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Top 6 AI Writing Assistant Tools for Research

Jul 9, 2025 |

26 Mins Read

Many students and researchers today use artificial intelligence (AI) to help improve their writing. These tools are not only for checking spelling or grammar, but they can help organise ideas, improve sentence structure, and manage citations.

Writers working on research papers often spend extra time editing and citing sources correctly. AI writing assistant tools are designed to support those specific tasks by using advanced language technology.

In this article, we explore how AI writing assistant tools like PaperPal, Jenny.AI, Aithor, Wisio.app, Trinka AI, and Grammarly work. Each tool offers a different approach to writing assistance, depending on what kind of research you are doing and what stage you are in.

What are AI Writing Assistant Tools

AI Writing Assistant Tools are software applications that utilise artificial intelligence to enhance writing. They analyse text using machine learning and natural language processing (NLP), which allows them to detect issues with grammar, tone, structure, and clarity.

Natural language processing is a type of AI that helps computers understand and generate human language. This technology allows writing assistants to do more than just catch spelling errors, they can suggest rewording, offer synonyms, and help improve sentence flow.

Early writing tools mainly checked for spelling and punctuation. Over time, they evolved into systems that assist with academic writing, including literature reviews, paper organisation, and citation formatting.

Main benefits of AI writing assistant tools:

Time Efficiency: These tools speed up writing by suggesting edits and checking grammar in real time.
Language Enhancement: They improve sentence structure and formal tone for academic audiences.
Citation Management: Many tools generate citations and apply citation styles automatically.
Research Workflow: Some AI writing assistant tools help structure research papers by suggesting outlines.

Comparing Key Research Writing Assistants

The table below compares six AI writing assistant tools used in academic research:

Tool Name	Best For	Key Features	Free Version
PaperPal	Journal submissions	Journal formatting, grammar checks	Yes
Jenny.AI	Drafting academic content	AI autocomplete, citation generator	Yes
Aithor	Structured drafting	Plagiarism detection, writing suggestions	Yes
Wisio.app	Peer-reviewed feedback	Human and AI editing, multilingual support	Limited
Trinka AI	ESL academic writing	Technical term support, citation formatting	Yes
Grammarly	General writing	Grammar checks, browser integration	Yes

Language Enhancement Capabilities

Each tool approaches grammar, tone, and style differently:

PaperPal: focuses on academic publishing with discipline-specific language suggestions.
Jenny.AI: offers real-time assistance through AI autocomplete for academic writing.
Aithor: helps users draft content with tone guidance and structure prompts.
Wisio.app: provides detailed editorial feedback tailored to scientific writing.
Trinka AI: helps non-native English speakers with academic tone corrections.
Grammarly: covers general grammar improvements but adapts to academic contexts.

Research Focused Features

These tools support research writing in different ways:

PaperPal: supports journal-specific formatting and citation checks.
Jenny.AI: generates in-text citations and formats reference lists.
Aithor: detects unoriginal content and suggests better source integration.
Wisio.app: allows collaborative editing with structured feedback.
Trinka AI: identifies missing citations and formats according to style guides.
Grammarly: includes basic citation suggestions and plagiarism detection.

PaperPal

PaperPal is an AI writing assistant tool that mostly focuses on helping researchers prepare academic manuscripts. It is designed to support you with the process of submitting papers to journals by ensuring that writing meets formatting and language requirements.

The tool includes journal-specific formatting options. This allows researchers and students to format their papers according to the guidelines of a selected journal, including structure, citations, and reference styles.

It also provides language support for technical writing by identifying discipline-specific terminology and suggesting corrections to align with academic tone and clarity.

Key features:

Journal Compatibility: Matches manuscript formatting to journal guidelines, including citation style.
Technical Language Support: Refines field-specific vocabulary and academic phrases.
Integration Capabilities: Connects with research tools like Overleaf and Word.

Jenni AI

Jenni AI helps with research-based writing tasks. It drafts academic content, manages citations, and supports the structure of academic arguments.

The platform generates text based on prompts or uploaded documents. It works with academic papers and uses AI to build sections of content that align with your topic.

Jenni AI also includes citation tools that format references in over 1,700 styles. You can save sources in a library and insert citations directly into your draft while writing.

Key features:

AI-Powered Drafting: Generates academic content from prompts or uploaded research.
Citation Integration: Supports in-text citations and reference management in multiple formats.
Collaborative Features: Enables group access to shared libraries and drafts.

Aithor

Aithor supports the academic writing process while helping maintain originality and proper writing practices.

It checks for unoriginal content by comparing written text against existing sources. This helps users revise their work to reduce overlap and avoid academic misconduct.

The platform allows users to add scholarly sources into their documents with an interface for inserting citations and generating references using common academic styles.

Key features:

Original Content Generation: enhances your writing without compromising your originality
Academic Integrity Tools: Flags duplicated phrases and offers paraphrasing suggestions.
Research Integration: Adds peer-reviewed sources and formats them according to guidelines.

Wisio App

Wisio supports academic collaboration by helping researchers work together on documents and improve their work through structured feedback.

The platform includes systems for reviewers to leave targeted comments on drafts. These comments are organised to help writers identify issues with clarity, logic, or formatting.

It also includes tools for managing research projects with task assignments, progress tracking, and draft organisation. Multiple users can edit documents at the same time, seeing changes in real time.

Key features:

Feedback System: Enables structured peer feedback with in-line comments.
Workflow Management: Supports task tracking and drafting stages for collaborative projects.
Collaborative Editing: Allows multiple users to edit a document simultaneously.

Trinka AI

Trinka AI supports writers who speak English as a second language (ESL). Its tools identify grammar and usage issues common among non-native speakers.

The platform recognises technical language from various academic fields such as engineering, medicine, and social sciences. It suggests corrections based on the context of the discipline.

Trinka also supports researchers preparing manuscripts for publication by checking for consistency with international journal standards, including formatting and language clarity.

Key features:

ESL Support: Offers grammar correction and formal language suggestions for non-native English writers.
Technical Terminology: Refines field-specific vocabulary across multiple disciplines.
Publication Standards: Evaluates manuscripts for compliance with journal requirements.

Grammarly

Grammarly helps users write with correct grammar, punctuation, and clarity. It works in academic, business, and casual writing by scanning text for errors and offering real-time suggestions.

For academic writing, Grammarly supports clarity and formal tone by identifying passive voice, informal phrasing, and awkward sentence structure. However, it does not provide research-specific features like citation formatting.

The tool works across emails, web browsers, word processors, and mobile apps. While helpful for basic academic editing, its focus is on general writing improvement rather than specialised research tasks.

Key features:

Universal Applications: Functions in Word, Google Docs, emails, and browsers.
Tone Adjustments: Offers suggestions to align writing with academic formality.
Integration Ecosystem: Works with Chrome, Microsoft Office, and email clients.

How to Choose the Right AI Writing Assistant for Your Research

Selecting an AI writing assistant depends on your specific academic task. Different tools support different aspects of the writing process.

Evaluating Your Writing Goals

Consider what you're writing before choosing a tool:

For a thesis, look for long-form structuring and reference tracking.
For journal articles, check for journal-specific formatting and academic tone adjustments.
For grant proposals, find tools with outlining and collaborative editing features.

Some tools help generate initial drafts, while others focus on editing, formatting, and feedback.

Integrating AI With Existing Tools

AI writing assistant tools work best when they connect with other research tools. Check if the assistant works with reference managers like Zotero or EndNote to maintain accurate citations.

Many platforms integrate with word processors like Google Docs, Microsoft Word, or Overleaf. Others allow importing and exporting in formats such as .docx, PDF, or LaTeX.

Ensuring Academic Integrity

Using AI writing assistant tools raises questions about originality. These tools don't replace human thinking but assist with language and formatting.

To use AI ethically:

Disclose AI use when required by your institution.
Review all AI-generated content manually for accuracy.
Revise AI-generated text before submission.

