AI Tools for Thesis Writing in 2025: Save Time & Improve Quality
Writing a thesis can involve many steps, such as reading academic papers, organising ideas, and formatting references. This process takes time, especially when working with large amounts of research.
In this blog, we’ll introduce you to some of the best AI tools designed for thesis writing.

These tools don't replace original thinking or writing. Instead, they handle time-consuming tasks so researchers can focus on developing their ideas and arguments.
- Time savings: AI tools can summarise articles in minutes rather than hours, helping researchers review more literature efficiently
- Writing clarity: These tools identify confusing sentences, awkward phrasing, and inconsistencies that might distract readers
- Organisation: Many tools help track sources, organise notes, and maintain consistent formatting throughout long documents
How ThesisAI, Gatsbi, Writefull, And Thesify Enhance Research
Each of these AI tools supports different aspects of thesis writing. When used together, they can help with the entire process from initial research to final editing.
ThesisAI

ThesisAI generate a complete scientific document (up to 50 pages) with a single prompt. Seamlessly integrates with LaTeX, Overleaf, Zotero, and Mendeley for effortless formatting and citation management. Includes automated research capabilities via Semantic Scholar for smart paper discovery. Supports writing in 20+ languages for global academic needs. See examples here.
Gatsbi

Gatsbi helps maintain logical structure throughout a thesis. It analyses how ideas connect across chapters and sections, ensuring the argument flows smoothly from beginning to end.
The tool supports technical elements like equations, citations, and data tables, making it especially useful for scientific writing. Unlike some AI tools, Gatsbi focuses on organising existing content rather than generating new text.
Writefull

Writefull improves academic language by checking grammar, vocabulary, and tone. It integrates with Microsoft Word and Overleaf (for LaTeX documents), providing feedback as you write.
The tool understands discipline-specific language and conventions, offering suggestions that match academic expectations. Its features include abstract generation, title refinement, and paraphrasing options for clearer expression.
Thesify

Thesify evaluates the strength of academic arguments and evidence. Rather than focusing only on grammar, it analyses whether claims are supported, arguments are logical, and ideas are clearly expressed.
The feedback resembles what you might receive from a professor or peer reviewer, with comments on structure, reasoning, and evidence use. This helps identify weaknesses in the argument before submission.
| Tool | Main Purpose | Works Best For | Compatible With | Special Features |
| TheseAI | generating a complete scientific document | Literature reviews | Web browsers | Concept mapping, source comparison |
| Gatsbi | Organising thesis structure | Maintaining logical flow | Web platform | Supports technical elements, citation integration |
| Writefull | Improving academic language | Grammar and style refinement | Word, Overleaf | Real-time feedback, LaTeX support |
| Thesify | Evaluating argument quality | Getting expert-like feedback | Web browsers | Logic assessment, evidence evaluation |
Key Functions of AI Thesis Writing Tools
AI thesis tools typically excel in three main areas: summarising research, improving language, and managing citations. Understanding these functions helps choose the right tool for specific writing challenges.
Research Summaries
AI summarisation tools read academic papers and create concise overviews highlighting key findings, methods, and conclusions. This technique helps researchers quickly grasp the main points without reading entire articles.
For example, when reviewing literature for a psychology thesis, the AI might extract information about study participants, experimental design, and statistical results. This allows researchers to compare multiple studies more efficiently.
However, AI thesis tools raise risks such as academic misconduct, loss of originality, privacy concerns, and inaccurate outputs if misused. University policies differ, so always check regulations, use AI responsibly, and critically review all AI-generated work. These summaries serve as starting points for deeper reading, not replacements for understanding the full text. You still need to verify important details and evaluate the quality of the original research.
Language Improvement
Language tools analyse writing for grammar, clarity, vocabulary, and academic tone. They identify issues like wordiness, passive voice overuse, and unclear phrasing that might confuse readers.
Some tools, like Writefull, understand discipline-specific conventions. They can suggest appropriate terminology for fields like medicine, engineering, or literature, helping writers match the expectations of their academic community.
These suggestions appear as you write or during review, similar to having an editor check your work. The writer maintains control over which changes to accept, ensuring the text still reflects their voice and ideas.
Citation Management
Citation tools format references according to academic styles like APA, MLA, or Chicago. They help maintain consistency throughout the document and ensure all sources are properly acknowledged.
