Enhancing the "Guide to Produce Scoping Literature Reviews Using AI Tools"

A Critical Analysis and Improvement Framework

Executive Summary

This report presents a comprehensive analysis of the "Guide to Produce Scoping Literature Reviews Using AI Tools" (dated March 2025) with the aim of identifying areas for improvement to enhance clarity, completeness, practical usability, and methodological rigor. The evaluation reveals that while the guide provides a strong foundation for conducting AI-assisted scoping reviews, several opportunities for enhancement exist. Key recommendations include: strengthening the grey literature strategy with more targeted search techniques; enhancing AI tool integration through more specific tool recommendations and usage examples; improving practical guidance with annotated examples and decision-support frameworks; addressing inconsistencies in the level of detail across sections; and adding visual workflows to improve usability. The report concludes that implementing these recommendations would significantly enhance the guide's utility for both novice and experienced researchers in Technology Innovation Management (TIM) and beyond.

1. Introduction

1.1 Context and Purpose

Scoping reviews have become increasingly important in rapidly evolving fields such as Technology Innovation Management (TIM), where researchers must map broad topics, identify research gaps, and analyze emerging trends across heterogeneous literature. The integration of artificial intelligence tools into the scoping review process presents opportunities to enhance efficiency, manage larger volumes of literature, and produce more comprehensive syntheses. However, this integration also introduces new methodological challenges, ethical considerations, and potential biases that must be carefully managed.

The "Guide to Produce Scoping Literature Reviews Using AI Tools" (March 2025) represents a significant step forward in providing structured guidance for researchers navigating this complex landscape. This report aims to critically analyze this guide and offer recommendations for enhancement based on current best practices in research methodology, AI-human collaboration, and instructional design principles.

1.2 Overview of the Existing Guide

The guide is organized into three main parts:

Foundation of Scoping Reviews: Covering the basics of scoping reviews, their distinctions from systematic reviews, key frameworks, benefits and limitations of AI use, AI biases, human oversight, and grey literature searches.
Method to Produce Scoping Reviews: Presenting a nine-step process integrating AI at each stage, from formulating research questions to disseminating findings, with sections on AI's role, human oversight, and practical guidance.
Updating Scoping Review Guide: Discussing the guide's strengths, version control procedures, AI assistance disclosure, and mechanisms for continued improvement.

The guide successfully integrates established scoping review frameworks (Arksey & O'Malley, 2005; Levac et al., 2010; JBI; PRISMA ScR) with emerging AI applications, addressing both the opportunities and challenges presented by these technologies.

1.3 Methodology for Analysis

This analysis employs a systematic approach to evaluate the guide:

Content analysis: Critical examination of the guide's components, structure, and comprehensiveness.
Gap analysis: Identification of missing or underdeveloped elements.
Best practice comparison: Evaluation against established methodological standards and instructional design principles.
Usability assessment: Analysis of the guide's clarity, accessibility, and practical applicability.

The evaluation considers both methodological rigor and practical utility, acknowledging that the guide must serve a diverse audience with varying levels of expertise in both scoping reviews and AI tools.

2. Strengths of the Current Guide

2.1 Comprehensive Framework Integration

The guide successfully synthesizes multiple scoping review frameworks, providing users with a strong methodological foundation. The inclusion of five frameworks—Arksey and O'Malley (2005), Levac et al. (2010), JBI, PRISMA ScR, and recent AI-focused updates—offers users flexibility to select approaches best suited to their research needs. The guide effectively contextualizes these frameworks within TIM applications, enhancing their relevance.

2.2 AI Integration Throughout the Review Process

A significant strength of the guide is its systematic integration of AI across all nine steps of the scoping review process. For each step, the guide outlines AI's potential role, necessary human oversight, and practical guidance, creating a balanced approach that leverages AI capabilities while maintaining research integrity. This structure helps users understand where and how AI can enhance their review process without compromising methodological rigor.

2.3 Strong Ethical Framework and Human Oversight

The guide demonstrates a commendable emphasis on research ethics and human oversight. Sections dedicated to AI biases (1.6), human oversight (1.7), and ethical considerations (2.8) provide crucial guidance on maintaining research integrity while leveraging AI tools. The AI Assistance and Human Oversight Disclosure Statement in Part 3 offers a valuable template for transparency in reporting AI use.

2.4 Practical Output Orientation

The guide excels in connecting scoping reviews to practical applications within TIM, demonstrating how reviews can generate actionable outputs such as venture pitches, product development opportunities, market analyses, and competitive intelligence reports. This application-oriented approach enhances the guide's relevance for both academic and industry users.

