.g., digital: technology, process, culture, strategy, data; lean: leadership commitment, employee engagement, standardization, continuous improvement)
- Instrument: survey/questionnaire (Likert scale), structured interviews, document analysis, or composite index construction
- Analytical method: weighted scoring model, AHP/fuzzy AHP for weighting criteria, factor analysis/PLS-SEM for success factor studies, case study comparison
- Validation: expert panel review, pilot testing, Cronbach's alpha for reliability
- Benchmarking against existing frameworks (e.g., compare new maturity model to DMM, CMMI-style scales)
Recommendation▾
Consider trimming the two domain-type templates slightly — some overlap in structure could be unified into a shared checklist with domain-specific branches
Examples17 / 20
Example 1: Input: "Разработка биосенсора на основе аптамеров для обнаружения Salmonella в пищевых продуктах" Output:
- Gap: existing antibody-based biosensors have stability/cost issues; aptamers offer higher stability and lower cost but need optimized selection and immobilization
- Research question: Can an aptamer-based electrochemical biosensor achieve LOD < 10² CFU/mL for Salmonella in food matrices within 30 min?
- Method: SELEX-derived aptamer selection → immobilization on gold electrode via thiol linkage → electrochemical impedance spectroscopy (EIS) detection → test against target vs. non-target bacteria (specificity) → spiked food sample validation
- Variables: aptamer concentration, incubation time, electrode surface density (independent); impedance change, LOD, specificity ratio (dependent)
- Validation: compare against PCR/culture gold standard, calculate sensitivity/specificity, triplicate runs
Example 2: Input: "Оценка зрелости цифровой трансформации предприятий машиностроения" Output:
- Gap: generic digital maturity models don't account for sector-specific constraints (legacy equipment, capital intensity) in machine-building
- Research question: What digital maturity model dimensions and weights best differentiate transformation progress among machine-building enterprises?
- Method: literature review to extract candidate dimensions (strategy, technology infrastructure, data integration, workforce digital skills, process automation) → expert panel (Delphi, 2 rounds) to weight dimensions via AHP → survey instrument (5-point Likert, ~40 items) deployed to 30-50 enterprises → compute composite maturity index → cluster enterprises into maturity tiers
- Validation: Cronbach's alpha > 0.7 for each dimension, correlation of index with objective performance metrics (e.g., OEE, revenue growth)
Recommendation▾
Add a short section on how to handle topics that don't cleanly fit either domain type (hybrid or ambiguous cases)
Best Practices
- Always tie dependent variables to a measurable, industry-recognized metric (LOD, CFU/mL, Pa·s, % lipid w/w, maturity index 0-5).
- For optimization problems, default to RSM/DOE over one-factor-at-a-time — faster convergence, captures interactions.
- For organizational studies, triangulate survey data with at least one qualitative source (interview, document review) to strengthen validity.
- Specify sample size/replicate count explicitly — reviewers always ask.
- Benchmark against an existing standard or gold-standard method, not just internal comparison.
- State assumptions about matrix complexity (e.g., real food samples vs. buffer; real enterprises vs. simulated data) since this affects generalizability.
Common Pitfalls
- Don't propose single-factor experiments when interactions matter (e.g., temperature AND shear rate jointly affect suspension viscosity) — use factorial/RSM designs instead.
- Don't build a maturity/assessment framework without weighting criteria — unweighted averages hide which factors actually drive outcomes.
- Don't skip specificity/selectivity testing for biosensors — sensitivity alone is insufficient for publication or practical use.
- Don't rely solely on self-reported survey data for lean/digital transformation studies without a validation check (objective KPIs or triangulation).
- Don't ignore reproducibility reporting (error bars, confidence intervals) in rheology/bioprocess results — reviewers will reject without it.