Many tools can generate citations automatically from a DOI, URL, or article title. They also check for missing information and formatting errors that might otherwise be overlooked.
This function helps prevent unintentional plagiarism by making proper attribution easier. It also saves time during the final editing process when references need to be checked and formatted.
How to Use AI Tools Ethically in Academic Writing
Universities increasingly recognise that AI tools can support the writing process, but they distinguish between acceptable assistance and potential academic misconduct.
Acceptable uses typically include grammar checking, citation formatting, and research organisation. These functions help improve presentation without changing the core content or ideas.
Most institutions draw the line at using AI to generate content or develop arguments. The thinking, analysis, and conclusions should come from the student, not from an AI system.
- Be transparent: Many universities now ask students to disclose which AI tools they used and how they were applied in the writing process
- Verify information: AI tools sometimes make mistakes with citations or summaries, so always check against original sources
- Maintain ownership: The ideas, arguments, and conclusions should reflect your understanding, not text generated by an AI
Universities like Cambridge, Oxford, and MIT have published guidelines explaining how students can use AI tools appropriately. These policies typically focus on using AI as an assistant rather than a replacement for original work.
How to Select the Right AI Tool for Your Field
Different academic fields have specific writing conventions and requirements. Choosing tools that understand these differences improves their effectiveness.
Science and Engineering
Science and engineering theses often include technical elements like equations, data tables, and specialised terminology. Tools like Gatsbi and Writefull support these features, including LaTeX formatting commonly used in these fields.
These disciplines typically use structured formats with clearly defined sections (introduction, methods, results, discussion). AI tools can help maintain this structure and ensure each section contains the expected content.
Humanities and Social Sciences
Humanities and social science writing often emphasises argument development, theoretical frameworks, and textual analysis. Tools like Thesify that evaluate argument quality and evidence use are particularly helpful.
These fields may use discipline-specific citation styles like Chicago or MLA. Citation tools that support these formats help maintain proper attribution of sources, especially when working with primary texts and archival materials.
Interdisciplinary Research
Interdisciplinary theses combine methods and conventions from multiple fields. This can create challenges when using AI tools designed for specific disciplines.
Researchers working across disciplines may benefit from using multiple tools together. For example, using Writefull for language improvement while using Thesify for feedback on argument structure and evidence.
Practical Integration of AI Tools in Thesis Writing
Adding AI tools to your writing process works best with a thoughtful approach. Starting small and gradually expanding tool use helps avoid overwhelming changes to established work habits.
Begin with One Chapter
Testing an AI tool on a single thesis chapter or section provides a clear sense of its benefits and limitations. This approach allows for comparison between AI-assisted and regular writing processes.
After completing the test section, evaluate whether the tool improved quality, saved time, or created new challenges. This information helps decide whether to continue using the tool for the full thesis.
Create Clear Boundaries
Deciding in advance which tasks you'll use AI for helps maintain academic integrity. For example, you might use AI for grammar checking and citation formatting but not for generating content or developing arguments.
These boundaries ensure the thesis remains your own intellectual work while still benefiting from technological assistance with mechanical aspects of writing.
Combine Complementary Tools
Different tools excel at different tasks. Using them together creates a more complete support system for thesis writing.
A sample workflow might include:
- Using TheseAI to gather and summarise research for the literature review
- Organising the thesis structure with Gatsbi to ensure logical flow
- Improving language and style with Writefull during drafting
- Getting feedback on argument quality with Thesify before submission
This approach uses each tool for its strengths while avoiding over-reliance on any single program.
The Future of AI in Thesis Writing
AI tools for academic writing continue to evolve, becoming more specialised and integrated with research workflows. Current trends suggest several developments on the horizon.
These tools increasingly understand discipline-specific conventions and terminology. This specialisation helps them provide more relevant feedback for different academic fields.
Integration between research platforms and writing tools is also improving. This allows researchers to move smoothly between finding sources, taking notes, drafting content, and formatting references.
As these tools develop, access to quality academic content remains essential. Zendy's AI-powered research library offers access to peer-reviewed articles that complement AI writing tools, creating a more complete research environment.
Frequently Asked Questions About AI Thesis Writing Tools
How do AI thesis writing tools protect my data and research?
Most academic AI tools have privacy policies stating they don't use uploaded content to train their models and maintain confidentiality of research materials, though specific protections vary by platform.
Do universities allow students to use AI tools for thesis writing?
