AI tools for literature discovery can help us find relevant studies, explore connections between papers, and organise the evidence we collect. But choosing a tool becomes easier when we know which part of the research process we need help with. Searching for papers, understanding their findings, and screening them for a systematic review involve different tasks.
In this post, I bring together twelve tools I have reviewed on Educators Technology. Some focus on academic search, others on citation networks, reading, or structured review management. This is my own selection, based on my reviews and research interests, and I am not affiliated with any of these tools. Each entry links to a fuller review if you want to explore it further.
Comparing AI tools for literature discovery
The table below compares their main roles. Several combine AI features with conventional search or review tools; their inclusion here does not mean every function uses generative AI.
| Tool | Main role | Useful starting point |
|---|---|---|
| Google Scholar | Scholarly search and citation tracking | Finding papers and following references |
| Consensus | Research questions and AI synthesis | Exploring what studies say about a question |
| Elicit | Discovery and evidence extraction | Building structured comparison tables |
| ResearchRabbit | Connected-paper discovery | Expanding a reading list from relevant papers |
| Connected Papers | Visual similarity graphs | Exploring research around one paper |
| Litmaps | Citation mapping and monitoring | Tracing connections and following new work |
| Scite | Citation-context analysis | Examining how later studies discuss a paper |
| R Discovery | Personalised recommendations | Keeping up with relevant publications |
| SciSpace | Search and AI-assisted reading | Questioning papers and comparing their content |
| Scholarcy | Structured document summaries | Organising papers already collected |
| Rayyan | Collaborative review screening | Selecting studies against eligibility criteria |
| Covidence | Structured review management | Coordinating screening, extraction, and appraisal |
1. Google Scholar
Google Scholar provides a broad starting point for finding scholarly literature. Keyword searches, date filters, “Cited by,” and “Related articles” help us locate papers and follow research beyond the first results page. Its newer AI-assisted features include Scholar Labs for detailed research questions and Quick Read for previews of relevant papers, where available.
I would use it to develop search vocabulary, locate accessible versions of papers, and trace earlier and later work around an influential study. For a substantial review, I would also search appropriate subject databases and record how sources were found. Read my full Google Scholar review.
2. Consensus
Consensus lets us search academic literature using ordinary research questions. It retrieves relevant papers and offers AI-generated syntheses connected to the sources. For suitable yes-or-no questions, its Consensus Meter presents apparent agreement or disagreement among the papers analysed.
This can help us get oriented around a question such as whether retrieval practice supports vocabulary learning. I would inspect the cited studies, compare their populations and methods, and check what the synthesis leaves out. An agreement indicator cannot establish evidence quality or complete coverage. Read my full Consensus review.
3. Elicit
Elicit connects paper discovery with structured evidence extraction. We can search with a research question and organise study information into comparison tables. Columns might cover participants, research design, interventions, findings, or limitations, with supporting source passages available for checking extracted information.
I would use it when a reading list has reached the point where I need to compare studies consistently. For education research, that might mean separating measured learning outcomes from students’ reported perceptions. Every important extraction still needs checking against the paper. Read my full Elicit review.
4. ResearchRabbit
ResearchRabbit helps us discover papers through connections among publications and authors. We can begin with relevant studies, organise them into collections, and explore related work through visual networks and recommendations.
I see a useful role here when keyword searching keeps returning the same papers. Starting with several relevant studies may lead to neighbouring research using different terminology. The starting collection matters, so I would compare recommendations with database results and keep a record of useful additions. Read my full ResearchRabbit review.
5. Connected Papers
Connected Papers creates a visual graph around a selected paper. Similarity is based on shared references and patterns of being cited together. The graph helps us explore neighbouring publications, while Prior Works and Derivative Works provide further routes into earlier and subsequent research.
I would use it to become familiar with the research surrounding a promising article. One important distinction: proximity represents similarity, so a line between papers does not necessarily mean one directly cites the other. Read the studies before interpreting a cluster as evidence of agreement. Read my full Connected Papers review.
6. Litmaps
Litmaps combines literature discovery with visual citation maps and monitoring. It can use relevant starting papers to suggest additional publications, display connections, and alert us to new related work. Shared maps also provide a way to discuss a developing reading list with colleagues.
For an ongoing project, I would use Litmaps to extend searches and revisit the literature as new papers appear. It could also support a graduate supervision meeting where students explain how their sources connect. A dense map does not establish strong evidence, and an empty area does not establish a research gap. Read my full Litmaps review.
7. Scite
Scite adds context to citation tracking. Its Smart Citations show passages around citations and classify them as supporting, contrasting, or mentioning an earlier paper. It also includes research question answering and tools for examining manuscript references.
I would use it to investigate what happened after a widely cited study was published: did later researchers extend its findings, question them, or simply mention the paper? The classification is a lead for closer reading. Check the complete citing passage and the study behind it before drawing conclusions. Read my full Scite review.
8. R Discovery
R Discovery focuses on finding relevant publications and maintaining an ongoing reading routine. Personalised recommendations, research alerts, saved reading lists, and AI-assisted questions about papers help users follow topics and explore suggested material.
I would use it to keep a professional reading list current between larger searches. A teacher following research on feedback or inclusive assessment could review recommendations and save promising studies for closer reading. Personalised feeds reflect selected interests and interactions; they cannot establish that a search is comprehensive. Read my full R Discovery review.
9. SciSpace
SciSpace brings literature search, PDF questioning, and comparative data extraction into one workspace. We can find papers, ask about unfamiliar concepts or methods, and organise selected study details into tables.
I would use it for focused questions during reading, such as how a study measured an outcome or defined a concept. It can also help organise comparisons across papers. Generated explanations and reviews need verification, especially when methodological qualifications affect what a finding means. Read my full SciSpace review.
10. Scholarcy
Scholarcy turns collected papers and other documents into structured summary cards. It separates important information into sections and provides ways to annotate, organise, and export reading material. Its main contribution comes when we already have documents to work through.
I would use it to prepare an initial overview of a reading collection, then add my own notes about relevance, methods, and limitations. It also offers a useful teaching activity: compare a summary with the original paper and identify missing context. Read my full Scholarcy review.
11. Rayyan
Rayyan helps researchers manage references after searching academic databases. Its review workflow includes duplicate management, title-and-abstract screening, full-text assessment, labels, and recorded decisions. Blind screening supports independent assessments before reviewers discuss disagreements.
I would consider it for a systematic or scoping review with explicit eligibility criteria and a substantial set of records. Relevance ranking can help prioritise screening, but eligibility remains a research decision. Some advanced functions depend on the plan or institutional access. Read my full Rayyan review.
12. Covidence
Covidence supports structured evidence reviews through reference importing, duplicate removal, independent screening, conflict resolution, extraction, and quality assessment. AI-assisted extraction can suggest information alongside supporting text for reviewers to verify.
I would use it when a team needs a shared process for moving from search results to included studies and checked evidence tables. It is particularly relevant when documenting exclusion reasons and reviewer decisions matters to the review method. Researchers still conduct the searches, appraise the evidence, and write the synthesis. Read my full Covidence review.
This guide is also available for free download in PDF format: AI Literature Discovery Tools for Teachers and Researchers.
How I would combine these tools
Start with a focused question and search the databases appropriate to your discipline. Google Scholar, Consensus, or Elicit can help identify candidate papers and useful terminology. When you have relevant starting studies, ResearchRabbit, Connected Papers, or Litmaps can extend discovery through publication connections.
Use Scite to examine citation context, and consider SciSpace or Scholarcy for support while reading. For a formal evidence review, Rayyan or Covidence can help manage selection and documentation. Keep checked references and notes in a reference manager so the work remains useful when writing begins.
You do not need all twelve. Choose tools around a specific task, check current access conditions, and always return to the original studies. For a systematic review, document searches, screening decisions, and any AI assistance used. The final interpretation should come from your examination of the evidence.
