Learn how you can improve your writing process to write academic essays
Sep 25, 2023 |
17 Mins Read
In academia, where ideas and knowledge converge, the written word is a powerful tool for conveying research and arguments. However, the journey from a blank page to a polished essay involves more than inspiration; it demands a systematic and strategic approach. The steps involved in the writing process are considered the building blocks of an essay, the academic writing style itself provides a deep knowledge of the subject matter and helps writers construct evidence-based arguments within their respective fields. This blog post explores the essential steps, techniques, and insights to improve your writing process and gear the approach towards academic essays.
Essay Writing Basics
The purpose of academic essays is to advance ideas and exchange discourse amongst scholars while also teaching the writer to think critically and analyse various areas of research. When structuring and writing an academic essay, it is essential to plan the flow of information to complement one another strategically; the research points contained in each paragraph have to be simple to absorb and not overbearing.
An academic essay has 3 key components: introduction, body paragraph and conclusion.
Pre-Writing Phase
Before writing the essay, having several brainstorming sessions will help writers understand the topic, scope, and arguments within the academic essay. Brainstorming allows writers to build enthusiasm and commitment towards the essay, as the topic becomes clearer with research, discussion and planning. Listed below are a few effective brainstorming techniques:
- Mind Mapping
A mind map is essential for brainstorming as it tracks related concepts. The first step is to write down the larger subject and then write down anything that is relevant to that, this helps writers and researchers visualise all the information related to the topic. It can be used to either break down a larger subject or focus on a certain component of a subject, this is beneficial for academic writers as it helps generate new ideas, foster collaboration and organise information.
- Clustering
The cluster analysis is a great method to club or “cluster” information and data together over certain similarities. This learning technique can be adapted to a brainstorming method to allow academic writers to structure their essays strategically which would allow information and ideas to flow smoothly.
- Free Writing
Free writing is a brainstorming method geared towards writers, it allows writers to write about a topic with no rules, guidelines or structure. The aim is to write as the thoughts come so that the writer can establish how much information they have on a topic. There are 3 simple rules to get this method right: don’t pause to read anything you have written, don’t cross out or erase anything as that is editing and not writing, and finally, don’t worry about spelling or grammar. This method allows writers to generate their ideas and polish them later, rather than having a thought and letting it go.
After having productive brainstorming sessions, the next step is to start the research. Certain institutions have guidelines as to what they consider reputable resources, for example, Wikipedia is not considered an academic source of information as the pages can be edited and written by anyone. Access reputable academic databases and libraries to conduct your research, We have listed a few below:
Once the research phase is done, you will have gathered a good amount of resources and information on your essay topic. The next crucial step is to develop a thesis statement, an essay has to have a thesis statement to serve as a guide for the reader and develop the author’s argument. Furthermore, formulating the thesis statement allows authors to see how their ideas are perceived in a sentence or two. A strong thesis statement specifies one main idea and asserts the author’s conclusions on the essay question or topic.
For example, if your essay is about the implementation of sustainable practices in the transport sector, your thesis statement can be: In recent years, there has been a rise in sustainable initiatives, this essay highlights and argues that sustainability in transportation is beneficial for human advancement and slowing climate change.
Writing The Essay
Once the initial stages of brainstorming, research, and the formulation of a thesis statement are done, the writing process is equipped with a clear roadmap. Each paragraph in an academic essay serves as a building block, cementing the foundation of the thesis while allowing room to explore other perspectives.
- Introduction
The introductory paragraph of an academic essay sets the tone and outlines the map of the essay. It should give the reader a clear idea of the points, arguments and methods the essay will highlight and discuss. There are 4 main components of a good essay introduction paragraph; the hook, context, thesis statement and a clear structure of the essay.
