Overview
Supports academic research and paper writing. Provides data collection and analysis, literature review, research design, academic paper drafting, citation and reference management, peer review preparation, reflection of 2026 edtech and AI education trends, and Bloom's Taxonomy-based research methodology.
Trigger Keywords
research, paper, academic paper, data collection, data analysis, report, literature review, peer review, research design, research methodology, survey, statistical analysis
Workflow
Step 1: Confirm Research Topic
Research purpose:
- Clarify research question (RQ)
- Target readers and publication venue (academic journal, thesis, report)
- Contribution and novelty of the research
Research scope:
- Research subjects (population, sample)
- Research period
- Methodology used (quantitative, qualitative, mixed)
Step 2: Literature Search Plan
Major domestic academic databases:
| DB | URL | Features |
|---|---|---|
| RISS | riss.kr | Free search of domestic theses, academic journals, and monographs. Operated by the Korea Education and Research Information Service under the Ministry of Education |
| DBpia | dbpia.co.kr | Focused on domestic academic journal articles. Many recent papers in AI literacy and edtech |
| KISS | kiss.kstudy.com | Korean academic information DB. Strong in humanities and social sciences |
| NDSL | ndsl.kr | Korea Institute of Science and Technology Information. Focused on science and engineering |
International databases:
- Google Scholar (scholar.google.com)
- PubMed (pubmed.ncbi.nlm.nih.gov) — medicine and life sciences
- IEEE Xplore (ieeexplore.ieee.org) — engineering
- JSTOR (jstor.org) — humanities and social sciences
Search strategy:
- Extract core keywords
- Expand synonyms and related terms
- Use Boolean operators (AND, OR, NOT)
- Filter by publication year (last 5 years recommended)
Step 3: Data Analysis
Literature classification:
- Topic clustering
- Identify trends by period
- Classify by research methodology
- Derive arguments
Data analysis:
- Quantitative research: statistical analysis (descriptive statistics, t-test, ANOVA, regression analysis)
- Qualitative research: content analysis, thematic analysis, case study
- Mixed research: integration of quantitative and qualitative
2026 edtech and AI education trends (latest paper trends in DBpia and RISS):
- AI literacy research: increasing papers exploring AI awareness and competencies of language education majors
- Learner modeling: active research on designing personalized learning paths based on AI courseware
- AI ethics: Increasing discourse on the ethical use of big data-based edtech
- AI courseware: Digital textbooks + AI integration, automated grading, error clinic, and learning analytics combined
- Hyper-personalized learning: PwC Samil Accounting Corporation report — the core of edtech is 'one-on-one customized learning'
Step 4: Designing the paper structure
Standard structure of an academic paper:
-
Introduction
- Research background and purpose
- Research question (RQ)
- Necessity and novelty of the research
-
Literature Review
- Review of major prior research
- Identification of research gap
- Positioning of this study
-
Methodology
- Research design
- Data collection method
- Analysis method
-
Results
- Data analysis results
- Statistical significance
- Visualization (tables, graphs)
-
Discussion
- Interpretation of results
- Comparison with prior research
- Implications
-
Conclusion
- Summary of the research
- Limitations of the research
- Suggestions for follow-up research
References:
- Citation styles such as APA, MLA, Chicago, etc.
- Reference management tools (Zotero, EndNote)
Step 5: Drafting and review
Drafting:
- Drafting each section
- Checking the logical flow
- Checking citation format
Preparing for peer review:
- Validity of the research methodology
- Reliability of the results
- Depth of the discussion
- Clarity of contribution
Using Bloom's Taxonomy
Application of Bloom's Taxonomy of Educational Objectives in edtech and AI education design:
| Cognitive level | Description | AI usage method |
|---|---|---|
| Remember | Memorizing facts | Flashcards, automated quizzes |
| Understand | Explaining concepts | Chatbot Q&A, summary generation |
| Apply | Actual use | Simulations, hands-on assignments |
| Analyze | Identifying structure | Data analysis, comparison |
| Evaluate | Judgment and critique | AI feedback, peer review |
| Create | New output | Projects, portfolios |
→ Recommended structure: AI automates lower cognitive levels (Remember–Apply), while teachers focus on guiding higher levels (Analyze–Create)
Usage examples
Example 1: "Please write a literature review report on the effectiveness of AI education." → Topic analysis → Literature search → Clustering → Report writing
Example 2: "Analyze the survey data and derive insights." → Data loading → Descriptive statistics → t-test/ANOVA → Interpretation of results
Example 3: "Lay out the structure of a master's thesis research proposal." → Clarify research questions (RQ) → Prior research → Research design → Thesis structure
Example 4: "Organize the references in APA format." → Collect source information → Apply APA style → Reference list
Deliverables
- Literature review report
- Data analysis report
- Research proposal draft
- Academic paper draft
- Reference list (APA, MLA, Chicago)
Precautions
Educational material access:
- If you tell us the range of accessible materials, we will analyze as much as possible within that range
When learning objectives are unclear:
- Start with the process of specifying the research question (RQ) together
When there is no raw data:
- Switch to a meta-analysis of existing prior research or a literature review direction
When journal submission guidelines differ:
- If you tell us the target journal name, we will adjust to match its submission guidelines
Related skills
- moai-education/curriculum-designer: Curriculum design, defining learning objectives
- moai-education/assessment-creator: Creating exam/assessment questions
When not to use this skill
- Curriculum and lecture design → the moai-education:curriculum-designer skill is more appropriate
- Creating exam questions → use the moai-education:assessment-creator skill
- Market research/business research → the moai-business:market-analyst skill is more appropriate
- Actual data collection (sending surveys, conducting interviews) → use the relevant tool (Google Forms, Kakao Forms, etc.) directly