Many universities permit AI tools for editing, citation formatting, and grammar checking, but typically require original thinking and content creation from the student; check your institution's specific guidelines.
How do TheseAI, Gatsbi, Writefull, and Thesify differ from general AI like ChatGPT?
These specialised academic tools understand scholarly conventions, integrate with research workflows, and focus on specific aspects of thesis writing rather than generating general content like ChatGPT.
Can AI thesis tools help with discipline-specific terminology?
Yes, tools like Writefull and Thesify recognise field-specific terminology and academic conventions across disciplines, offering more relevant suggestions than general writing tools.
Will AI tools for thesis writing improve my research quality?
AI tools can enhance presentation quality and efficiency but don't improve the underlying research quality; they help organise and communicate ideas more clearly rather than generating new insights.

Zendy to Showcase AI-Powered Library Innovations at the Charleston Conference 2025
We’re thrilled to announce that Zendy will be taking the stage at this year’s Charleston Conference, one of the most anticipated gatherings for librarians, publishers, and information professionals worldwide. Join us on November 4 at 11:30 AM in Salon 2, Gaillard Centre, for our live demo session titled: “Transforming Your Library Services with Zendy AI Tools.” In this interactive session, Mike Perrine (VP of Sales and Marketing, WT Cox) and Kamran Kardan (Co-Founder, Zendy) will demonstrate how Zendy’s innovative AI-driven tools are revolutionising the way libraries manage content, empower discovery, and enhance user engagement. Zendy helps solve one of the biggest challenges libraries face today, providing users with faster, smarter access to research insights. Our platform enables instant article summarisation, concept extraction, and trusted AI-powered answers through our intelligent assistant, ZAIA. With Zendy, libraries can streamline their services and give researchers a more intuitive, efficient way to interact with scholarly information. We’re also proud to share that Zendy has been selected for the prestigious Charleston Premiers, a showcase recognising the most innovative and forward-thinking products reshaping scholarly communication. Representing Zendy at the Premiers will be Kamran Kardan (Co-Founder) and Lisette van Kessel (Head of Marketing), who will present how Zendy’s mission to make knowledge accessible and affordable continues to evolve through technology and partnership. The Charleston Conference has long been a hub for meaningful dialogue and collaboration in the world of academic information services, and we’re excited to be part of shaping its future. Event Details: Session: Transforming Your Library Services with Zendy AI Tools Date: November 4, 2025 Time: 11:30 AM Location: Salon 2, Gaillard Centre We look forward to connecting with fellow innovators, librarians, and partners, and showcasing how Zendy AI is redefining what’s possible for libraries and researchers alike. Don’t miss it, see how Zendy is shaping the future of knowledge discovery. To register and learn more about the Charleston Conference, please visit: https://www.charleston-hub.com/the-charleston-conference/about-the-conference/ .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }

From Boolean to Intelligent Search: A Librarian’s Guide to Smarter Information Retrieval
For decades, librarians have been the trusted guides in the vast world of information. But today, that world has grown into something far more complex. Databases multiply, metadata standards evolve, and users expect instant answers. Traditional search still relies on structured logic, keywords, operators, and carefully crafted queries. AI enhances this by interpreting intent rather than just words. Instead of matching text, AI tools for librarians analyse meaning. A researcher looking for “climate change effects on migration” won’t just get papers containing those words, but research exploring environmental displacement, socioeconomic factors, and regional studies. This shift from keyword to context means librarians can spend less time teaching a researcher how to “speak database” and more time helping them evaluate and use the results effectively. The Evolution of Library Search Traditional search engines focus on keywords and often return long lists of potential matches. With AI, libraries can now benefit from search engines that employ natural language processing (NLP) and machine learning (ML) to understand user queries and map them to the most relevant resources, even when key terms are missing or imprecise. Semantic search, embedding-based retrieval, and vector databases allow AI to find conceptually similar resources and suggest new directions for research. Examples of AI Tools for Librarians AI ToolMain FunctionLibrarian BenefitZendyAI-powered platform offering literature discovery, summarisation, keyphrase highlighting, and PDF analysisSupports researchers with instant insights, simplifies literature reviews, and improves discovery across 40M+ publicationsConsensusAI-powered academic search enginemanaging citation libraries, efficient literature reviewEx Libris PrimoIntegrates AI for discovery and metadata managementImproves record accuracy and user experienceMeilisearchFast, scalable vector search with NLPEnhanced search for large content databases The Ethics of Intelligent Search AI doesn’t just retrieve; it prioritises. AI tools for librarians determine which results appear first, whose research receives visibility, and what remains hidden. This creates ethical questions around transparency and bias. Librarians are uniquely positioned to question those algorithms, advocate for equitable access, and ensure users understand how results are ranked. In an AI-driven world, digital literacy extends beyond knowing how to search—it’s about learning how machines think. In conclusion AI tools for librarians are becoming more accessible. Platforms now integrate summarisation, concept mapping, and citation analysis directly into search. helping librarians and users avoid unreliable content. For libraries, experimenting with these tools can mean faster reference responses, smarter cataloguing, and better support for researchers drowning in information overload. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }

Why AI like ChatGPT still quotes retracted papers?
AI models like ChatGPT are trained on massive datasets collected at specific moments in time, which means they lack awareness of papers retracted after their training cutoff. When a scientific paper gets retracted, whether due to errors, fraud, or ethical violations, most AI systems continue referencing it as if nothing happened. This creates a troubling scenario where researchers using AI assistants might unknowingly build their work on discredited foundations. In other words: retracted papers are the academic world's way of saying "we got this wrong, please disregard." Yet the AI tools designed to help us navigate research faster often can't tell the difference between solid science and work that's been officially debunked. ChatGPT and other assistants tested Recent studies examined how popular AI research tools handle retracted papers, and the results were concerning. Researchers tested ChatGPT, Google's Gemini, and similar language models by asking them about known retracted papers. In many cases, they not only failed to flag the retractions but actively praised the withdrawn studies. One investigation found that ChatGPT referenced retracted cancer imaging research without any warning to users, presenting the flawed findings as credible. The problem extends beyond chatbots to AI-powered literature review tools that researchers increasingly rely on for efficiency. Common failure scenarios The risks show up across different domains, each with its own consequences: Medical guidance: Healthcare professionals consulting AI for clinical information might receive recommendations based on studies withdrawn for data fabrication or patient safety concerns Literature reviews: Academic researchers face citation issues when AI assistants suggest retracted papers, damaging credibility and delaying peer review Policy decisions: Institutional leaders making evidence-based choices might rely on AI-summarised research without realising the underlying studies have been retracted A doctor asking about treatment protocols could unknowingly follow advice rooted in discredited research. Meanwhile, detecting retracted citations manually across hundreds of references proves nearly impossible for most researchers. How Often Retractions Slip Into AI Training Data The scale of retracted papers entering AI systems is larger than most people realise. Crossref, the scholarly metadata registry that tracks digital object identifiers (DOIs) for academic publications, reports thousands of retraction notices annually. Yet many AI models were trained on datasets harvested years ago, capturing papers before retraction notices appeared. Here's where timing becomes critical. A paper published in 2020 and included in an AI training dataset that same year might get retracted in 2023. If the model hasn't been retrained with updated data, it remains oblivious to the retraction. Some popular language models go years between major training updates, meaning their knowledge of the research landscape grows increasingly outdated. Lag between retraction and model update Training Large Language Models requires enormous computational resources and time, which explains why most AI companies don't continuously update their systems. Even when retraining occurs, the process of identifying and removing retracted papers from massive datasets presents technical challenges that many organisations haven't prioritised solving. The result is a growing gap between the current state of scientific knowledge and what AI assistants "know." You might think AI systems could simply check retraction databases in real-time before responding, but most don't. Instead, they generate responses based solely on their static training data, unaware that some information has been invalidated. Risks of Citing Retracted Papers in Practice The consequences of AI-recommended retracted papers extend beyond embarrassment. When flawed research influences decisions, the ripple effects can be substantial and long-lasting. Clinical decision errors Healthcare providers increasingly turn to AI tools for quick access to medical literature, especially when facing unfamiliar conditions or emerging treatments. If an AI assistant recommends a retracted study on drug efficacy or surgical techniques, clinicians might implement approaches that have been proven harmful or ineffective. The 2020 hydroxychloroquine controversy illustrated how quickly questionable research spreads. Imagine that dynamic accelerated by AI systems that can't distinguish between valid and retracted papers. Policy and funding implications Government agencies and research institutions often use AI tools to synthesise large bodies of literature when making funding decisions or setting research priorities. Basing these high-stakes choices on retracted work wastes resources and potentially misdirects entire fields of inquiry. A withdrawn climate study or economic analysis could influence policy for years before anyone discovers the AI-assisted review included discredited research. Academic reputation damage For individual researchers, citing retracted papers carries professional consequences. Journals may reject manuscripts, tenure committees question research rigour, and collaborators lose confidence. While honest mistakes happen, the frequency of such errors increases when researchers rely on AI tools that lack retraction awareness, and the responsibility still falls on the researcher, not the AI. Why Language Models Miss Retraction Signals The technical architecture of