Example: The rise of sustainable practices in the transport sector is imperative to its advancement (Hook). In recent years, the world has witnessed electric cars, alternative routes, carpooling applications, and improvements in public transport; these enhancements have encouraged the general population to utilise alternative methods of transport rather than driving their personal vehicles daily (Context). This essay states that sustainable practices in the transport sector are beneficial for human advancement and slowing climate change (Thesis). The essay discusses the development and implementation of sustainable aviation fuel in recent flights while acknowledging key drawbacks. Furthermore, the essay assesses how carpooling alternatives are valuable for the safety of the environment; and finally, the accessibility and affordability of public transport (Structure).
- Developing Arguments
To effectively develop the arguments that support the thesis statement, the writer should deconstruct the topic and map all possible aspects of the topic. Based on available research, literature and evidence; create a stance that has appropriate citations. Each body paragraph should break down the argument and end with an explanation as to why the essay’s stance is convincing.
- Structuring Body Paragraphs
In an academic essay, each body paragraph is dedicated to a specific point or argument; this paragraph would consist of a topic sentence, evidence, opposing research, context and explanation. Each developed argument should flow and serve the research paper’s positioning in the subject area. The best practice for structuring effective body paragraphs is to follow the P.E.E method which stands for point, evidence and explanation.
Example: The usage of sustainable aviation fuel is key to maintaining the same amount of weekly flights while reducing its effects on climate change (Point). In recent studies, Smith (2021) found that the components required to produce sustainable aviation fuel not only source environmentally friendly ingredients but also practice eco-friendly processes during the production and manufacturing phases (Evidence). The approach to creating sustainability in transport starts by examining the processes by which the vehicles are manufactured as the environmental output of those factories is significant, utilising sustainable aviation fuel eliminates harmful production practices and decreases pollution caused by aeroplanes (Explanation).
- Writing the Conclusion
The conclusion of an academic essay should be an impactful recap of the essay, which should include supporting evidence for the arguments presented; by this paragraph in the essay, the reader should be drawn to supporting the thesis statement.
Editing and Polishing
During the editing stage, it is common for authors to look for grammatical errors; while this is important, it’s also beneficial to keep an eye out for clarity issues. In academic writing, structuring clear and concise sentences is imperative so that all readers can efficiently comprehend the material.
Here is a check-list of what you should look out for while editing an academic essay:
- Correct running sentences with too many subordinate clauses
- Sentences should be written in active voice
- Assess whether a sentence is written in an academic and formal tone
- Assess whether the essay is structured for the intended audience and purpose
Finalising the Essay
Once the essay has reached the finalising phase, it’s important to refer to your institutional formatting guidelines and ensure that all the requirements have been met. Once that is done, the bibliography has to be double-checked to ensure the references are in the correct style without grammar and formatting mistakes. The bibliography is an essential part of an academic essay as it helps readers, professors, and researchers understand where the evidence was retrieved from and how the arguments were constructed; having an accurate bibliography also gives the essay credibility. The final step is to give the essay one last proofread to ensure that it is free of errors.
FAQs
How long should an essay be?
Ideally, an essay should be about 5-7 pages which should contain about 1500-2000 words. However, a detailed essay can range from anywhere between 8-10 pages containing about 2500-3000 words.
What are the key elements of a perfect essay?
Great essays have a clear and concise introduction, thesis and conclusion. The body paragraphs within a good essay flow and connect back to the thesis statement, creating cohesive arguments as the academic paper progresses.
How can I improve my essay-writing skills?
Improving your essay writing skills lies in the planning and proofreading phases rather than the writing itself. Before beginning your essay, plan out the paragraphs, and arguments, and follow the structures to create uniformed paragraphs. In the proofreading stage, keep an eye out for grammatical errors as well as clarity-related errors.
Are there any online resources to assist with essay writing?
The most useful essay-writing tool is Grammarly, it offers multiple suggestions and corrections as you write so that the corrections can be made simultaneously, further simplifying the proofreading stage.
How important is the thesis statement in an essay?
