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This group activity is conceived of as part of a larger session about finding academic books and articles. It is an opportunity to address emerging AI tools like Elicit and the various chatbots. The jigsaw design assigns each group one tool to evaluate and present to the rest of the class.

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Includes links to learning modules developed by the UA libraries.

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Ourapproach to information literacyallows us to teach students the "why" instead of only the "how,"and it helps them think of themselves as creators and participants in scholarly communications. It also addresses competencies that students need to develop in order to become skillful researchers.

Learn more about our approach and our instructional support.

We can work with you to embed information literacy (including AI literacy) into your courses.
Set up an instruction consultation with your liaison librarian.

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LibGuides: AI Literacy in the Age of ChatGPT: How UA libraries can help

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LibGuides: AI Literacy in the Age of ChatGPT: How UA libraries can help

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Using Generative AI Responsibly for Brainstorming and Refining a Research Question

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Using Generative AI Responsibly for Brainstorming and Refining a Research Question

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Using Generative AI Responsibly for Brainstorming and Refining a Research Question

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This activity involves using Generative AI platforms, such as ChatGPT, Claude, Gemini, and Pi.ai, to assist in brainstorming and refining research questions. Students will experiment with different prompts, and engage in a conversational approach with the AI to get the best, most useful results.

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A Curious Person's Guide to Artificial Intelligence

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A Curious Person's Guide to Artificial Intelligence

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Artificial intelligence is a branch of computer science that aims to build smart machines capable of performing tasks that typically require human intelligence.

Artificial intelligence is everywhere. And the recent explosion of new AI technologies and tools has introduced many new terms that you need to know to understand it.

The technology fuels virtual assistants, like Apple's Siri, helps physicians to spot cancer in MRIs and allows your phone to recognize your face.

Tools that generate content have reignited the field. Chatbots, like ChatGPT and Bard, write software code and chapter books. Voice tools can manipulate celebrities' speech. Image generators can make hyper-realistic photos given just a bit of text.

This groundbreaking technology has the potential to revolutionize entire industries, but even experts have trouble explaining how some tools work. And tech leaders disagree on whether these advances will bring a utopian future or a dangerous new reality, where truth is indecipherable from fiction.
Artificial Intelligence
Artificial intelligence is an umbrella term for a vast array of technology. There is no single definition, and even researchers disagree. Generally, AI is a field of computer science that focuses on creating and training machines to perform intelligent tasks, "something that, if a person was doing it, we would call it intelligence," said Larry Birnbaum, a professor of computer science at Northwestern University.

For decades, AI has largely been used for analysis, allowing people to spot patterns and make predictions by assessing huge sets of data.

But advancements in the field have led to a boom in generative AI, a form of artificial intelligence that can make things. The technology can create words, sounds, images and video, sometimes at a level of sophistication that mimics human creativity. It backs chatbots like ChatGPT and image generators like DALL-E.

Although this technology can't "think" like humans do, it can sometimes create work of a similar quality. AI-powered image generators have made photos that tricked art judges into thinking they were human-made, and voice generating software has preserved voices of people suffering from degenerative diseases such as ALS.

Chatbots backed by generative AI have dazzled users by carrying on eerily lifelike conversations, an early dream of the field as envisioned by Alan Turing. In 1950, he developed the "Turing test," which judged the success of an AI machine by how well it could fool users into believing it was human.

Turing never gave much credence to the idea that a computer could really "think," he called that question "too meaningless to deserve discussion."
How AI Learns
Artificial intelligence software is nothing without data.

The tools develop intelligence through machine learning, a process that allows computers to "learn" on their own, without requiring a programmer to tell them each step. Feed a computer massive amounts of data, and it eventually can recognize patterns and predict outcomes.

Key to this process are neural networks, mathematical systems that act like a computerized brain, helping the technology find connections in data. They're modeled after the human brain, with layers of artificial "neurons" that communicate information to one another. Even experts don't necessarily understand all the intricacies of how neural networks work.

Large language models, or LLMs,are a type of neural network that learns to write and converse with users; they back all of the chatbots that have swooped onto the scene in recent months. They learn to "speak" by hoovering up massive amounts of text, often websites scraped from the internet, and finding statistical relationships between words. When these systems pattern-match, it can lead to feats of creativity. A chatbot can create song lyrics closely matching Jay-Z's style because it's absorbed the patterns of his entire discography. But LLMs don't have awareness of the meanings behind words.

Parameters, which are numerical points across a large language model's training data, dictate how proficient it is at its tasks, such as predicting the next word in a sentence.

In the future, some researchers say, the technology will approachartificial general intelligence, or AGI, a point at which it matches or exceeds the intelligence of humans. The idea is core to the mission of some artificial intelligence labs, like OpenAI, which lists achieving AGI as its goal in its founding documents. Other experts contest that AI is anywhere close to achieving that kind of sophistication, with some critics contending that it's a marketing term.
How Do We Interact With AI
The most common way people experience artificial intelligence is throughchatbots, which work like an advanced form of instant messenger, answering questions and formulating tasks from prompts.

These bots are trained on troves of internet data, including Reddit conversations and digital books. Chatbots are incredibly adept at finding patterns and imitating speech, but they don't interpret meanings, experts say. "It's a super, super high-fidelity version of autocomplete," Birnbaum said of the LLMs that power the chatbots.

Since it debuted in November, ChatGPThas stunned users with its ability to produce fluid language, generate complete novels, computer code, TV episodes and songs. GPT stands for "generative pre-trained transformer." "Generative," meaning that it uses AI to create things. "Pre-trained," means that it has already been trained on a large amount of data. And "transformer" is a powerful type of neural network that can process language.

Created by the San Francisco start-upOpenAI, ChatGPT has led to a rush of companies releasing their own chatbots. Microsoft's chatbot,Bing, uses the same underlying technology as ChatGPT. And Google released a chatbot, Bard, based on the company's LaMDA model.

Some people think chatbots will alter how people find and consume information on the internet. Instead of entering a term into a search engine, like Google, and sifting through various links, people may end up asking a chatbot a question and getting a confident answer back. (Though sometimes these answers are false)
Taming AI
The boom in generative artificial intelligence brings exciting possibilities, but also concerns that the cutting-edge technology might cause harm.

Chatbots can sometimes make up sources or confidently spread misinformation. In one instance, ChatGPT invented a sexual harassment scandal against a college law professor. It can also churn out conspiracy theories and racist answers. Sometimes it expresses biases in its work. In one experiment, robots identified Black men when asked to find a "criminal" and marked all "homemakers" as women.

AI ethicists and researchers have long been concerned that, because chatbots draw on massive amounts of human speech, using data from Twitter to Wikipedia, they absorb our problems and biases. Companies have tried to put semantic guardrails in place to limit what chatbots can say, but that doesn't always work.

Sometimes artificial intelligence produces information that sounds plausible but is irrelevant, nonsensical or entirely false. These odd detours are called hallucinations.Other people have become so immersed in chatbots they falsely believe the software is sentient, meaning it can think, feel, and act outside of human control. Experts say it can't, at least not yet, but it can speak in a fluid way so that it mimics something alive.

Another worry isdeepfakes,which are synthetically generated photos, audio or video that are fake but look real. The same technology that can produce awesome images could be deputized to fake wars, make celebrities say things they didn't actually say or cause mass confusion or harm.

Companies test their artificial intelligence models for vulnerabilities, rooting out biases and weaknesses by simulating flaws in a process called red teaming.

Despite attempts to tame the technology, the innovation and sophistication of generative AI causes some to worry.

"When things talk to us like humans, we pick up a little suspension of disbelief," said Mark Riedl, professor of computing at Georgia Tech and an expert on machine learning. "We kind of assume that these things are trying to be faithful to us, and when they come across as authoritative, we can find it hard to be skeptical."
Companies In The Chatbot Race

OpenAI: The San Francisco-based artificial intelligence research lab launched as a nonprofit to build "artificial general intelligence" outside of Big Tech's control. Since then, it's transformed into a major corporate player, creating image generator DALL-E and chatbot ChatGPT. It is now for-profit and has partnered with companies including Microsoft and Salesforce.

Google: The tech giant, long a leader in AI including via search, launched chatbot Bard after competitors' offerings went viral. It is known for its LaMDA technology, a system for building chatbots based on large language models.

Microsoft: The software company invested billions of dollars in OpenAI and teamed up to create a Bing chatbot, developed on GPT-4 technology. But there have been missteps, including when the chatbot went rogue, told reporters it has feelings and called itself Sydney, forcing the tech giant to reel it back in some ways.

Meta: Even before ChatGPT, Facebook's parent company released a chatbot called Blenderbot, but it failed to gain traction. Its chief artificial intelligence scientist later called the bot "boring" because it was "made safe."

IBM: IBM was an early leader in artificial intelligence close to the current chatbot trends, most notably with its robot Watson, which captivated audiences on "Jeopardy!"

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A Curious Person's Guide To Artificial Intelligence.

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Artificial intelligence is everywhere. And the recent explosion of new AI technologies and tools has introduced many new terms that you need to know to understand it. The technology fuels virtual...

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What I Mean When I Say Critical AI Literacy

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What I Mean When I Say Critical AI Literacy

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Where does ChatGPT fit into the Framework for Information Literacy?

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Where does ChatGPT fit into the Framework for Information Literacy?

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07_Perspectives

Perspectives on the Framework

The possibilities and problems of AI in library instruction

Amy B. James is online librarian for education and information literacy at the Baylor University Libraries, email: amy_b_james@baylor.edu. Ellen Hampton Filgo is director of liaison program at the Baylor University Libraries, email: ellen_filgo@baylor.edu.

The above screenshot (figure 1) was what was generated when we asked ChatGPT, the generative AI system that has been the subject of a thousand hot takes about how it's disrupting academia-as-we-know-it, to describe itself for an academic librarian audience. Perhaps it's learning a bit too much from the public relations documents that were a part of the vast amounts of data it was trained on, when it describes itself as "highly relevant," "invaluable," and "accurate." It did not, however, bring up the caveat that greets you when you open up ChatGPT itself: that it "may occasionally generate incorrect information," that it "may occasionally produce harmful instructions or biased content," or that it has "limited knowledge of the world and events after 2021."1 In addition, it doesn't bring up the reddest of academic red flags-that ChatGPT provides an easy way for students to cheat and plagiarize. The Atlantic has claimed that because of ChatGPT and other AI, "the undergraduate essay [which] has been at the center of humanistic pedagogy for generations . . . is about to be disrupted from the ground up."2 A writer at Times Higher Education has suggested that allowing AI to replace a student's creative voice means "abandoning our responsibilities as educators."3

For as many handwringing accounts of how generative AI will destroy academia, there seem to be twice as many researchers, teachers, technologists, and pundits embracing what AI (and specifically ChatGPT) can do for teaching and learning. They suggest using it for overcoming writer's block, generating outlines, creating summaries, generating prompts for discussion, asking for definitions, or generating flawed examples for critique.4 One compelling argument by Christopher Grobe in the Chronicle of Higher Education suggests that what generative AI can help us with is to "provide new starting points for some of the processes we routinely use to think."5 We agree with Grobe's argument that ChatGPT can give us a good starting point from which to work. The text generated by ChatGPT in the screenshot at the start of this article is an overly optimistic and idealized view of itself. We hope that in this article we can add the nuance that it lacks.

Academic librarians serve their students and faculty to help them navigate the research process. Therefore, when a new technological tool blazes through higher education, as ChatGPT has over the last few months, it becomes increasingly important that librarians are aware of the tool and its uses so that they can serve their students and faculty. After decades of the ACRL Information Literacy Competency Standards for Higher Education, the ACRL Framework for Information Literacy for Higher Education was established with a much more flexible route for integration into curricula. The Framework provides librarians and disciplinary faculty with a customizable way to provide information literacy instruction that meets the needs of students and enables them to become participants in the information that they are producing (not just consuming). Because of the Framework's flexible nature, librarians can incorporate new technology, like ChatGPT, more easily into their instruction.

We have found that the idea of ChatGPT (and generative AI more broadly) can be connected to many of the knowledge practices and dispositions from the six frames of the ACRL Framework. In some places, the Framework enables us to embrace ChatGPT as an exciting new tool that adds value to information literacy instruction. In other places, the Framework's discussions of evaluating authority and examining bias shines light on the inherent flaws of ChatGPT. In the next section, we will review each of the frames and discuss how ChatGPT fits into each of those Frames.

Authority is Constructed and Contextual

The Authority is Constructed and Contextual frame states that learners who are growing their information literate abilities "develop awareness of the importance of assessing content with a skeptical stance and with a self-awareness of their own biases and worldview."6 On the opening screen, ChatGPT provides a disclaimer to let users know that the information that it provides may contain biases and that it has a limited knowledge of current events. As the usage of ChatGPT increases it will become even more important that students know how to evaluate whether information that they come across is authoritative within the context of their research. Students need to recognize that bias is everywhere and ChatGPT is getting information that exists out on the open web. Much of the information that it produces derives from text from political organizations, nonprofits, companies, and individuals, and this context shapes the model's output. ChatGPT is built on predictive language modeling, which means it generates natural sounding language, not necessarily factual language. Students should always approach information, from ChatGPT or elsewhere, with skepticism.

Information Creation as a Process

As with any new technology, there will be people who are hesitant adopters. In the early 2000s many librarians were skeptical of students using Wikipedia. Now we realize that we need to be instructing students about the proper use of Wikipedia, rather than banning it. One of the dispositions for the Information Creation as a Process frame states that "learners who are developing their information literate abilities accept the ambiguity surrounding the potential value of information creation expressed in emerging formats or modes."7 To us, this is direct confirmation that ChatGPT has a place in the library instruction classroom, both for its use in instruction and for discussion with students. Librarians should absolutely be talking about it with students, trying it out, and discovering together what those problems and promises are. In particular, students should be made aware of the machine learning process: tools like ChatGPT are trained on large amounts of textual data in an iterative process, whereby it "learns" from this data over time. This process is very similar to how information literate scholars learn, research, create, write, and refine their ideas over time, as presented in this frame.

The frame challenges us that "the dynamic nature of information creation and dissemination requires ongoing attention to understand evolving creation processes."8 A technology columnist for The New York Times advised his readers along the same lines: "today's students will graduate into a world full of generative A.I. programs. They'll need to know their way around these tools-their strengths and weaknesses, their hallmarks and blind spots-in order to work alongside them. To be good citizens, they'll need hands-on experience to understand how this type of A.I. works, what types of bias it contains, and how it can be misused and weaponized."9 It is imperative librarians bring students to that attention about the process of creating information through ChatGPT and similar AI models.

Information has Value

The Information has Value frame indicates that learners who are developing their information literacy skills need to "learn the importance of giving credit to the original ideas of others through proper attribution and citation."10 As more learners use ChatGPT for citation assistance, they will need to be extremely careful to verify accuracy of all citations. As explained above, ChatGPT does not always provide factual information. For example, if you ask ChatGPT to give you a list of scholarly articles on a particular topic, it will list articles with full citations, including DOIs. The articles appear to be related titles from reputable-sounding journals. However, if you Google each of the articles, you will discover that some of them do not exist. ChatGPT will automatically generate nonexistent articles through predictive language modeling, making article titles appear genuine, even if they are not. If you ask it to give you citations for scholarly articles on the topic of higher education and information literacy, it will give you several citations that look very convincing. Here is an example:

If a student were to take this AI response and incorporate those citations into a paper or assignment, they would include some nonexistent articles. Students citing articles they have not read of course is a problem, but students citing fake articles that cannot be read compounds the problem. As students become creators of information themselves, they will need to accurately cite all information that they use, not only for the ethical and moral good, but also for the sake of their own reputation in their field(s). ChatGPT is a minefield when it comes to citation, and we would not recommend students use it for that purpose. However, asking ChatGPT to generate citations like this in an in-class exercise and discussing the results with students can help them to see the minefield for what it is and navigate around it.

Research as Inquiry

One of the knowledge practices for the Research as Inquiry frame states that "learners who are developing their information literate abilities deal with complex research by breaking complex questions into simple ones, limiting the scope of investigations."11 We have found that students will often come to research consultations or library instruction sessions with broad or vague research questions that they often do not know how to simplify or narrow down to research writing sufficiently scoped to a level that they can tackle in five to eight pages. While they might be interested in, let's say, "the problem of poverty" or "the abortion debate," they cannot digest the enormous amount of research in multiple academic disciplines that have attempted to address these types of questions. This is where we believe ChatGPT can help students (and even seasoned researchers) in generating ways to break complex problems down. ChatGPT can help refine research questions, determine search terms, come up with synonyms and related terms or phrases for searching, help decide which databases to search, generate textual concept maps, and even help generate citations. For example, here was the response it gave when we asked it "What search terms should we use for our hypothetical research question, 'why aren't college athletes paid?'"

ChatGPT can also create textual concept maps to help think through various aspects of a research topic. This can be useful for students who need to narrow or refine their topic. Simply asking the AI to help narrow a research topic can be useful as it will give you a variety of ways to explore a topic. For example, we asked it to help us narrow our search on why college athletes aren't paid, and it gave us detailed options in an easy-to-read format:

You can see clearly how ChatGPT can help students push past that inquiry threshold. Often, they don't even know where to start in their search for information, or how to probe the nuances or facets of a large complex question for a scope they can grasp. Instead of typing their entire research question into Google or a database (as we have all seen students do) and having to sift through a mountain of results, they can type it into ChatGPT and ask "How do I start searching? Where could I go from here? What's manageable?" As students are growing in their information literacy abilities, ChatGPT can help scaffold their skills enabling them to accomplish this task more confidently in the future.

One caveat that always bears repeating: ChatGPT has biases. It is trained on a large dataset of material from the internet. It may not produce underrepresented or less well-researched aspects of a topic. Because of this, it is important for students to explore topics holistically, with ChatGPT as one tool in their toolbelt.

Scholarship as Conversation

"Learners who are developing their information literate abilities see themselves as contributors to scholarship rather than only consumers of it."12 This disposition from the Scholarship as Conversation frame, which shows up similarly in Information has Value, means that students are able to see themselves as a part of the scholarly conversation through the ways they critically examine, interact, and synthesize course and research materials, along with how they can contribute their own ideas and research through writing, presenting, and publishing. Whether students are intending to pursue further academic study or not, their voices are valuable in the conversation. This is where we urge caution in the use of ChatGPT, which may undermine the development of their academic voice.

On the extreme end, there will be students who use ChatGPT to generate an essay to turn in, and on the other end, there will be students who use it with great skill to enhance or refine their writing, to find a starting place for scattered thoughts, or to break out of creative blocks. As we teach about how students can join the scholarly conversation with their own voice, we need to emphasize the use of ChatGPT as a supportive resource, along with librarians, and other academic mentors.

Searching as Strategic Exploration

The Searching as Strategic Exploration frame describes searching as "nonlinear and iterative, requiring the evaluation of a range of information sources and the mental flexibility to pursue alternate avenues as new understanding develops."13 This section is a succinct summary of why we as librarians provide information literacy instruction. We work with students to increase their information literacy skills and subsequently an awareness of the scholarly conversation that is taking place around them, something that ChatGPT will not do. ChatGPT will not teach students how to evaluate itself. Librarians are still necessary to encourage learners to evaluate the information sources and tools that may not be familiar to them.

For example, many students enter college having never searched for peer-reviewed, scholarly articles, so that skill may be entirely new to them. Their range of information sources grows when, through our instruction, we help them see the value in using academic databases for finding the information that they need in an effective, efficient manner. In addition, they will most likely have to take another look at their use of Wikipedia. Through information literacy instruction, they will gain a new understanding of how to use it effectively in a higher education context. ChatGPT is a brand-new source for information and our understanding of it is still developing. Our job, as teaching librarians, is not to shun the tool, but to embrace it and guide students toward using the tool responsibly and ethically. As with any new tool, librarians, as well as students, will need to show their flexibility and adapt to these new tools and resources as they become available.

Conclusion

The ACRL Framework for Information Literacy for Higher Education is open-ended enough for us to try new things in our teaching, explore new tools and new ways of helping students to understand the information in the world around them. These are just a few ways that we see how the Framework addresses ChatGPT and other generative AI tools. When we first were looking at this issue, we took a highlighter to the Framework document and made several dozen connections, and we're sure that's not all. In looking at these tools through the lens of the Framework, we can see both the promise and the pitfalls. Ultimately, there must be instruction about these tools: how they were developed, the ethics surrounding their use, and the specificity of the ways they can both help and hurt students. While we are aware that it can seem burdensome to add "one more thing" onto the plate of librarians who provide information literacy instruction, we urge librarians to have discussions with their faculty partners to share the burden of instruction. We are also confident that instruction librarians will create lesson plans that teach about or use ChatGPT in library instruction and will share them with their peers at places like Project CORA, the Framework for Information Literacy Sandbox, and other lesson plan repositories.

Notes

1. "ChatGPT," accessed February 24, 2023, https://chat.openai.com.

2. Stephen Marche, "The College Essay Is Dead," The Atlantic, December 6, 2022, para. 5, https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-writing-college-student-essays/672371/.

3. Adrian J. Wallbank, "ChatGPT and AI Writers: A Threat to Student Agency and Free Will?," Time Higher Education, January 18, 2023, para. 9, https://www.timeshighereducation.com/campus/chatgpt-and-ai-writers-threat-student-agency-and-free-will.