most AI research assistants makes them inherently vulnerable to the retraction problem. Understanding why helps explain what solutions might actually work. Corpus quality controls lacking AI models learn from their training corpus, the massive collection of text they analyse during development. Most organisations building these models prioritise breadth over curation, scraping academic databases, preprint servers, and publisher websites without rigorous quality checks. The assumption is that more data produces better models, but this approach treats all papers equally regardless of retraction status. Even when training data includes retraction notices, the AI might not recognise them as signals to discount the paper's content. A retraction notice is just another piece of text unless the model has been specifically trained to understand its significance. Sparse or inconsistent metadata Publishers handle retractions differently, creating inconsistencies that confuse automated systems: Some journals add "RETRACTED" to article titles Others publish separate retraction notices A few quietly remove papers entirely This lack of standardisation means AI systems trained to recognise one retraction format might miss others completely. Metadata، the structured information describing each paper, often fails to consistently flag retraction status across databases. A paper retracted in PubMed might still appear without warning in other indexes that AI training pipelines access. Hallucination and overconfidence AI hallucination occurs when models generate plausible-sounding but false information, and it exacerbates the retraction problem. Even if a model has no information about a topic, it might confidently fabricate citations or misremember details from its training data. This overconfidence means AI assistants rarely express uncertainty about the papers they recommend, leaving users with no indication that additional verification is needed. Real-Time Retraction Data Sources Researchers Should Trust While AI tools struggle with retractions, several authoritative databases exist for manual verification. Researchers concerned about citation integrity can cross-reference their sources against these resources. Retraction Watch Database Retraction Watch operates as an independent watchdog, tracking retractions across all academic disciplines and publishers. Their freely accessible database includes detailed explanations of why papers were withdrawn, from honest error to fraud. The organisation's blog also provides context about patterns in retractions and systemic issues in scholarly publishing. Crossref metadata service Crossref maintains the infrastructure that assigns DOIs to scholarly works, and publishers report retractions through this system. While coverage depends on publishers properly flagging retractions, Crossref offers a comprehensive view across multiple disciplines and publication types. Their API allows developers to build tools that automatically check retraction status, a capability that forward-thinking platforms are beginning to implement. PubMed retracted publication tag For medical and life sciences research, PubMed provides reliable retraction flagging with daily updates. The National Library of Medicine maintains this database with rigorous quality control, ensuring retracted papers receive prominent warning labels. However, this coverage is limited to biomedical literature, leaving researchers in other fields without equivalent resources. DatabaseCoverageUpdate SpeedAccessRetraction WatchAll disciplinesReal-timeFreeCrossrefPublisher-reportedVariableFree APIPubMedMedical/life sciencesDailyFree Responsible AI Starts with Licensing When AI systems access research papers, articles, or datasets, authors and publishers have legal and ethical rights that need protection. Ignoring these rights can undermine the sustainability of the research ecosystem and diminish trust between researchers and technology providers. One of the biggest reasons AI tools get it wrong is that they often cite retracted papers as if they’re still valid. When an article is retracted, e.g. due to peer review process not being conducted properly or failing to meet established standards, most AI systems don’t know, it simply remains part of their training data. This is where licensing plays a crucial role. Licensed data ensures that AI systems are connected to the right sources, continuously updated with accurate, publisher-verified information. It’s the foundation for what platforms like Zendy aim to achieve: making sure the content is clean and trustworthy. Licensing ensures that content is used responsibly. Proper agreements between AI companies and copyright holders allow AI systems to access material legally while providing attribution and, when appropriate, compensation. This is especially important when AI tools generate insights or summaries that are distributed at scale, potentially creating value for commercial platforms without benefiting the sources of the content. in conclusion, consent-driven licensing helps build trust. Publishers and authors can choose whether and how their work is incorporated into AI systems, ensuring that content is included only when rights are respected. Advanced AI platforms, such as Zendy, can even track which licensed sources contributed to a particular output, providing accountability and a foundation for equitable revenue sharing. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
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