The thesis statement gives the essay direction and provides a clear roadmap to the writer. Every other component of the essay should support or explain the thesis statement.
How do I avoid plagiarism in my essay?
The most effective way to avoid plagiarism is to keep a record of all the sources you will utilise in your essay and then paraphrase the points, you will then have to cite the original author using in-text citations.
Digital Information Literacy Guidelines for Academic Libraries
Information literacy is the skill of finding, evaluating, and using information effectively. Data literacy is the skill of understanding numbers and datasets, reading charts, checking how data was collected, and spotting mistakes. Critical thinking is the skill of analysing information, questioning assumptions, and making sound judgments. With so many digital tools today, students and researchers need all three skills, not just to find information, but also to make sense of it and communicate it clearly. Why Academic Libraries Should Offer Literacy Programs Let’s face it: research can be overwhelming. Students face a flood of information, some of it unreliable or even misleading. Predatory journals, low-quality datasets, and confusing search results can make learning stressful. Libraries are more than book storage, they’re a place to build practical skills. Programs that teach information and data literacy help students think critically, save time, and feel more confident with research. Key Skills Students, Researchers, and Librarians Need Finding and Using Scholarly Content Knowing how to search a database efficiently is a big deal. Students should learn how to use filters, Boolean logic, subject headings and, of course, intelligent search. They should also know the difference between journal articles, conference papers, and open-access resources. Evaluating Sources and Data Not all information is equal. Programs should teach students how to check if sources are reliable, understand peer review, and spot bias in datasets. A few practical techniques, like cross-checking sources or looking for data provenance, can make research much stronger. Managing Information Ethically Citing sources properly, avoiding plagiarism, and respecting copyright are essentials. Tools like Zotero or Mendeley help keep references organised, so students spend less time managing files and more time on research. Sharing Findings Clearly Communicating is sharing, and sharing is caring. It’s one thing to collect information; it’s another to communicate it. Using infographics, slides, or storytelling techniques to make research more memorable. Ultimately, clear communication ensures that the work they’ve done can be understood, used, and appreciated by others. Frameworks That Guide Literacy Programs ACRL Framework: Provides six key concepts for teaching information literacy. EU DigComp / DigCompEdu: Covers digital skills for students and educators. Data Literacy Project: Helps students understand how to work with datasets, complementing traditional research skills. These frameworks help librarians structure programs so students get consistent, practical guidance. Steps to Build a Digital Literacy Program Audit Campus Needs: Talk to students and faculty, see what resources exist, and find gaps. Set Learning Goals: Decide what students should be able to do at the end, and make goals measurable. Select Content and Tools: Choose databases, software, and datasets that fit the library’s budget and tech setup. Create Short, Modular Lessons: Break skills into manageable pieces that build on each other. Launch and Improve: Introduce the program, gather feedback, and adjust lessons based on what works and what doesn’t. Teaching Strategies and Online Tools Flipped and Embedded Instruction Students watch a short video about search techniques at home, then practice in class. A librarian might join a research methods class, helping students build search strings live. Pre-class quizzes on topics like peer review versus predatory journals prepare students for hands-on exercises. Short Videos and Tutorials Quick videos (2–5 minutes) can teach one skill at a time, like citation management, evaluating sources, or basic data visualisation. Include captions, transcripts, and small practice exercises to reinforce learning. AI Summaries and Chatbots AI tools can summarise articles, suggest keywords, highlight main points, and even draft bibliographies. But they aren’t perfect, they can make mistakes, miss nuances, or misread complex tables. Human oversight is still important. Free Resources and Open Datasets Students can practice with free databases and datasets like DOAJ, PubMed, arXiv, Kaggle, or Zenodo. Using one of the open-access resources keeps programs affordable while providing real-world examples. Checking if Students Are Learning Before and After Assessments: Simple quizzes or tasks to see how skills improve. Performance Rubrics: Compare beginner, developing, and advanced levels in searching, evaluating, and presenting data. Analytics: Track which videos