Research Better: 6 Best AI Tools for Research Efficiency
Jan 25, 2024 |
8 Mins Read
The increased usage of AI tools has sparked many conversations in the world of academia, which inspired the innovation of AI tools for research. The integration of technology in academia has brought innovation that introduced digital libraries, plagiarism trackers and grammar-checking softwares, this changed the way researchers write and discover academic literature. In recent years, the academic sphere has witnessed the rapid growth and implementation of AI, which we believe can be leveraged to create efficiency in research.
As an AI-powered research library, Zendy provides key tools like summarisation and keyphrase highlighting to streamline the process of literature review, we also developed and launched ZAIA, the AI research assistant. These tools have been designed to create efficiency in research, which allows researchers to invest significant time in data analysis and their primary research.
With the growing integration of AI products, we believe in the ethical use of AI in the world of research while also harnessing the innovative technology to streamline research processes amongst students and researchers alike. According to a study that looked at the usage of AI amongst students, it was found that every fourth student (25.2%) uses AI-based tools frequently, while almost half of the students (47.8%) use AI-based tools occasionally. Slightly more than a third of the students (36.6%) do not use AI-based tools at all.
In this blog, we recommend the 6 best AI-driven tools to assist you with annotating, citing and more!
1. ZAIA AI Assistant for Research
ZAIA is a domain-specific LLM designed to assist researchers in understanding essential research concepts and finding relevant papers. This tool enhances the efficiency of the literature review process, providing answers backed by millions of academic research papers.
Housing over 200 million papers from all fields of science, this tool generates a comprehensive summary of an academic paper, including the area of study the research addressed and its overall impact on the discipline.
3. Paperguide
Paperguide is an all in one AI workspace for researchers to conduct literature reviews, understand and extract data from scientific papers, collaborate, manage and write research.
4. Tableau
This tool generates date visualisation and analytical tools for businesses and researchers. The platform is equipped with Einstein AI which is driven by machine learning and delivers predictions and recommendations within Tableau workflows to assist with efficient decision-making.
5. Scite.ai
This is a well-rounded citation tool that provides context to citations by clearly stating whether an academic paper supports or contrasts the cited claim. This helps researchers save time having to read lengthy papers and quickly determine whether the citation is relevant to their research.
6. Consensus AI
This tool annotates insights about research papers using AI. It produces “study snapshots” to condense lengthy research papers by mentioning study aims, variables, and findings. The platform also provides credible responses backed by academic papers to queries, by presenting research papers that both support and contrast the query, allowing users to browse through objective responses.
Limitations of AI Tools
While AI tools are effective in streamlining research processes, they raise ethical concerns and can impact research integrity if misused. AI raises ethical issues including data privacy, algorithm bias and misuse of AI. However, due to the increasing usage of AI tools, policies are developing quickly to ensure the technology is adequately regulated.
Tips for Using AI Tools in Research
Follow these recommendations to ensure ethical AI usage:
- Always fact-check content generated by AI tools
- Do not write academic articles using AI tools. Instead, use these tools to edit and structure original research content.
- Do not use AI tools to generate references, instead use it to manage and store the references.
- Use AI tools that produce accurate results.
- Incorporate your own voice and style of written content as much as possible.
In conclusion, AI is a resourceful innovation in academic research when used ethically. The correct and responsible usage of AI can create immeasurable efficiency in research processes, and aid in citation management, resource annotation, data visualization, literature discoverability and summarisation.
Discover a comprehensive suite of AI-driven tools like summarisation, keyphrase highlighting and ZAIA - AI assistant for research on Zendy now.
Speed Up Your Research With “Insights”
'Insights', a brand-new feature designed to make your research experience faster, simpler, and more accessible. Insights gives you short, clear summaries of research papers, pulling out the most important information so you can understand the main points in just a few lines. Instead of reading through pages of dense content, you’ll get a quick overview that helps you decide if the paper is worth exploring further. Here’s how Insights can help: Save time by getting to the heart of a paper faster. Understand complex topics without feeling stuck. Focus on what matters and decide quickly what’s relevant to you. Why We Created Insights? We’ve heard from many of you that keeping up with research can feel like a never-ending task. There’s so much to read, and it’s hard to know where to start. That’s where 'Insights' comes in, to help you make the most of your time exploring the right research paper you are looking for. How Does It Work? Insights uses our AI to scan through a paper and extract key points. It focuses on sections like the introduction, methodology, results, and conclusion, so you can get a clear sense of what the paper is about. You don’t have to worry about missing anything important; it’s all laid out in a simple, easy-to-digest format. Head over to Zendy, search for what you are looking for, and see how Insights can give you a clearer overview in seconds, Check out Insights now! .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
Responsible AI In Research And Why It Matters
Artificial Intelligence (AI) is changing how we live, work, and learn. However, as AI continues to evolve, it is important to ensure it is developed and used responsibly. In this blog, we’ll explore what responsible AI means, why it is essential, and how tools like ZAIA, Zendy's AI assistant for researchers, implement these principles in the academic sector. What Is Responsible AI? Responsible AI, also known as ethical AI refers to building and using AI tools guided by key principles: Fairness Reliability Safety Privacy and Security Inclusiveness Transparency Accountability AI vs Responsible AI: Why Does Responsible AI Matter? Keep in mind that AI is not a human being. This means it lacks the ability to comprehend ethical standards or a sense of responsibility in the same way humans do. Therefore, ensuring these concepts are embedded in the development team before creating the tool is more important than building the tool itself. In 2016, Microsoft launched a Twitter chatbot called "Tay", a chatbot designed to entertain 18- to 24-year-olds in the US to explore the conversational capabilities of AI. Within just 16 hours, the tool's responses turned toxic, racist, and offensive due to being fed harmful and inappropriate content by some Twitter users. This led to the immediate shutdown of the project, followed by an official apology from the development team. In such cases, "Tay" lacked ethical guidelines to help it differentiate harmful content from appropriate content. For this reason, it is crucial to train AI tools on clear principles and ethical frameworks that enable them to produce more responsible outputs.The development process should also include designing robust monitoring systems to continuously review and update the databases' training, ensuring they remain free of harmful content. Overall, the more responsible the custodian is, the better the child’s behaviour will be. The Challenges And The Benefits of Responsible AI Responsible AI is not a "nice-to-have" feature, it’s a foundational set of principles that every AI-based tool must implement. Here's why: Fairness: By addressing biases, responsible AI ensures every output is relevant and fair for all society’s values. Trust: Transparency in how AI works builds trust among users. Accountability: Developers and organisations adhere to high standards, continuously improving AI tools and holding themselves accountable for their outcomes. This ensures that competition centers on what benefits communities rather than simply what generates more revenue. Implementing responsible AI comes with its share of challenges: Biased Data: AI systems often learn from historical data, which may carry biases. This can lead to skewed outcomes, like underrepresenting certain research areas or groups. Awareness Gaps: Not all developers and users understand the ethical implications of AI, making education and training critical. Time Constraints: AI tools are sometimes developed rapidly, bypassing essential ethical reviews, which increases the risk of errors. Responsible AI and ZAIA ZAIA, Zendy’s AI-powered assistant for researchers, is built with a responsible AI framework in mind. Our AI incorporates the six principles of responsible AI, fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, to meet the needs of students, researchers, and professionals in academia. Here’s how ZAIA addresses these challenges: Fairness: ZAIA ensures balanced and unbiased recommendations, analysing academic resources from diverse disciplines and publishers. Reliability and Safety: ZAIA’s trained model is rigorously tested to provide accurate and dependable insights, minimising errors in output. Transparency: ZAIA’s functionality is clear and user-friendly, helping researchers understand and trust its outcomes. Accountability: Regular updates improve ZAIA’s features, addressing user feedback and adapting to evolving academic needs. Conclusion Responsible AI is the foundation for building ethical and fair systems that benefit everyone. ZAIA is Zendy’s commitment to this principle, encouraging users to explore research responsibly and effectively. Whether you’re a student, researcher, or professional, ZAIA provides a reliable and ethical tool to enhance your academic journey. Discover ZAIA today. Together, let’s build a future where AI serves as a trusted partner in education and beyond. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
AI is Transforming Academic Research and Publishing – A Conversation with Kamran Kardan, CEO of Zendy
AI's real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. Sabine Louët, CEO of SciencePOD sat down with Kamran Kardan, CEO of Zendy to discuss how technology, particularly AI, is reshaping the way researchers and independent scholars access critical information and how research is published. Removing Barriers in Academic Research When asked by Sabine Louët “what drove the creation of Zendy?” Kamran Kardan’s response was clear and purposeful: “Zendy was created to remove the barriers that restrict access to academic research”.He highlights the significant gap that exists for those outside privileged institutions, who often face prohibitive costs or limitations when trying to access essential research. Zendy, he says, aims to make academic content not only affordable but also widely accessible to researchers, students, and professionals globally.Accessing scientific literature remains a privilege reserved for those with institutional affiliations, leaving independent researchers or those from less-resourced regions at a disadvantage. As Kardan puts it, “Zendy is committed to levelling the playing field”, it offers a legitimate alternative to illicit means of accessing research. AI’s Role in Enhancing Research Accessibility AI has become a buzzword, but Kardan stresses the importance of AI in Zendy’s strategy, describing AI as an enabler rather than the focal point. Zendy, he explains, uses AI to enhance user experience by making vast amounts of data more navigable. One of the platform’s key AI-driven features is its summarisation tool, which allows users to quickly digest complex academic papers. With this tool, users can identify relevant content faster and focus their research efforts more effectively. A forthcoming feature called ‘findings’, will use AI to group related articles together, offering a comparative perspective on topics and highlighting differing viewpoints. This tool is designed to empower researchers to explore a topic from multiple angles without having to sift through unrelated material. Safeguarding Research Integrity in the Age of AI Another point of discussion between Sabine Louët and Kardan was the issue of integrity while also leveraging AI. Kardan acknowledges that this is critically important and explains that Zendy is built on principles of transparency and respect for intellectual property. Their AI tools do not merely extract data but give due credit to authors and publishers. In addition, the platform’s revenue-sharing model ensures that content creators benefit from the usage of their work, fostering a more sustainable and fair ecosystem for academic publishing. Kardan also addresses the issue of AI-generated inaccuracies, commonly referred to as “hallucinations”. He emphasises that Zendy's AI is structured to avoid these risks. If the AI does not have sufficient data to provide an answer, it refrains from making assumptions, thus maintaining a high standard of accuracy. AI: Not Just Speed, but Deeper Learning In Kardan’s view, AI's real potential lies not just in speeding up processes but also in helping users engage more deeply with academic content. The tools developed by Zendy are designed to simplify complex materials, making them more approachable for users across various disciplines, without compromising on the depth of information. Louët agrees and notes that these features, particularly AI-driven comparison and summarising tools, align with the needs of modern researchers who require both efficiency and reliability in handling academic content. Looking Ahead: The Future of AI in Research What does the future look like? Kardan foresees more AI advancements that will continue to transform research access, making it more affordable, transparent and equitable. The focus is not just on technology for technology’s sake but on providing meaningful solutions that directly address the challenges of the academic community. “AI’s role in academic publishing is still evolving”, says Kardan, “and Zendy is committed to using AI responsibly to enhance access to knowledge, not to replace human expertise”. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }