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Generative AI, especially large language models (LLMs), present exciting and unprecedented opportunities and complex challenges for academic research and scholarship.<\/p>\n
As the different versions of LLMs (such as ChatGPT, Gemini, Claude, Perplexity.ai and Grok) continue to proliferate, academic research is beginning to undergo a significant transformation.<\/p>\n
Students, researchers and instructors in higher education\u00a0need AI literacy knowledge, competencies and skills<\/a>\u00a0to address these challenges and risks.<\/p>\n In a time of rapid change, students and academics are advised to look to their institutions, programs and units for discipline-specific policy or guidelines regulating the use of AI.<\/p>\n A recent study\u00a0led by a data science researcher<\/a>\u00a0found that at least 13.5 per cent\u00a0of biomedical abstracts last year showed<\/a>\u00a0signs of AI-generated text.<\/p>\n Large language models can\u00a0now support nearly every stage of the research process<\/a>, although caution and human oversight are always needed to judge when use is appropriate, ethical or warranted \u2014 and\u00a0to account for questions of quality control and accuracy<\/a>. LLMs can:<\/p>\n However, there are significant concerns and challenges surrounding the appropriate, ethical, responsible and effective use of generative AI tools in the conduct of research, writing and research dissemination. These include:<\/p>\n There are\u00a0two categories of emerging LLM-enhanced tools<\/a>\u00a0that support academic research:<\/p>\n 1. AI research assistants:<\/strong>\u00a0The number of AI research assistants that support different aspects and steps of the research process is growing at an exponential rate. These technologies have the potential to enhance and extend traditional research methods in academic work. Examples include AI assistants that support:<\/p>\n 2. \u2018Deep research\u2019 AI agents<\/strong>: The field of artificial intelligence is advancing quickly with the rise\u00a0of \u201cdeep research\u201d<\/a>\u00a0AI agents. These next-generation agents combine LLMs,\u00a0retrieval-augmented generation<\/a>\u00a0and sophisticated reasoning frameworks to conduct in-depth, multi-step analyses.<\/p>\n Research<\/a>\u00a0is currently being conducted to evaluate the quality and effectiveness of deep research tools.\u00a0New evaluation criteria<\/a>\u00a0are being developed to assess their performance and quality.<\/p>\n Criteria include elements such as cost, speed, editing ease and overall user experience \u2014 as well as\u00a0citation and writing quality, and how these deep research tools adhere to prompts<\/a>.<\/p>\n The purpose of deep research tools is to meticulously extract, analyze and synthesize scholarly information, empirical data and diverse perspectives from a wide array of online and social media sources. The output is a detailed report, complete with citations, offering in-depth insights into complex topics.<\/p>\n In just a short span of four months (December 2024 to February 2025), several companies (like Google Gemini, Perplexity.ai and ChatGPT) introduced their \u201cdeep research\u201d platforms.<\/p>\n The Allen Institute for Artificial Intelligence, a\u00a0non-profit AI research institute based in Seattle<\/a>, is experimenting with a new open access research tool called Ai2 ScholarQA that helps\u00a0researchers conduct literature reviews<\/a>\u00a0more efficiently by providing more in-depth answers.<\/p>\n Several guidelines have been developed to encourage the responsible and ethical use of generative AI in research and writing. Examples include:<\/p>\n LLMs are also powerful tools to support interdisciplinary research. Recent emerging research (yet to be peer reviewed) on the\u00a0effectiveness of LLMs for research<\/a>\u00a0suggests they have great potential in areas such as biological sciences, chemical sciences, engineering, environmental as well as social sciences. It also suggests LLMs can help eliminate disciplinary silos by bringing together data and methods from different fields and automating data collection and generation to create interdisciplinary datasets.<\/p>\n Helping to analyze and summarize large volumes of research across various disciplines can aid interdisciplinary collaboration. \u201cExpert finder\u201d AI-powered platforms can analyze researcher profiles and publication networks to map expertise, identify potential collaborators across fields and reveal unexpected interdisciplinary connections.<\/p>\n This emerging knowledge suggests these models will be able to help researchers drive breakthroughs by combining insights from diverse fields \u2014 like epidemiology and physics, climate science and economics or social science and climate data \u2014 to address complex problems.<\/p>\n Canadian universities and research partnerships are providing AI literacy education to people in universities\u00a0and beyond<\/a>.<\/p>\n The\u00a0Alberta Machine Intelligence Institute<\/a>\u00a0offers K-12 AI literacy programming and\u00a0other resources<\/a>. The institute is a\u00a0not-for profit<\/a>\u00a0organization and part of\u00a0Canada\u2019s Pan-Canadian Artificial Intelligence Strategy<\/a>.<\/p>\n Many\u00a0universities are<\/a>\u00a0offering\u00a0AI literacy educational opportunities<\/a>\u00a0that focus specifically on the use of generative AI tools in\u00a0assisting research activities<\/a>.<\/p>\n Collaborative university work is also happening. For example, as vice dean of the Faculty of Graduate & Postdoctoral Studies at the University of Alberta (and an information science professor), I have worked with deans from the University of Manitoba, the University of Winnipeg and Vancouver Island University to develop guidelines and recommendations\u00a0around generative AI and graduate and postdoctoral research and supervision<\/a>.<\/p>\n Considering the growing power and capabilities of large language models, there is an urgent need to develop AI literacy training tailored for academic researchers.<\/p>\n This training should focus on both the potential and the limitations of these tools in the different stages of the research process and writing.<\/p>\nResearcher use of AI<\/strong><\/h3>\n
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AI research assistants, \u2018deep research\u2019 AI agents<\/strong><\/h3>\n
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Emerging guidelines<\/strong><\/h3>\n
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LLMs support interdisciplinary research<\/strong><\/h3>\n
Research-focused AI literacy<\/strong><\/h3>\n