AI-Assisted Scoping Reviews: A Critical Analysis
Acknowledgement: This content was generated by ChatGPT Deep Research. View original prompt and source.
Executive Summary
This critical analysis evaluates the Guide to Produce Scoping Literature Reviews Using AI Tools for clarity, completeness, usability, and methodological rigor, focusing on scoping review best practices (e.g., Arksey & O’Malley, Levac et al., JBI, PRISMA-ScR), instructional design, educational usability, and AI-human collaboration principles. Overall, the guide is exceptionally comprehensive and innovative in integrating AI at each step. It provides clear step-by-step instructions and TIM-specific examples, aligning well with foundational scoping review frameworks while addressing the unique aspects of AI assistance.
However, its thoroughness can result in dense detail, potentially challenging novice users. Key gaps include the omission of a formal stakeholder consultation step and managing heavy reliance on AI outputs. The analysis below provides ratings (1=poor to 5=excellent) for each major section. Ethical considerations (bias, plagiarism) and opposing perspectives are discussed. Despite minor inconsistencies, the guide is a robust resource, highly usable and methodologically sound, transforming scoping reviews into an accessible, tech-augmented practice while upholding rigor and ethics.
Part 1: Foundation of Scoping Reviews
1.1 When to Use a Scoping Review
Concisely explains scenarios for scoping reviews (broad questions, emerging topics, heterogeneous literature, minimal appraisal need, gap identification), aligning well with literature. Uses clear language, bullet points, and relevant TIM examples (e.g., mapping quantum computing apps). Correctly contrasts with systematic reviews. Suggestion: Explicitly state optional nature of quality appraisal.
1.2 Key Frameworks
Comprehensively covers major frameworks (Arksey & O’Malley, Levac et al., JBI, PRISMA-ScR) and introduces "AI-focused updates." Describes each with TIM examples. Strong alignment with foundational methods. Suggestions: Cite key publications; clarify that "AI updates" are emerging methods, not a formal framework yet.
1.3 Benefits of Using AI
Impressively enumerates AI advantages (speed, discovery, organization, scalability, error reduction, insight generation, cost savings). Aligns with AI-human collaboration concepts and current academic discussions. Clear bullet points. Suggestion: Briefly mention the human-in-the-loop principle here (elaborated later).
1.4 Limitations of Scoping Reviews
Thoroughly lists ten limitations (lack of appraisal, no meta-analysis, breadth vs depth, heterogeneity, bias risk, resource intensity, scope creep, grey lit omission, need for updates). Comprehensive and methodologically aligned. Clarity slightly reduced by volume; usability could improve with grouping. Addresses grey literature risks well.
1.5 Limitations of Using AI Tools
Excellent section confronting AI risks (hallucinations, context lack, bias, recency focus, grey lit difficulty, inconsistency, ethics/plagiarism) with concrete mitigation steps for each. Easy-to-follow Limitation → Mitigation format. Aligns with AI ethics frameworks emphasizing human oversight. Proactively addresses concerns and reinforces methodological rigor.
1.6 AI Biases
Deeper dive into specific AI biases (selection, algorithmic, confirmation, citation, language/accessibility, hallucination) with TIM examples and mitigations (diverse search, cross-verification, counter-keywords, manual checks). Actionable and ethically sound. Usability slightly impacted by list length; a summary table could help.
1.7 Human Oversight
Clearly reiterates the indispensable role of human experts (monitoring AI, evaluating credibility, intervening on misinterpretations). Emphasizes blending AI efficiency with human contextual knowledge. Aligns with human-in-the-loop principles and ethical responsibility. Could mention documenting oversight actions.
1.8 Step-by-Step Grey Literature Searches
Standout section with a detailed 7-step procedure for grey literature tailored to TIM (define scope, keywords, sources, search databases, competitor info, contact experts, filter/evaluate). Highly useful, merging traditional and business-intelligence approaches. Addresses AI's limitation in finding grey lit. Lengthy but logically structured. Suggestion: Mention potential AI assistance in finding grey sources.
1.9 Reference Management
Highlights importance of reference management tools (Zotero, Mendeley, EndNote) with AI features. Explains benefits (efficiency, formatting, collaboration, detection). Practical advice. Aligns with reproducibility and transparency (PRISMA-ScR). Could mention other tools but covers the main ones.
Part 2: Method to Produce Scoping Reviews
Part 2 outlines a nine-step method integrating AI for efficiency while requiring manual validation. It emphasizes AI enhances, not replaces, human expertise. Steps cover the entire process from question formulation to dissemination, including ethics. This framing aligns with augmented intelligence and best practices.
