Scoping Review Guide

Population:  Who is being studied?
Concept:     What is the key concept or phenomenon?
Context:     In what setting or discipline?

Example question:
"What is known about the use of AI tools in undergraduate
STEM education, including types of tools, pedagogical
approaches, and reported outcomes?"

Search strategy development:
1. Identify key terms from the PCC framework
2. Develop synonyms and related terms for each concept
3. Combine using Boolean operators

Example search string (PubMed):
("artificial intelligence" OR "machine learning" OR "deep learning"
 OR "natural language processing" OR "chatbot" OR "intelligent tutoring")
AND
("undergraduate" OR "higher education" OR "university student"
 OR "college student")
AND
("STEM" OR "science education" OR "engineering education"
 OR "mathematics education" OR "computer science education")

Databases to search:
- Discipline-specific databases (ERIC, PubMed, IEEE Xplore, etc.)
- Multidisciplinary databases (Scopus, Web of Science)
- Grey literature sources (ProQuest Dissertations, conference proceedings)
- Reference lists of included studies

| Criterion | Inclusion | Exclusion |
|-----------|-----------|-----------|
| Population | Undergraduate STEM students | K-12, graduate, non-STEM |
| Concept | AI-based educational tools | Non-AI technology (e.g., basic LMS) |
| Context | Formal educational settings | Informal learning, self-study apps |
| Study type | Empirical research (any design) | Editorials, opinion pieces |
| Language | English, Chinese | Other languages |
| Date | 2015-2025 | Before 2015 |

# Example: Data charting template as a structured dictionary
charting_template = {
    "study_id": "",           # Author, year
    "country": "",            # Country where study was conducted
    "study_design": "",       # RCT, quasi-experimental, case study, survey, etc.
    "sample_size": 0,
    "population": "",         # Student demographics
    "ai_tool_type": "",       # Chatbot, ITS, NLP-based, etc.
    "ai_tool_name": "",       # Specific tool name (e.g., ChatGPT, ALEKS)
    "subject_area": "",       # Physics, CS, Math, Biology, etc.
    "pedagogical_approach": "",  # Flipped classroom, adaptive learning, etc.
    "outcome_measures": [],   # Learning gains, engagement, satisfaction, etc.
    "key_findings": "",       # Brief summary of main results
    "limitations": ""         # Reported limitations
}

import pandas as pd
import matplotlib.pyplot as plt

# Example: Visualize publication trends
df = pd.read_csv("charted_data.csv")

# Publications by year
year_counts = df["year"].value_counts().sort_index()
fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(year_counts.index, year_counts.values, color="#0072B2")
ax.set_xlabel("Publication Year")
ax.set_ylabel("Number of Studies")
ax.set_title("Included Studies by Year")
plt.tight_layout()
plt.savefig("studies_by_year.pdf", dpi=300)

# Evidence map: cross-tabulation
evidence_map = pd.crosstab(df["ai_tool_type"], df["outcome_measures"])
print(evidence_map)