3. Areas for Improvement

3.1 Grey Literature Strategy Enhancement

While section 1.8 provides a step-by-step approach to grey literature searches, several aspects could be strengthened:

Search technique specificity: The current guidance on keyword selection and Boolean operators remains general. More domain-specific examples and advanced search techniques would enhance utility.
Source evaluation criteria: The guide mentions authority, credibility, relevance, and timeliness as criteria for evaluating grey literature, but lacks detailed frameworks for systematic quality assessment of non-peer-reviewed sources.
Grey literature typology: A more comprehensive categorization of grey literature types relevant to TIM (e.g., patents, technical standards, startup pitch decks, regulatory guidance) would help users identify appropriate sources.

3.2 Inconsistent Depth Across AI Tool Integration

The guide references various AI tools including ChatGPT, Consensus, Perplexity, Elicit, OpenRead, and Rayyan, but the level of detail regarding specific tools varies significantly across sections:

Tool-task alignment specificity: Some sections provide clear recommendations for specific tools (e.g., Rayyan for screening, Elicit for data extraction), while others remain general. More consistent, detailed guidance on tool selection would benefit users.
Implementation examples: The guide would benefit from more screenshots, sample prompts, and workflow examples showing how to effectively use recommended AI tools for specific tasks.
Tool evaluation criteria: Limited guidance is provided on how to select appropriate AI tools based on features, limitations, and specific research needs.

3.3 Limited Practical Examples and Case Studies

While the guide includes numerous conceptual examples, it lacks comprehensive, end-to-end case studies demonstrating the full process:

Worked examples: Step-by-step examples showing how a specific research question evolves through all nine stages would provide valuable context.
Domain diversity: More examples spanning diverse TIM domains (e.g., cybersecurity, green technology, digital manufacturing) would help users apply the framework to their specific research areas.
Common challenges: Examples illustrating common pitfalls and their solutions would enhance practical utility.

3.4 Visual Aids and Decision-Support Tools

The guide contains relatively few visual aids to support comprehension and decision-making:

Process workflows: Visual representations of the nine-step process and sub-processes would enhance understanding of relationships between steps.
Decision trees: Tools to help users make methodological choices (e.g., framework selection, AI tool selection) would improve usability.
Comparative tables: More structured comparisons of AI tools, frameworks, and methodological approaches would facilitate informed decision-making.

4. Detailed Recommendations for Enhancement

4.1 Enhancing Grey Literature Strategy

4.1.1 Expanded Grey Literature Typology

Develop a comprehensive typology of grey literature sources relevant to TIM:

Grey Literature Type	Description	Example Sources	Search Strategies	Quality Indicators
Technical/Industry Reports	Documents published by companies or industry associations	Gartner, Forrester, McKinsey, IDC	Direct website searches, Subscription databases	Methodology transparency, data sources cited, author credentials
Patents & IP Documents	Legal documents describing inventions	USPTO, EPO, WIPO	Patent databases with CPC/IPC codes	Citation rates, legal status, company background
Policy & Regulatory Documents	Government publications on technology policy	EU Commission, NIST, FCC	Government websites, regulatory database	Publishing authority, date currency, public consultation evidence
Standards & Protocols	Technical specifications and standards	IEEE, ISO, W3C	Standards bodies' repositories	Industry adoption rates, development process transparency
Startup Materials	Pitches, business plans, funding documents	Crunchbase, PitchBook, AngelList	Investor databases, accelerator websites	Funding secured, team credentials, technological feasibility
Social Media & Tech Blogs	Informal expert discussions	Twitter, LinkedIn, Medium, Substack	Social listening tools, expert list monitoring	Author expertise, engagement metrics, citation of primary sources

4.1.2 Advanced Search Techniques for Grey Literature

Add a section on advanced search techniques specifically for grey literature:

Example: Advanced Patent Searching for AI Implementation in Healthcare

Database: USPTO

Basic Search: ("artificial intelligence" OR "machine learning") AND "healthcare"

Advanced Search:

CPC Classification: G16H50/20 (ICT for pattern recognition in healthcare data)
AND
Keywords: (diagnos* OR prognos* OR predict*)
AND
Date Range: After 01/01/2020
NOT
Claims: "blockchain" OR "distributed ledger"

4.1.3 Quality Assessment Framework for Grey Literature

Develop a structured framework for evaluating grey literature quality:

Authority Assessment (Score 1-5)

Author/organizational expertise in the field
Transparency about affiliations and potential conflicts
Citation and use by recognized experts

Methodological Clarity (Score 1-5)

Clear description of data collection methods
Transparency about limitations
Adequate sample size or data sources

Evidence Base (Score 1-5)

References to peer-reviewed research
Primary data vs. secondary interpretations
Verification possibilities

Relevance to Review Question (Score 1-5)

Direct vs. tangential relevance
Appropriate context (industry, timeframe)
Applicability to review population/concept

Currency and Timeliness (Score 1-5)

Publication date relevance
Reflects current technological capabilities
Accounts for recent regulatory changes

Decision Rule: Consider including grey literature scoring ≥ 15/25, with no individual category scoring < 3.

4.2 Improving AI Tool Integration

4.2.1 Comprehensive AI Tool Evaluation Matrix

Develop a detailed comparison of AI tools for different scoping review stages:

Tool	Best For	Key Features	Limitations	Pricing Model	Integration Capabilities	Example Use Case
ChatGPT 4o	Question formulation, Search term expansion, Initial text drafting	Contextual understanding, Query refinement, Text generation	Hallucinations, Knowledge cutoff, No direct academic database access	Subscription ($20/month)	API integration, Plugins	Generating variations of review questions with enhanced specificity
Elicit	Literature search, Data extraction, Citation analysis	Direct access to academic literature, Tabular data extraction, Research question formulation	Limited coverage of some journals, Occasional metadata errors	Freemium (Advanced features paid)	Zotero export, CSV export	Extracting study characteristics from 50 papers into a consistent table format
Perplexity	Exploratory searches, Real-time information, Citation generation	Live web access, Citation verification, Search across multiple sources	Variable source quality, Limited customization of search parameters	Freemium ($20/month Pro)	Limited API access	Quickly exploring emerging technology trends with verified sources
Rayyan	Study screening, Inclusion/exclusion application, Collaboration	Blinded review options, AI-assisted inclusion suggestions, Multi-reviewer support	Learning curve, Limited to screening phase	Free for academics	Reference manager import/export	Two researchers independently screening 500 abstracts with conflict resolution

4.2.2 Stage-Specific AI Prompt Templates

Add templates for effective AI tool prompting at each review stage:

Example: Prompt Template for Literature Screening with ChatGPT

I'm conducting a scoping review on [TOPIC] with the following inclusion criteria:

1. Population: [SPECIFY]
2. Concept: [SPECIFY]
3. Context: [SPECIFY]
4. Publication timeframe: [SPECIFY]

I need help evaluating if the following abstracts meet these criteria. For each abstract:

1. Analyze how it aligns with each inclusion criterion (provide specific evidence)
2. Highlight any exclusion factors
3. Recommend inclusion or exclusion with confidence level (high/medium/low)
4. Note any areas requiring human judgment

Here are the abstracts:
[PASTE ABSTRACTS]

4.2.3 AI-Human Workflow Diagrams

Develop visual representations of optimal workflows combining AI and human input:

Example: AI-Human Workflow for Data Extraction

1. PREPARATION (Human)

Define extraction categories
Develop extraction template
Test on sample papers

2. INITIAL EXTRACTION (AI)

Process full texts
Generate structured data
Flag uncertain extractions

3. VERIFICATION (Human)

Review AI extractions
Correct errors
Resolve flagged items

4. SYNTHESIS (AI+Human)

AI identifies patterns
Human interprets significance
Collaborative refinement

4.3 Enhancing Practical Examples and Case Studies

4.3.1 End-to-End Case Study

Develop a comprehensive case study demonstrating all nine steps:

Case Study: Mapping the Landscape of Quantum Computing Commercialization Strategies

For each of the nine steps, provide:

Example research actions
Sample outputs
AI tools used
Challenges encountered and solutions
Time investment
Key decision points

4.3.2 Annotated Examples of AI-Generated Content

Include examples of AI outputs with annotations highlighting strengths and weaknesses:

Example: AI-Generated Literature Summary with Critical Annotations

Original Paper Excerpt:

[Paper excerpt would appear here]

AI-Generated Summary:

[AI summary would appear here]

Annotations:

Accurately captured central methodology
Correctly identified sample size and population
Oversimplified statistical findings (human should verify p-values)
Misinterpreted author's conclusions about limitations
Failed to note conflict of interest disclosure

4.3.3 Troubleshooting Guide

Develop a section addressing common challenges in AI-assisted scoping reviews:

Challenge	Symptoms	Potential Causes	Solutions	Prevention Strategies
AI Hallucinations in Citations	Non-existent sources, incorrect attribution, fabricated quotations	Prompt design, AI knowledge gaps, Insufficient verification	Cross-reference with databases, Use DOI verification, Consult original sources	Use structured prompts, Limit generative tasks, Implement verification protocols
Information Asymmetry in Search Results	Overlooked relevant studies, Overrepresentation of certain fields	Database bias, Language limitations, Search term specificity	Complement with manual searches, Use multiple databases, Consult subject experts	Utilize diverse databases, Include non-English sources, Employ backward/forward citation tracking
Data Extraction Inconsistencies	Variable extraction format, Missing data fields, Incorrect categorization	Inconsistent prompting, Document formatting variations, AI comprehension limits	Standardize extraction templates, Implement quality checks, Manual verification of sample	Pre-test extraction on diverse papers, Use structured data templates, Clear category definitions

4.4 Improving Visual Aids and Decision Support

4.4.1 Scoping Review Process Flow Diagram

Develop a comprehensive visual representation of the entire process:

[A visual workflow diagram would appear here showing the nine steps with main actions for each step, AI integration points, human oversight requirements, decision points, feedback loops, and expected outputs]

4.4.2 AI Tool Selection Decision Tree

Create a decision tree to help users select appropriate AI tools:

[A decision tree would appear here with branches based on: review stage, user expertise level, available resources (time/budget), data volume, collaboration requirements, leading to specific tool recommendations]

4.4.3 Quality Assurance Checklists

Develop stage-specific quality assurance checklists:

Example: AI-Enhanced Data Extraction Quality Checklist

Extraction template tested on diverse sample of papers
AI tools calibrated with known examples
Consistency checks implemented between human and AI extraction
Uncertain AI extractions flagged and reviewed
Random sample of 10% of papers manually verified
Field completeness assessed (≥ 95% complete)
Contextual accuracy verified by subject expert
Extraction bias assessment completed

5. Implementation Considerations

5.1 Balancing Comprehensiveness and Usability

While enhancing the guide's comprehensiveness, care must be taken to maintain usability:

Modular structure: Organize enhanced content in clearly defined modules allowing users to focus on relevant sections.
Progressive disclosure: Implement a tiered information structure with essential guidance followed by optional advanced content.
Visual signposting: Use consistent visual cues to distinguish basic guidance from advanced techniques.

5.2 Audience Differentiation

Consider differentiating guidance for different user groups:

Novice researchers: Provide more structured workflows and simplified starting points.
Experienced researchers: Offer advanced techniques and customization options.
AI experts vs. AI novices: Include separate tracks addressing different levels of AI familiarity.

5.3 Sustainability and Updates

Ensure the enhanced guide remains current and sustainable:

Tool-agnostic principles: Focus on underlying principles that transcend specific AI tools.
Update mechanisms: Implement clear processes for regular reviews and updates.
Community contribution: Facilitate user contributions of new examples, tools, and techniques.

6. Addressing Potential Counterarguments

6.1 Over-reliance on AI Tools

Concern: Enhanced guidance on AI tools may promote over-reliance and diminish critical thinking.

Response: The improved guide should emphasize stronger human oversight frameworks, clearly defining non-delegable human responsibilities and implementing verification protocols for AI outputs. By explicitly addressing AI limitations and providing robust examples of critical evaluation, the guide can promote responsible AI use rather than dependency.

6.2 Increased Complexity

Concern: Adding more detailed guidance may overwhelm users and reduce accessibility.

Response: The enhanced guide should implement progressive disclosure principles, allowing users to access basic guidance before exploring advanced techniques. Clear visual organization, a tiered structure, and interactive navigation can help users find relevant information without encountering overwhelming complexity.

6.3 Rapid Technological Change

Concern: Specific AI tool recommendations may quickly become outdated.

Response: The guide should focus on underlying principles and methodological approaches rather than specific tool features. Tool recommendations should be supplemented with evaluation frameworks enabling users to assess new tools as they emerge. A structured update process is essential for maintaining relevance.

7. Conclusion

The "Guide to Produce Scoping Literature Reviews Using AI Tools" provides a valuable foundation for researchers integrating AI into the scoping review process. The recommendations presented in this report aim to enhance its utility through more detailed guidance on grey literature, improved AI tool integration, expanded practical examples, and enhanced visual decision support.

By implementing these improvements, the guide can better serve diverse users across the TIM field and beyond, supporting rigorous, efficient, and transparent scoping reviews that leverage AI capabilities while maintaining methodological integrity. The enhanced guide would not only improve immediate research outcomes but also contribute to establishing best practices in the rapidly evolving field of AI-enhanced evidence synthesis.

Future developments should consider creating interactive digital versions of the guide, establishing a community of practice for ongoing refinement, and developing specialized modules for different disciplinary contexts beyond TIM.