2.1 Step 1: Formulate the Review Question and Scope
Exhaustive guidance on defining the question and scope using AI (ChatGPT for ideas) and human oversight. Thoroughly explains PCC framework with multiple TIM examples and covers alternatives (CIMO, ECLIPSE, etc.). Details scope parameters (time, literature types, geography) and lists concrete outputs (question, objectives, keywords, criteria). Methodologically rigorous but lengthy; a summary could aid usability.
2.2 Step 2: Search for Articles
Covers search strategy development and execution, integrating AI roles (keyword generation) and human refinement. Recommends specific AI tools (ChatGPT, Consensus, Perplexity, Elicit, OpenRead) with capabilities described. Lists diverse evidence sources (academic, industry, preprints, patents) and databases. Provides excellent example search queries for TIM topics. Effectively merges classic search methodology with AI augmentation.
2.3 Step 3: Select Articles
Details study screening and selection, combining AI tools (Rayyan for prioritization/deduplication) with human processes (dual screening). Explains title/abstract then full-text screening phases. Provides practical guidance on using Rayyan. Emphasizes the importance of dual screening for reducing bias and increasing reliability. Stresses documenting the process via PRISMA-ScR flow diagram and exclusion logs. Wisely positions AI as supportive, not autonomous, in selection.
2.4 Step 4: Extract Data
Covers data charting/extraction, mixing AI assistance (summarization, categorization via tools like Elicit, SciSpace) with mandatory human verification (checking accuracy, context, methods differences). Lists key data elements and TIM-specific examples. Provides very useful example table structures (PCC-based and technology assessment). Tool suggestions are numerous but enhance usability. Emphasizes iterative refinement of extraction forms.
2.5 Step 5: Analyze and Synthesize Data
Addresses collating and summarizing extracted data using AI (theme generation, mapping) and human validation (verifying themes, ensuring depth, applying theory). Covers descriptive summaries, narrative synthesis, and thematic/visual analysis (concept maps, citation networks). Requires tracking decisions and documenting process for transparency. Ensures analysis remains methodical and theory-informed despite AI use. Thorough, but length might challenge some users.
2.6 Step 6: Interpret Results
Focuses on deriving meaning and implications. AI assists (summaries, suggestions) but humans critically evaluate, integrate theory, and add context (policy, practice). Unique section on translating research into TIM actions (product opportunities, strategy, competitive intelligence) with concrete examples. Covers explaining significance, identifying review limitations, and maintaining the broad, exploratory perspective of scoping reviews. Emphasizes verifying AI interpretations.
2.7 Step 7: Write the Scoping Review
Guides drafting the manuscript using AI (draft generation) with crucial human oversight (refining coherence, tone, rigor, citations). Includes unique, highly practical templates for translating results into actionable assets (Research Vignette, Pitch Presentation) for TIM audiences. Advises AI-assisted writing tools (ChatGPT, Grammarly) alongside manual editing, fact-checking, and plagiarism checks. Covers revision, quality control, and documenting methods (including AI use) following PRISMA-ScR.
Note: Some content regarding 'turning findings into assets' and templates appears to overlap between sections 2.6, 2.7, and potentially 2.9 in the source text. This webpage attempts to present the core ideas logically.
2.8 Step 8: Incorporate Ethical Considerations
Exhaustive section making ethics an integrated step. Covers AI usage transparency, bias mitigation (in sources and process), plagiarism avoidance (using tools like Turnitin), data privacy/security (GDPR/CCPA), compliance with guidelines (e.g., ACM Code), and ethical stakeholder engagement (if applicable). Discusses broader ethical implications of AI in TIM. Mandates clear documentation (AI disclosure, audit trails). A hallmark of the guide's commitment to responsible research.
2.9 Step 9: Disseminate Findings
Comprehensive strategies for sharing results with diverse audiences. AI assists (summaries, visuals, platform suggestions) with human oversight (accuracy, tailoring). Covers traditional routes (journals, conferences) and modern approaches (policy briefs, open science platforms like OSF/Figshare, public engagement via blogs, social media, infographics). Lists concrete outputs for a multi-faceted dissemination plan. Encourages maximizing impact beyond academia.
2.10 Checklist for Authors and Reviewers
Provides a highly useful summary checklist covering key tasks from steps 1–9. Excellent for self-assessment or peer review, reinforcing critical items like defining PCC, ensuring comprehensive search, dual screening, data extraction, visualization, actionable interpretation, ethical compliance, and dissemination. Serves as a quick reference and quality control tool.
- Step 1: Define PCC, actionable question, scope.
- Step 2: Identify sources, precise search strategy, use AI refinement.
- Step 3: Use Rayyan, apply criteria, dual screen, AI prioritization.
- Step 4: Extract characteristics, drivers/barriers, gaps.
- Step 5: Visualize insights, use AI summaries, network analysis.
- Step 6: Translate to action, strategic guidance, competitive reports.
- Step 7: Craft vignette/pitch, communicate for impact.
- Step 8: Ethics: bias mitigation, transparency, data privacy.
Part 3: Updating Scoping Review Guide
Acknowledges the evolving nature of methods and AI tools, outlining the guide's strengths and a robust process for version control and updates to maintain relevance and accuracy.
3.1 Strengths of the Guide
Summarizes key strengths: transforming reviews into TIM assets, step-by-step TIM examples, efficient AI integration, inclusion of grey literature, focus on human oversight for integrity, and emphasis on ethical AI use. Provides a reflective overview of the guide's value proposition.
3.2 Version Control and Updates System
Details a rigorous system for updates: semantic versioning, scheduled 6-month reviews, ad-hoc updates, transparent changelog, stakeholder feedback solicitation, editorial board review, and accessibility of versions (current v1.0, March 4, 2025). Demonstrates commitment to maintaining a living, credible document.
3.3 AI Assistance and Human Oversight Disclosure
Exemplary disclosure statement detailing how AI (ChatGPT, Perplexity, Gemini, Grok) assisted in drafting (~50% initial content) and how humans provided extensive oversight (validation, citation checks, bias detection, multiple reviews) to ensure accuracy, coherence, and rigor. Asserts alignment with university guidelines and promises continued responsible integration in updates. Builds trust and models transparency.
3.4 Ways to Contribute
Invites community contributions to improve the guide (suggest tech enhancements, corrections, troubleshooting, examples, grey lit methods, fill gaps, refine AI limitations/oversight guidance). Fosters a collaborative ethos and provides contact information (Tony Bailetti). Turns the guide into an evolving community resource.
3.5 Acknowledgements
Briefly acknowledges contributions from TIM faculty and students, indicating community input and real-world testing have informed the guide, lending credibility.
3.6 Epilogue
Provides concluding thoughts urging researchers to balance AI efficiency with rigor, ethics, and transparency. Reiterates AI should augment, not replace, human expertise and critical evaluation. Contextualizes the guide within the evolving research landscape emphasizing oversight and collaboration. A fitting summary of the guide's philosophy.
Overall Assessment & Recommendations
Overall Assessment
The guide demonstrates exceptional thoroughness, integrating AI thoughtfully into the scoping review process while upholding methodological best practices (Arksey & O'Malley, PRISMA-ScR). Its structure is logical, enhanced by practical TIM examples, templates, and checklists. It proactively addresses AI risks through consistent emphasis on human oversight and ethical considerations.
Minor gaps include limited formal discussion of stakeholder consultation (though acknowledged) and potential for content overlap. The heavy reliance on specific AI tools necessitates the planned regular updates. Overall, it is a highly valuable resource scoring 4 or 5/5 on most criteria for its sections.
Actionable Recommendations
- Streamline Content: Merge or clarify overlapping sections (e.g., dissemination assets/templates) to reduce redundancy.
- Add Stakeholder Consultation (Optional): Include a brief optional step or sub-section on conducting stakeholder consultation, citing Levac et al. and referencing ethical requirements.
- Enhance Visual Aids: Incorporate flowcharts (overall process, AI/human interplay), example outputs (PRISMA diagram snippet), or annotated AI summary examples to aid understanding.
- Add Troubleshooting Section: Develop a dedicated section or appendix addressing common issues encountered when using AI tools (e.g., irrelevant results, inaccurate summaries) with solutions.
- Regularly Update Tool Info: Diligently follow the version control plan (3.2) to keep AI tool recommendations current, potentially via an online companion page.
- Include Reference List: Add a formal reference list for key frameworks (Arksey & O’Malley, PRISMA-ScR, JBI) and potentially major AI tools for academic rigor.
- Minor Edits: Correct typos (e.g., "elect" vs "select") and ensure consistent section numbering in future revisions.
Conclusion
The guide reviewed represents a significant contribution, expertly marrying traditional scoping review methodology with the potential of AI assistance. It provides a comprehensive, practical, and ethically grounded roadmap for TIM researchers. By implementing the suggested refinements, particularly enhancing visual aids and formally including stakeholder consultation guidance, its utility can be further amplified. Its commitment to transparency, human oversight, and continuous updates positions it as a leading resource for navigating the complexities of AI-augmented research synthesis in a rapidly evolving technological landscape.