Empowering Research Writing and Next Steps

AI writing assistant tools have changed how academic writing is planned and processed. These tools help with grammar correction, citation formatting, and research workflow.

In the future, AI writing assistant tools will likely offer deeper integration with citation managers, research databases, and publishing platforms. Some may add voice input, multilingual support, and automatic journal formatting.

Access to reliable academic sources remains essential for these tools to function effectively. Platforms that provide full-text academic content allow AI writing assistant tools to generate accurate citations and summaries. Zendy offers one such environment by combining scholarly content with AI tools that support literature review and citation.

Discover how Zendy's AI-powered research library can enhance your writing workflow at Zendy.io.

How do AI writing assistant tools maintain academic integrity?

AI writing assistant tools do not generate original research or ideas. They improve grammar, structure, and clarity, allowing the writer's own thoughts and arguments to remain central.

Which AI writing assistant offers the best citation management?

PaperPal and Trinka AI include built-in tools for formatting citations in academic styles. Jenni AI supports over 1,700 citation formats and allows integration with reference managers.

Are free versions of these AI writing assistant tools sufficient for research?

Free versions include basic grammar checks but typically exclude advanced features like formatting, citation tools, or deep academic editing. Paid versions provide more comprehensive research support.

Can these tools help with discipline-specific terminology?

Trinka AI and PaperPal recognise subject-specific vocabulary in fields like medicine, engineering, and social sciences. They check for accuracy and consistency in technical language.

From Curator to Digital Navigator: Evolving Roles for Modern Librarians

With the growing integration of digital technologies in academia, librarians are becoming facilitators of discovery. They play a vital role in helping students and researchers find credible information, use digital tools effectively, and develop essential research skills. At Zendy, we believe this shift represents a new chapter for librarians, one where they act as mentors, digital strategists, and AI collaborators. Zendy’s AI-powered research assistant, ZAIA, is one example of how librarians can enhance their work using technology. Librarians can utilise ZAIA to assist users in clarifying research questions, discovering relevant papers more efficiently, and understanding complex academic concepts in simpler terms. This partnership between human expertise and AI efficiency allows librarians to focus more on supporting critical thinking, rather than manual searching. According to our latest survey, AI in Education for Students and Researchers: 2025 Trends and Statistics, over 70% of students now rely on AI for research. Librarians are adapting to this shift by integrating these technologies into their services, offering guidance on ethical AI use, research accuracy, and digital literacy. However, this evolution also comes with challenges. Librarians must ensure users understand how to evaluate AI-generated content, check for biases, and verify sources. The focus is moving beyond access to information, it’s now about ensuring that information is used responsibly and critically. To support this changing role, here are some tools and practices modern librarians can integrate into their workflows: AI-Enhanced DiscoveryUsing tools like ZAIA to help researchers refine queries and find relevant studies faster. Research Data Management Organising, preserving, and curating datasets for long-term academic use. Ethical AI and Digital Literacy Training Teaching researchers how to verify AI outputs, evaluate bias, and maintain academic integrity. Collaborative Digital Spaces Facilitating research communication through online repositories and discussion platforms. In conclusion, librarians today are more than curators, they are digital navigators shaping how knowledge is accessed, evaluated, and shared. As technology continues to evolve, so will its role in guiding researchers and students through the expanding world of digital information. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202512 Mins ReadDiscover

Strategic AI Skills Every Librarian Must Develop

In 2026, librarians who understand how AI works will be better equipped to support students and researchers, organise collections, and help patrons find reliable information faster. Developing a few key AI skills can make everyday tasks easier and open up new ways to serve your community. Why AI Skills Matter for Librarians AI tools that recommend books, manage citations, or answer basic questions are becoming more common. Learning how these tools work helps librarians: Offer smarter, faster search results. Improve cataloguing accuracy. Provide better guidance to researchers and students. Remember, AI isn’t replacing professional judgment; it’s supporting it. Core AI Literacy Foundations Before diving into specific tools, it helps to understand some basic ideas behind AI. Machine Learning Basics:Machine learning means teaching a computer to recognise patterns in data. In a library setting, this could mean analysing borrowing habits to suggest new titles or resources. Natural Language Processing (NLP):NLP is what allows a chatbot or search tool to understand and respond to human language. It’s how virtual assistants can answer questions like “What are some journals about public health policy?” Quick Terms to Know: Algorithm: A set of steps an AI follows to make a decision. Training Data: The information used to “teach” an AI system. Neural Network: A type of computer model inspired by how the brain processes information. Bias: When data or systems produce unfair or unbalanced results. Metadata Enrichment With AI Cataloguing is one of the areas where AI makes a noticeable difference. Automated Tagging: AI tools can read through titles and abstracts to suggest keywords or subject headings. Knowledge Graphs: These connect related materials, for example, linking a book on climate change with recent journal articles on the same topic. Bias Checking: Some systems can flag outdated or biased terminology in subject classifications. Generative Prompt Skills Knowing how to “talk” to AI tools is a skill in itself. The clearer your request, the better the result. Try experimenting with prompts like these: Research Prompt: “List three recent studies on community reading programs and summarise their findings.” Teaching Prompt: “Write a short activity plan for a workshop on evaluating online information sources.” Summary Prompt: “Give me a brief overview of this article’s key arguments and methods.” Adjusting tone or adding detail can change the outcome. It’s about learning how to guide the tool rather than letting it guess. Ethical Data Practices AI tools can be useful, but they also raise questions about privacy and fairness. Librarians have always cared deeply about protecting patron information, and that remains true with AI. Keep personal data anonymous wherever possible. Review AI outputs for signs of bias or misinformation. Encourage clear policies around how data is stored and used. Ethical AI is part of a librarian’s duty to maintain trust and fairness. Automating Everyday Tasks AI can take over some of the small, routine jobs that fill up a librarian’s day. Circulation: Systems can send overdue reminders automatically or manage renewals. Chatbots: Basic questions like “What are the library hours?” can be handled instantly. Collection Management: AI can spot patterns in borrowing data to suggest which books to keep, reorder, or retire. Building Your Learning Path Getting comfortable with AI doesn’t have to mean earning a new degree. Start small: Take short online courses or micro-certifications in AI literacy. Join librarian groups or online forums where people share practical tips. Block out one hour a week to try out a new tool or attend a webinar. A little consistent learning goes a long way. Making AI Affordable Many smaller libraries worry about cost, but not every tool is expensive. Free Tools: Some open-access AI platforms, like Zendy, offer affordable access to research databases and AI-powered features. Shared Purchases: Partnering with other libraries to share licenses can cut costs. Cloud Services: Pay-as-you-go plans mean you only pay for what you actually use. There’s usually a way to experiment with AI without stretching the budget. Showing Impact Once AI tools are in use, it’s important to show their value. Track things like: Time saved on cataloguing or circulation tasks. Patron feedback on new services. How often are AI tools used compared to manual systems? Numbers matter, but so do stories. Sharing examples, like a student who found research faster thanks to a new search feature, can make your case even stronger. And remember, the future of librarianship is about using AI tools in libraries thoughtfully to keep libraries relevant, reliable, and welcoming spaces for everyone. .wp-block-image img { max-width: 75% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202511 Mins ReadDiscover

Key Considerations for Training Library Teams on New Research Technologies

The integration of Generative AI into academic life appears to be a significant moment for university libraries. As trusted guides in the information ecosystem, librarians are positioned to help researchers explore this new terrain, but this transition requires developing a fresh set of skills. Training your library team on AI-powered research tools could move beyond technical instruction to focus on critical thinking, ethical understanding, and human judgment. Here is a proposed framework for a training program, organised by the new competencies your team might need to explore. Foundational: Understanding Access and Use This initial module establishes a baseline understanding of the technology itself. Accessing the Platform: Teach the technical steps for using the institution's approved AI tools, including authentication, subscription models, and any specific interfaces (e.g., vendor-integrated AI features in academic databases, institutional LLMs, etc.). Core Mechanics: Explain what a Generative AI platform (like a Large Language Model) is and, crucially, what it is not. Cover foundational concepts like: Training Data: Familiarise staff with how to access the institution’s chosen AI tools, noting any specific authentication requirements or limitations tied to vendor-integrated AI features in academic databases. Prompting Basics: Introduce basic prompt engineering, the art of crafting effective, clear queries to get useful outputs. Hallucinations: Directly address the concept of "hallucinations," or factually incorrect/fabricated outputs and citations, and emphasise the need for human verification. Conceptual: Critical Evaluation and Information Management This module focuses on the librarian's core competency: evaluating information in a new context. Locating and Organising: Train staff on how to use AI tools for practical, time-saving tasks, such as: Generating keywords for better traditional database searches. Summarising long articles to quickly grasp the core argument. Identifying common themes across a set of resources. Evaluating Information: This is perhaps the most critical skill. Teach a new layer of critical information literacy: Source Verification: Always cross-check AI-generated citations, summaries, and facts against reliable, academic sources (library databases, peer-reviewed journals). Bias Identification: Examine AI outputs for subtle biases, especially those related to algorithmic bias in the training data, and discuss how to mitigate this when consulting with researchers. Using and Repurposing: Demonstrate how AI-generated material should be treated—as a raw output that must be heavily edited, critiqued, and cited, not as a final product. Social: Communicating with AI as an Interlocutor The quality of AI output is often dependent on the user’s conversational ability. This module suggests treating the AI platform as a possible partner in a dialogue. Advanced Prompt Engineering: Move beyond basic queries to teach techniques for generating nuanced, high-quality results: Assigning the AI a role (such as a 'sceptical editor' or 'historical analyst') to potentially shape a more nuanced response. Practising iterative conversation, where librarians refine an output by providing feedback and further instructions, treating the interaction as an ongoing intellectual exchange. Shared Understanding: Practise using the platform to help users frame their research questions more effectively. Librarians can guide researchers in using the AI to clarify a vague topic or map out a conceptual framework, turning the tool into a catalyst for deeper thought rather than a final answer generator. Socio-Emotional Awareness: Recognising Impact and Building Confidence This module addresses the human factor, building resilience and confidence Recognising the Impact of Emotions: Acknowledge the possibility of emotional responses, such as uncertainty about shifting professional roles or discomfort with rapid technological change, and facilitate a safe space for dialogue. Knowing Strengths and Weaknesses: Reinforce the unique, human-centric value of the librarian: critical thinking, contextualising information, ethical judgment, and deep disciplinary knowledge, skills that AI cannot replicate. The AI could be seen as a means to automate lower-level tasks, allowing librarians to focus on high-value consultation. Developing Confidence: Implement hands-on, low-stakes practice sessions using real-world research scenarios. Confidence grows from successful interaction, not just theoretical knowledge. Encourage experimentation and a "fail-forward" mentality. Ethical: Acting Ethically as a Digital Citizen Ethical use is the cornerstone of responsible AI adoption in academia. Librarians must be the primary educators on responsible usage. Transparency and Disclosure: Discuss the importance of transparency when utilizing AI. Review institutional and journal guidelines that may require students and faculty to disclose how and when AI was used in their work, and offer guidance on how to properly cite these tools. Data Privacy and Security: Review the potential risks associated with uploading unpublished, proprietary, or personally identifiable information (PII) to public AI services. Establish and enforce clear library policies on what data should never be shared with external tools. Copyright and Intellectual Property (IP): Discuss the murky legal landscape of AI-generated content and IP. Emphasise that AI models are often trained on copyrighted material and that users are responsible for ensuring their outputs do not infringe on existing copyrights. Advocate for using library-licensed, trusted-source AI tools whenever possible. Combating Misinformation: Position the librarian as the essential arbiter against the spread of AI-generated misinformation. Training should include spotting common AI red flags, teaching users how to think sceptically, and promoting the library’s curated, authoritative resources as the gold standard. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }

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