AI in Education for Students and Researchers: 2025 Trends and Statistics
Mar 10, 2025 |
20 Mins ReadWith millions of scholarly content published every year, in addition to the integration of Artificial Intelligence (AI) into various fields in the past few years, including the educational sector, AI in education has had, and still, a major impact on simplifying research projects, accelerating discoveries, and optimising learning experiences. Giving students and researchers the chance to work efficiently and effectively more than ever. So, how much time do researchers spend on repetitive tasks that AI can simplify?
On the other side of the story, The global market for AI in education was estimated to be worth $2.5 billion in 2022 and is expected to more than double by 2025, according to the most recent data from AIPRM. However, how exactly are researchers using AI, and what are the challenges they face?
This means that Artificial Intelligence (AI) is changing every aspect of modern life, including education and research. It’s reshaping how students learn, how researchers solve problems, and how educators teach. According to our latest survey, 73.6% use AI in education, 51% use it for literature review and 46.3% of students and researchers are using AI in education for writing and editing, showing just how quickly these tools are being adopted.
AI in education helps by sorting through the tremendous amount of scientific information, analysing large datasets of structured or unstructured data, and spotting connections that might take months to find manually. It also takes care of time-consuming tasks like summarising studies and formatting citations, so researchers can focus on bigger questions. With so much information and so little time, AI isn’t just helpful, it’s becoming a necessity.
Zendy surveyed more than 1,500 students and researchers to understand how they use AI tools. The study shows how people incorporate AI into their academic work, the benefits they find most useful, and the challenges they face. The findings give a clearer picture of AI’s role in academic work and its impact on productivity.
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Demographics of AI Users in Research
Zendy’s study provides insights into who is using AI in education. Most respondents are young learners, early in their academic journey, which gives us a sense of tools and support they’re looking for.
- 60.1% of respondents are female, 36% are male while the rest prefer not to disclose.
- 67.6% are between 18-24 years old, reflecting early-career researchers and students.
- 45% are undergraduate students, 37.2% are high school students, exploring AI tools for learning and research
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Habits and AI in Education Adoption
Students and researchers are highly engaged in academic literature and are shifting toward AI-driven tools for efficiency. Zendy's survey reveals how dedicated students and researchers are to expanding their knowledge and staying current in their fields. Regularly engaging with academic literature is a key part of their studies and professional growth, reflecting the effort they invest in learning.
The survey also shows a clear preference for online databases, highlighting a growing reliance on digital tools for easy access to research materials. This shift points to a broader move toward more convenient and centralised platforms, supported by the use of responsible AI and other technologies in academic work. These findings underline the importance of user-friendly, well-resourced tools that meet the changing needs of today’s learners and professionals.
- 71.5% read research papers daily or several times a week, indicating high engagement
- 49.3% of respondents spend an average of 4.5 hours each day engaging with research papers
- 50% prefer online databases for accessing research articles, reflecting the growing digitisation of academic research.
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How is AI Being Used in Research?
The study highlights how AI in education is transforming scholarly content practices, with more researchers using it in their daily routines. One of the most common uses is for literature reviews, traditionally a time-consuming task that AI is helping to make faster and more manageable. The findings show a willingness to embrace AI and point to key areas where it can have an even greater impact, especially in literature reviews, writing, and editing.
- 73.6% have used or are exploring AI tools for research.
- 51% use AI for literature reviews.
- 46.3% for writing and editing, highlighting key areas for AI development
These findings indicate a widespread acceptance of AI in education and a growing demand for AI-powered tools.
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Accessibility and Device Preferences
With research becoming increasingly digital, the choice of devices used for academic work is evolving, most users still rely on desktops for research, and more researchers are turning to mobile devices. This shift highlights the need to focus on making mobile access smoother and more user-friendly, all while ensuring that the desktop experience remains just as reliable and effective.
- 57.9% prefer desktops valuing stability and a larger screen.
- 34.8% prefer mobile devices for reading research, emphasising convenience and portability.
- 7.3% prefer tablets.
This trend highlights the need for mobile-friendly AI-powered research platforms while maintaining robust desktop experiences.
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Impact of AI in Education
Finally, researchers were asked about their perception of AI’s effectiveness in academic work. Over half of the respondents shared they consider AI tools to be highly effective, particularly for simplifying complex tasks. Many highlighted how impactful these tools are in saving hours and making the research journey more efficient, showing just how valuable AI has become in the academic sector.
- 39.6% find it very effective
- 33.4% find it effective
- 21.8% are neutral
- 3.7% think it’s ineffective
- 1.5% think it’s very ineffective
The overwhelming majority see AI as a valuable tool, streamlining research and saving time.
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Ethical Concerns & AI Limitations
AI offers many benefits, but there are still some ethical issues to work through:
- Bias and Accuracy – AI in education can reflect biases in the data it’s trained on, which can lead to misleading results.
- Ethical Concerns – Researchers need to make sure AI-generated content meets academic integrity standards.
- Cost and Access – Some AI tools are expensive, making them harder to access for students and researchers with limited resources.
To address these AI ethical issues, educators, researchers, and technology providers need to work together to ensure AI is used responsibly in academia.
The Future of AI in Education
AI in education is evolving rapidly, and the trends from Zendy’s survey suggest where it’s headed next.
With 73.6% of respondents already using AI in education, its role will only expand. One of the biggest areas of growth is predictive analysis, where AI is expected to help researchers spot patterns in massive datasets—an extension of how AI is already streamlining literature reviews and data organisation today.
Collaboration is another key area. As AI in education tools become more sophisticated, they will help researchers across different disciplines and countries work together more efficiently, reducing language barriers and improving access to global knowledge.
As AI technology advances, its impact on academic research will deepen, offering both opportunities and challenges. The focus now is on ensuring these tools remain accessible, ethical, and aligned with researchers’ real needs.
Finally, AI in education is set to transform experimentation and simulations. Innovations in AI-driven modelling, combined with augmented and virtual reality, could make complex experiments more interactive, accurate, and scalable.
Conclusion
The survey offers a closer look at how AI is undeniably shaping the future of education, specially scientific research, helping students and researchers work more efficiently. From automating literature reviews to improving writing and editing, it’s clear that AI in education is becoming an indispensable part of academic workflows. However, challenges like affordability and accessibility remain key areas to address, ensuring that AI-powered research tools remain accessible and fair for everyone.
At Zendy, we are committed to developing AI-driven tools that cater to the real needs of students, researchers, and professionals.
Download the full report to learn about the methodology behind our findings, explore deeper insights into AI in education, and discover how it’s shaping the academic world.
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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; }
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; }
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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