First, the search strategy. Since time is tight, they need something focused. Maybe start with key databases like PubMed, IEEE Xplore, and ACM Digital Library. Also, include Google Scholar and preprints from arXiv or SSRN. Search terms should combine AI and qualitative analysis. Maybe "AI tools for qualitative data analysis" or "machine learning qualitative research". Limit the search to the last five years to keep it current. Use snowballing by checking references of key papers.

Next, inclusion criteria. They need to be pragmatic. Tools should be AI-driven, specifically for qualitative data. Available in English, peer-reviewed or reputable preprints. Exclude non-AI tools, those not in English, or without enough detail. Maybe set a publication date from 2019 onwards. Also, exclude tools that aren't accessible or require payment beyond a certain point.

For the screening process, streamline it. Use tools like Rayyan for deduplication. Two reviewers for title/abstract screening with a third for conflicts. Full-text review with the same approach. Maybe a 10% sample for consistency checks. Use a spreadsheet to track decisions.

Quality assessment needs to be quick. Focus on key aspects: clarity of purpose, technical validation, usability info, peer review status, and user feedback. A simple checklist with yes/no questions. Assign a quality score and categorize as high, medium, or low. Maybe spend 10-15 minutes per tool.

Synthesis method should categorize tools by features, AI techniques, strengths, limitations. Use a table for quick overview. Thematic analysis for common trends. Highlight top tools based on quality and features. Present findings in a summary table and narrative.

Wait, the user might be a researcher or a project manager needing to implement tools quickly. They might not have time for a full review. So, the rapid review must balance comprehensiveness with speed. They might need actionable results, so the synthesis should highlight practical tools. Also, they might not mention it, but cost and ease of integration could be factors. Maybe include those in the criteria if possible. Also, check if the tools are actively maintained or have support. But time is limited, so focus on what's feasible in two weeks. Need to make sure each step is time-boxed. Maybe allocate days for each phase: search (2 days), screening (3 days), assessment (2 days), synthesis (3 days), with buffer days.

Also, consider if the user is in academia or industry. If industry, commercial tools might be more relevant. If academia, open-source or research-focused tools. The user didn't specify, so maybe keep criteria neutral.

In the search strategy, maybe include some industry sources like Gartner or tech blogs, but peer-reviewed sources are more credible. However, preprints can include the latest tools not yet in journals.

Inclusion criteria: availability in English, but what about the tools themselves? Maybe the tool's interface needs to be in English. That's an important point. Also, during quality assessment, user feedback could be from case studies or testimonials. If a tool has been used in published studies, that's a plus.

Synthesis should compare features like NLP capabilities, coding support, visualization, collaboration features. Maybe group tools by use cases: thematic analysis, discourse analysis, etc.

Potential pitfalls: missing key tools because of limited search sources. Maybe include some key websites like towardsdatascience.com or analyticsindiamag.com for more coverage. But time is a constraint.

Also, check for existing reviews or meta-analyses on the topic to save time. If there's a recent review, maybe update it with new tools.

Overall, the plan needs to be efficient but thorough enough to capture the main tools. Prioritize sources that are likely to have relevant info and use strict inclusion to filter quickly. The user will need clear, concise results with recommendations. Make sure each step is doable within the timeframe, and maybe suggest tools that come up frequently as top candidates.

Here's a rapid review framework to complete your analysis in 2 weeks, balancing rigor with time constraints:

1. Abbreviated Search Strategy

• Core Databases: PubMed, IEEE Xplore, ACM Digital Library, Scopus (prioritize 2-3).


• Gray Literature: Google Scholar (first 5 pages), arXiv, SSRN, and AI tool directories (e.g., AIhub, FutureTools.io).


• Search Terms:


• AI tools: "AI qualitative analysis," "machine learning qualitative research," "NLP for text analysis," "automated coding software."


• Methodology: "qualitative data analysis," "thematic analysis," "content analysis."


• Filters: Last 5 years (2019–2024), English language.


• Snowballing: Scan references of 3–5 key papers for additional tools.

2. Pragmatic Inclusion Criteria

• Tools explicitly using AI/ML/NLP for qualitative analysis (e.g., coding, theme extraction).


• Tools with public documentation/case studies (free or paid).


• Peer-reviewed articles, preprints, or reputable industry reports.

• General-purpose text analysis tools (e.g., sentiment analysis APIs).


• Tools without English documentation.


• Tools requiring custom coding (unless widely adopted, e.g., Hugging Face pipelines).

3. Streamlined Screening Process

• Title/Abstract Screening: 2 reviewers, 1 hour/day for 2 days.


• Focus: Does the tool use AI for qualitative analysis?


• Full-Text Screening: 2 reviewers, 3 days.


• Focus: Availability, functionality, and documentation.


• Conflict Resolution: Third reviewer adjudicates disagreements.

4. Quick Quality Assessment

• Purpose Clarity: Does the tool clearly define its AI-driven qualitative analysis use case?


• Technical Validation: Is there evidence of accuracy/testing (e.g., peer-reviewed validation, user case studies)?


• Usability: Is there documentation or tutorials for non-technical users?


• Accessibility: Free trial/demo available?


• Updates: Active development (GitHub commits, version updates in past year)?

Time Allocation: 2 days.

5. Efficient Synthesis Method

Narrative Synthesis:

• Themes: Group tools into categories (e.g., thematic analysis, discourse analysis).


• Trends: Highlight common strengths (e.g., speed) and limitations (e.g., transparency).


• Recommendations: Identify 3–5 top tools based on quality score and usability.

Sample Timeline

• Days 1–2: Search + inclusion criteria.


• Days 3–7: Screening.


• Days 8–9: Quality assessment.


• Days 10–12: Synthesis + write-up.

Let me know if you need templates for screening forms or quality checklists!