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.
Top 5 AI Ethical Issues that Can Impact Your Research Integrity
In a recent blog, we discussed responsible AI in research and why it matters. Now, we’ll discuss some AI ethical issues and what you should not be doing with AI in your research journey. This blog looks at common mistakes people make with AI in research, explains why they happen, and offers practical tips to avoid them. 1. Trusting AI Outputs Without Checking Them One big AI ethical issue is trusting everything AI tools generate without taking the time to verify it. AI models like ChatGPT can produce convincing answers, but they’re not always accurate. In research, this can lead to spreading incorrect information or drawing the wrong conclusions. Why It Happens: AI systems learn from existing data, which might include errors or biases. As a result, they can unintentionally repeat those issues. What You Can Do: Treat AI-generated content as a helpful draft, not the final word. Always double-check the information with reliable sources. 2. Using AI for Tasks That Require Human Judgment Relying on AI for decisions that need a human touch, like reviewing academic papers, is risky. These tasks often require context and empathy, which AI doesn’t have. Why It Happens: AI seems efficient, but it doesn’t understand the subtleties of human situations, leading to potential AI ethical issues in judgment and fairness. What You Can Do: Let AI assist with organizing or summarizing information, but make sure a person is involved in decisions that affect others. 3. Not Giving Credit to AI Tools Even when AI is used responsibly, failing to acknowledge its role can mislead readers about the originality of your work. Why It Happens: People might not think of AI as a source that needs to be cited, overlooking important AI ethical issues related to transparency and attribution. What You Can Do: Treat AI tools like any other resource. Check your institution’s or publisher’s guidelines for how to cite them properly. 4. Over-Reliance on AI for Creative Thinking AI can handle repetitive tasks, but depending on it too much can stifle human creativity. Research often involves brainstorming new ideas, which AI can’t do as well as people. Why It Happens: AI makes routine tasks more manageable, so letting it take over more complex ones is tempting. What You Can Do: Use AI to free up critical thinking and creative problem-solving time. Let it handle the busy work while you focus on the bigger picture to avoid these AI ethical issues. 5. Giving AI Access to Sensitive Data Allowing AI tools to access personal information without proper permission can pose serious security risks. Why It Happens: Some AI tools require access to data to function effectively, but their security measures might not be sufficient leading to potential AI ethical issues. What You Can Do: Limit the data AI tools can access. Use platforms with strong security features and comply with data protection regulations. Final Thoughts AI can be a valuable tool for researchers, but it’s not without its challenges. Many of these challenges stem from AI ethical issues that arise when AI is misused or misunderstood. By understanding these common mistakes and taking steps to address them, you can use AI responsibly and effectively. The key is to see AI as an assistant that complements human effort, not a replacement. .wp-block-image img { max-width: 80% !important; margin-left: auto !important; margin-right: auto !important; }
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: 80% !important; margin-left: auto !important; margin-right: auto !important; }