or tools students use most to improve future lessons. Working With Faculty Embedded Workshops: Librarians teach skills directly tied to assignments. Joint Assignments: Faculty design research projects that naturally teach literacy skills. Faculty Training: Show instructors how to integrate digital literacy into their courses. Tackling Challenges Staff Training: Librarians may need extra help with data tools. Peer mentoring and workshops work well. Limited Budgets: Open access tools, collaborative licensing, and free platforms help make programs feasible. Distance Learners: Make videos and tutorials accessible anytime, account for different time zones and internet access. Looking Ahead AI, open science, and global collaboration are changing research. AI can personalise learning, but it still needs oversight. Open science and FAIR data principles (set of guidelines for making research data Findable, Accessible, Interoperable, and Reusable to both humans and machines) encourage transparency and reproducibility. Libraries can also connect with international partners to share resources and best practices. FAQs How long does a program take to launch?Basic services can start in six months; full programs usually take 1–2 years. Do humanities students need data skills? Yes, focus is more on qualitative analysis and digital humanities tools. Where can libraries find free datasets? Government repositories, Kaggle, Zenodo, and university archives. Can small libraries succeed without data specialists?Yes, faculty collaboration and online resources can cover most needs. .wp-block-image img { max-width: 75% !important; margin-left: auto !important; margin-right: auto !important; }
From Boolean to Intelligent Search: A Librarian’s Guide to Smarter Information Retrieval
As a librarian, you’ve always been the person people turn to when they need help finding answers. But the way we search for information is changing fast. Databases are growing, new tools keep appearing, and students expect instant results. Only then will you know the true benefit of AI for libraries, to help you make sense of it all. From Boolean to Intelligent Search Traditional search is still part of everyday library work. It depends on logic and structure, keywords, operators, and carefully built queries. But AI adds something new. It doesn’t just look for words; it tries to understand what someone means. If a researcher searches for “climate change effects on migration,” an AI-powered tool doesn’t just pull results with those exact words. It also looks for studies about environmental displacement, regional challenges, and social impacts. This means you can spend less time teaching people how to “speak database” and more time helping them understand the research they find. The Evolution of Library Search Traditional search engines focus on matching keywords, which often leads to long lists of results. With AI, search tools can now read queries in natural language, just the way people ask questions, and still find accurate, relevant material. Natural language processing (NLP) and machine learning (ML) make it possible for search systems to connect related ideas, even when the exact words aren’t used. Features like semantic search and vector databases help AI recognise patterns and suggest other useful directions for exploration. Examples of AI Tools Librarians Can Use Tool / PlatformWhat It DoesWhy It Helps LibrariansZendyA platform that combines literature discovery, AI summaries, keyphrase highlighting, and PDF analysisHelps librarians and researchers access, read, and understand academic papers more easilyConsensusAn AI-powered academic search engine that summarises findings from peer-reviewed studiesHelps with literature reviews and citation managementEx Libris PrimoUses AI to support discovery and manage metadataImproves record accuracy and helps users find what they need fasterMeilisearchA fast, flexible search engine that uses NLPMakes it easier to search large databases efficiently The Ethics of Intelligent Search Algorithms influence what users see and what they might miss. That’s why your role is so important. You can help users question why certain results appear on top, encourage critical thinking, and remind them that algorithms are not neutral. Digital literacy today isn’t just about knowing how to search, it’s about understanding how the search works. In Conclusion AI tools for librarians are becoming easier to use and more helpful every day. Some platforms now include features like summarisation, citation analysis, and even plans to highlight retracted papers, something Zendy is working toward. Trying out these tools can make your work smoother: faster reference responses, smarter cataloguing, and better guidance for researchers who often feel lost in the flood of information. AI isn’t replacing your expertise, it’s helping you use it in new ways. And that’s what makes this moment exciting for librarians everywhere. .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; }
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom