Academic Research Assistant: Automate Literature Reviews with Zapserp
Transform your research workflow by automating literature discovery, paper analysis, and bibliography generation. This guide shows you how to build an intelligent research assistant that helps academics and students conduct comprehensive literature reviews efficiently.
What We're Building
A smart academic research assistant that:
- Searches multiple academic databases and sources
- Extracts key findings and methodologies from papers
- Generates annotated bibliographies automatically
- Identifies research gaps and trending topics
- Creates structured literature reviews
Core Research Engine
const { Zapserp } = require('zapserp')
class AcademicResearchAssistant {
constructor(apiKey) {
this.zapserp = new Zapserp({ apiKey })
this.researchCache = new Map()
this.citationNetwork = new Map()
this.disciplineKeywords = {
'computer_science': ['algorithm', 'machine learning', 'data structure', 'software'],
'medicine': ['clinical trial', 'diagnosis', 'treatment', 'patient'],
'psychology': ['behavior', 'cognitive', 'therapy', 'study'],
'biology': ['gene', 'protein', 'cell', 'organism', 'molecular']
}
}
async conductLiteratureReview(topic, discipline = null, yearFilter = 5) {
console.log(`š Starting literature review for: ${topic}`)
const research = {
topic,
discipline,
timestamp: new Date().toISOString(),
papers: [],
keyFindings: [],
researchGaps: [],
bibliography: [],
summary: {}
}
// Generate search queries for academic content
const queries = this.generateAcademicQueries(topic, discipline)
for (const query of queries) {
try {
const papers = await this.searchAcademicContent(query, yearFilter)
research.papers.push(...papers)
// Brief delay between searches
await this.delay(1000)
} catch (error) {
console.error(`Search failed for query: ${query}`, error)
}
}
// Remove duplicates and analyze papers
research.papers = this.deduplicatePapers(research.papers)
research.keyFindings = await this.extractKeyFindings(research.papers)
research.researchGaps = this.identifyResearchGaps(research.papers, research.keyFindings)
research.bibliography = this.generateBibliography(research.papers)
research.summary = this.generateLiteratureSummary(research)
console.log(`ā
Found ${research.papers.length} relevant papers`)
return research
}
generateAcademicQueries(topic, discipline) {
const baseQueries = [
`"${topic}" research paper`,
`"${topic}" academic study`,
`"${topic}" literature review`,
`"${topic}" systematic review`,
`"${topic}" meta-analysis`
]
// Add discipline-specific queries
if (discipline && this.disciplineKeywords[discipline]) {
const keywords = this.disciplineKeywords[discipline]
keywords.forEach(keyword => {
baseQueries.push(`"${topic}" ${keyword} research`)
})
}
// Add temporal queries for recent research
const currentYear = new Date().getFullYear()
baseQueries.push(`"${topic}" research ${currentYear}`)
baseQueries.push(`"${topic}" recent studies ${currentYear - 1}`)
return baseQueries.slice(0, 8) // Limit to avoid overwhelming results
}
async searchAcademicContent(query, yearFilter) {
const searchResults = await this.zapserp.search({
query: `${query} site:scholar.google.com OR site:pubmed.ncbi.nlm.nih.gov OR site:arxiv.org OR site:jstor.org OR site:researchgate.net`,
engines: ['google', 'bing'],
limit: 15,
language: 'en'
})
// Filter for academic sources
const academicResults = this.filterAcademicSources(searchResults.results)
if (academicResults.length === 0) return []
// Extract content from papers
const urls = academicResults.slice(0, 8).map(result => result.url)
const contentResults = await this.zapserp.readerBatch({ urls })
const papers = []
contentResults.results.forEach((content, index) => {
if (content && content.content) {
const paperData = this.extractPaperData(content, academicResults[index])
// Apply year filter
if (yearFilter && paperData.year) {
const currentYear = new Date().getFullYear()
if (currentYear - paperData.year <= yearFilter) {
papers.push(paperData)
}
} else {
papers.push(paperData)
}
}
})
return papers
}
filterAcademicSources(results) {
const academicDomains = [
'scholar.google.com', 'pubmed.ncbi.nlm.nih.gov', 'arxiv.org',
'jstor.org', 'researchgate.net', 'sciencedirect.com',
'springer.com', 'wiley.com', 'nature.com', 'science.org',
'ieee.org', 'acm.org', 'plos.org', 'biorxiv.org'
]
const academicIndicators = [
'abstract', 'doi:', 'journal', 'volume', 'issue',
'citation', 'references', 'methodology', 'conclusion'
]
return results.filter(result => {
// Check for academic domains
if (academicDomains.some(domain => result.url.includes(domain))) {
return true
}
// Check for academic content indicators
const snippet = result.snippet.toLowerCase()
return academicIndicators.some(indicator => snippet.includes(indicator))
})
}
extractPaperData(content, searchResult) {
const text = content.content
const title = content.title || searchResult.title
return {
title: this.cleanTitle(title),
authors: this.extractAuthors(text),
year: this.extractYear(text),
journal: this.extractJournal(text),
abstract: this.extractAbstract(text),
methodology: this.extractMethodology(text),
keyFindings: this.extractPaperFindings(text),
citations: this.extractCitations(text),
doi: this.extractDOI(text),
url: content.url,
source: this.extractSource(content.url),
relevanceScore: this.calculateRelevance(text, title),
wordCount: text.split(/\s+/).length
}
}
cleanTitle(title) {
// Remove common prefixes and clean up title
return title
.replace(/^\[PDF\]|\[HTML\]/, '')
.replace(/^.*?-\s*/, '')
.trim()
}
extractAuthors(text) {
// Multiple patterns for author extraction
const authorPatterns = [
/Authors?[:\s]+(.*?)(?:\n|Abstract|Introduction)/i,
/By[:\s]+(.*?)(?:\n|Abstract|Introduction)/i,
/([A-Z][a-z]+\s+[A-Z][a-z]+(?:,\s*[A-Z][a-z]+\s+[A-Z][a-z]+)*)/
]
for (const pattern of authorPatterns) {
const match = text.match(pattern)
if (match) {
return match[1]
.split(/,|and|\n/)
.map(author => author.trim())
.filter(author => author.length > 0)
.slice(0, 5) // Limit to first 5 authors
}
}
return []
}
extractYear(text) {
const yearPattern = /\b(19|20)\d{2}\b/g
const years = text.match(yearPattern)
if (years) {
// Return the most recent year that's not in the future
const currentYear = new Date().getFullYear()
const validYears = years
.map(year => parseInt(year))
.filter(year => year >= 1990 && year <= currentYear)
.sort((a, b) => b - a)
return validYears[0] || null
}
return null
}
extractJournal(text) {
const journalPatterns = [
/Published in[:\s]+(.*?)(?:\n|,|\.)/i,
/Journal[:\s]+(.*?)(?:\n|,|\.)/i,
/(?:In|From)[:\s]+(.*?)(?:Volume|Vol|Issue|\n)/i
]
for (const pattern of journalPatterns) {
const match = text.match(pattern)
if (match) {
return match[1].trim()
}
}
return null
}
extractAbstract(text) {
const abstractPattern = /Abstract[:\s]+(.*?)(?:\n\n|Introduction|Keywords|1\.|Background)/is
const match = text.match(abstractPattern)
return match ? match[1].trim().substring(0, 500) :
text.substring(0, 300) + '...' // Fallback to first 300 chars
}
extractMethodology(text) {
const methodPatterns = [
/Method(?:ology|s)?[:\s]+(.*?)(?:\n\n|Results|Discussion|Conclusion)/is,
/Approach[:\s]+(.*?)(?:\n\n|Results|Discussion|Conclusion)/is,
/Procedure[:\s]+(.*?)(?:\n\n|Results|Discussion|Conclusion)/is
]
for (const pattern of methodPatterns) {
const match = text.match(pattern)
if (match) {
return match[1].trim().substring(0, 300)
}
}
return null
}
extractPaperFindings(text) {
const findings = []
// Look for results and conclusions
const resultPatterns = [
/Results?[:\s]+(.*?)(?:\n\n|Discussion|Conclusion)/is,
/Findings?[:\s]+(.*?)(?:\n\n|Discussion|Conclusion)/is,
/Conclusion[:\s]+(.*?)(?:\n\n|References|Acknowledgments)/is
]
resultPatterns.forEach(pattern => {
const match = text.match(pattern)
if (match) {
const finding = match[1].trim()
if (finding.length > 50) {
findings.push(finding.substring(0, 200))
}
}
})
return findings
}
extractCitations(text) {
// Count reference patterns
const citationPatterns = [
/\[\d+\]/g,
/\(\d{4}\)/g,
/et al\./g
]
let citationCount = 0
citationPatterns.forEach(pattern => {
const matches = text.match(pattern)
if (matches) citationCount += matches.length
})
return citationCount
}
extractDOI(text) {
const doiPattern = /DOI[:\s]*(10\.\d+\/[^\s]+)/i
const match = text.match(doiPattern)
return match ? match[1] : null
}
extractSource(url) {
try {
return new URL(url).hostname.replace('www.', '')
} catch {
return 'Unknown Source'
}
}
calculateRelevance(text, title) {
// Simple relevance scoring based on academic indicators
let score = 0
const academicTerms = [
'research', 'study', 'analysis', 'methodology', 'data',
'results', 'conclusion', 'findings', 'experiment', 'survey'
]
const lowerText = text.toLowerCase()
const lowerTitle = title.toLowerCase()
academicTerms.forEach(term => {
if (lowerTitle.includes(term)) score += 0.2
if (lowerText.includes(term)) score += 0.1
})
// Boost score for papers with clear structure
if (lowerText.includes('abstract')) score += 0.2
if (lowerText.includes('methodology')) score += 0.2
if (lowerText.includes('references')) score += 0.1
return Math.min(score, 1)
}
deduplicatePapers(papers) {
const seen = new Set()
return papers.filter(paper => {
const key = paper.title.toLowerCase().substring(0, 50)
if (seen.has(key)) return false
seen.add(key)
return true
})
}
async extractKeyFindings(papers) {
const allFindings = []
papers.forEach(paper => {
if (paper.keyFindings && paper.keyFindings.length > 0) {
paper.keyFindings.forEach(finding => {
allFindings.push({
finding,
paper: paper.title,
authors: paper.authors,
year: paper.year,
relevance: paper.relevanceScore
})
})
}
})
// Sort by relevance and return top findings
return allFindings
.sort((a, b) => b.relevance - a.relevance)
.slice(0, 10)
}
identifyResearchGaps(papers, keyFindings) {
const gaps = []
// Analyze patterns in research
const methodologies = papers.map(p => p.methodology).filter(Boolean)
const years = papers.map(p => p.year).filter(Boolean)
// Identify temporal gaps
if (years.length > 0) {
const latestYear = Math.max(...years)
const oldestYear = Math.min(...years)
if (latestYear - oldestYear > 3) {
gaps.push({
type: 'temporal',
description: `Research spans ${oldestYear}-${latestYear}. Recent work may be needed.`
})
}
}
// Identify methodology gaps
const uniqueMethods = [...new Set(methodologies)]
if (uniqueMethods.length < 3 && papers.length > 5) {
gaps.push({
type: 'methodological',
description: 'Limited methodological diversity detected. Alternative approaches may be underexplored.'
})
}
return gaps
}
generateBibliography(papers) {
return papers
.filter(paper => paper.title && paper.year)
.sort((a, b) => (a.authors[0] || '').localeCompare(b.authors[0] || ''))
.map(paper => {
const authors = paper.authors.length > 0 ? paper.authors.join(', ') : 'Unknown Author'
const journal = paper.journal ? ` ${paper.journal}.` : ''
const doi = paper.doi ? ` DOI: ${paper.doi}` : ''
return `${authors} (${paper.year}). ${paper.title}.${journal}${doi}`
})
}
generateLiteratureSummary(research) {
const totalPapers = research.papers.length
const avgYear = research.papers
.filter(p => p.year)
.reduce((sum, p) => sum + p.year, 0) / research.papers.filter(p => p.year).length
const topSources = {}
research.papers.forEach(paper => {
topSources[paper.source] = (topSources[paper.source] || 0) + 1
})
const sortedSources = Object.entries(topSources)
.sort(([,a], [,b]) => b - a)
.slice(0, 3)
return {
totalPapers,
averageYear: Math.round(avgYear),
topSources: sortedSources,
keywordDensity: research.keyFindings.length / totalPapers,
researchGapCount: research.researchGaps.length
}
}
delay(ms) {
return new Promise(resolve => setTimeout(resolve, ms))
}
}
module.exports = AcademicResearchAssistant
Usage Examples
Basic Literature Review
const researcher = new AcademicResearchAssistant('your-zapserp-api-key')
// Conduct a comprehensive literature review
const review = await researcher.conductLiteratureReview(
'machine learning in healthcare',
'computer_science',
3 // Last 3 years only
)
console.log(`š Literature Review Summary:`)
console.log(`Found ${review.papers.length} relevant papers`)
console.log(`Average publication year: ${review.summary.averageYear}`)
console.log(`Key findings: ${review.keyFindings.length}`)
// Display top findings
review.keyFindings.slice(0, 5).forEach((finding, index) => {
console.log(`\n${index + 1}. ${finding.finding}`)
console.log(` Source: ${finding.paper} (${finding.year})`)
})
Focused Research Query
// Target specific research questions
const topics = [
'deep learning interpretability',
'AI bias detection methods',
'federated learning privacy'
]
for (const topic of topics) {
const review = await researcher.conductLiteratureReview(topic, 'computer_science', 2)
console.log(`\nš ${topic.toUpperCase()}`)
console.log(`Papers found: ${review.papers.length}`)
console.log(`Research gaps: ${review.researchGaps.length}`)
if (review.researchGaps.length > 0) {
review.researchGaps.forEach(gap => {
console.log(` Gap: ${gap.description}`)
})
}
}
Generate Research Bibliography
const review = await researcher.conductLiteratureReview('quantum computing algorithms')
// Export bibliography for academic writing
console.log('\nš BIBLIOGRAPHY\n')
review.bibliography.forEach((citation, index) => {
console.log(`${index + 1}. ${citation}`)
})
// Export as file
const fs = require('fs')
const bibliographyText = review.bibliography
.map((citation, index) => `${index + 1}. ${citation}`)
.join('\n\n')
fs.writeFileSync('bibliography.txt', bibliographyText)
console.log('ā
Bibliography exported to bibliography.txt')
Advanced Features
Research Trend Analysis
// Analyze research trends over time
const trendTopics = ['AI ethics', 'machine learning', 'data privacy']
for (const topic of trendTopics) {
const review = await researcher.conductLiteratureReview(topic, null, 5)
// Group papers by year
const yearCounts = {}
review.papers.forEach(paper => {
if (paper.year) {
yearCounts[paper.year] = (yearCounts[paper.year] || 0) + 1
}
})
console.log(`\nš ${topic} Research Trend:`)
Object.entries(yearCounts)
.sort(([a], [b]) => parseInt(a) - parseInt(b))
.forEach(([year, count]) => {
console.log(` ${year}: ${'ā'.repeat(count)} (${count} papers)`)
})
}
Cross-Disciplinary Research
// Compare research across disciplines
const researchQuestion = 'artificial intelligence applications'
const disciplines = ['computer_science', 'medicine', 'psychology']
const crossDisciplinaryResults = {}
for (const discipline of disciplines) {
const review = await researcher.conductLiteratureReview(
researchQuestion,
discipline,
3
)
crossDisciplinaryResults[discipline] = {
paperCount: review.papers.length,
topSources: review.summary.topSources,
keyFindings: review.keyFindings.slice(0, 3)
}
}
console.log('š¬ Cross-Disciplinary Analysis:')
Object.entries(crossDisciplinaryResults).forEach(([discipline, data]) => {
console.log(`\n${discipline.toUpperCase()}:`)
console.log(` Papers: ${data.paperCount}`)
console.log(` Top source: ${data.topSources[0]?.[0] || 'N/A'}`)
console.log(` Key findings: ${data.keyFindings.length}`)
})
Best Practices
- Focused Queries: Start with specific research questions rather than broad topics
- Year Filtering: Use recent papers (3-5 years) for current research states
- Source Diversity: Include multiple academic databases for comprehensive coverage
- Regular Updates: Re-run searches periodically to catch new publications
- Manual Validation: Always review AI-extracted findings for accuracy
Conclusion
You now have a powerful academic research assistant that can significantly speed up literature reviews and research discovery. This tool helps researchers stay current with their fields and identify new research opportunities.
Ready to enhance your research? Consider adding features like citation network analysis, automated hypothesis generation, or integration with reference management tools like Zotero or Mendeley.