4. Kevin Roose, "Don't Ban ChatGPT in Schools. Teach With It," The New York Times, January 12, 2023, sec. Technology, https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html; Matt Miller, "ChatGPT, Chatbots and Artificial Intelligence in Education," Ditch That Textbook (blog), December 17, 2022, https://ditchthattextbook.com/ai/; Leanne Ramer, "Adapt, Evolve, Elevate: ChatGPT Is Calling for Interdisciplinary Action," Time Higher Education, February 22, 2023, https://www.timeshighereducation.com/campus/adapt-evolve-elevate-chatgpt-calling-interdisciplinary-action.

5. Christopher Grobe, "Why I'm Not Scared of ChatGPT," The Chronicle of Higher Education, January 18, 2023, para. 11, https://www.chronicle.com/article/why-im-not-scared-of-chatgpt.

6. ACRL, "Framework for Information Literacy for Higher Education," Association of College & Research Libraries, February 9, 2015, https://www.ala.org/acrl/standards/ilframework.

7. ACRL, "Framework for Information Literacy for Higher Education."

8. ACRL, "Framework for Information Literacy for Higher Education."

9. Roose, "Don't Ban ChatGPT in Schools. Teach With It."

10. ACRL, "Framework for Information Literacy for Higher Education."

11. ACRL, "Framework for Information Literacy for Higher Education."

12. ACRL, "Framework for Information Literacy for Higher Education."

13. ACRL, "Framework for Information Literacy for Higher Education."

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Where does ChatGPT fit into the Framework for Information Literacy?

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Where does ChatGPT fit into the Framework for Information Literacy? The possibilities and problems of AI in library instruction

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"Research Rabbit is a citation-based mapping tool that focuses on the relationships between research works. It uses visualizations to help researchers find similar papers and other researchers in their field."

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"The best thing is that you don't have to go through all the struggle to figure out what words are required for your research and you can just sit back put a link of paper that you already have and start reading."

- A Happy Rabbit

"Having papers suggested that are both earlier and later than those already on my list, as well as the similar papers function, have helped me to find work I would have needed to pore over reference lists for ages to find otherwise. It's a huge time-saver!"

- An Efficient Rabbit

"It's intuitive and tremendously useful. You save a lot of time identifying the most influential papers in a topic/field. Every scientist should know this!"

- A Delighted Rabbit

"You have done an amazing job with this app-Revolutionary, in my humble opinion."

- A Jubilant Rabbit

"I'm delighted with this excellent app. How easy it is to get related papers, how fast it is to get a similar article. But mostly, the way to cluster papers by their relationship and by timeline is a fantastic feature. You have done something incredible."

- An Analytical Rabbit

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The most powerful discovery app ever built for researchers 🔥

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"Similar to Elicit, Consensus uses LLMs to help researchers find and synthesize answers to research questions, focusing on the scholarly authors' findings and claims in each paper."

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Search - Consensus: AI Search Engine for Research

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Consensus is a search engine that uses AI to find answers in scientific research. Try searching for free.

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"Using LLMs, perplexity is a search engine that provides AI-generated answers (much like ChatGPT) including citations which are linked above the summaries."

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Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

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ChatGPT 4o (Log in | Sign Up to Try) Zhijiang

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ChatGPT 4o (Log in | Sign Up to Try) Zhijiang

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Use ChatGPT to answer questions, write, create images, complete work, and code-all in one place. Get started for free or download the app.

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"Litmaps uses the citation network to discover literature that's easily overlooked with other search methods"

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Litmaps | Your Literature Review Assistant

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Join the 250,000+ researchers, students, and professionals using Litmaps to accelerate their literature review. Find the right papers faster. Get started for free!

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1. What are the strengths of the tool?

2. What are the limitations of the tool?

3. How might it be integrated into library instruction?

You may also wish to evaluate the resource from a student perspective using the AI Tools Report Card

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"Elicit finds papers relevant to your topic by searching through papers and citations and extracting and synthesizing key information."

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Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

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Artificial Intelligence (AI) and Information Literacy

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Artificial Intelligence (AI) and Information Literacy

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Welcome to this online module on artificial intelligence (AI) and information literacy! We've designed this module to help you navigate the rapidly changing world of AI, and to gain important critical thinking toolkits to use when you interact with AI. The more AI becomes integrated into the systems we use every day, the more important it will be for you to be able to understand its uses and limitations.

You can use the links on the left-hand side to learn:

How generative AI works

What AI often gets wrong

Strategies for fact-checking AI

How to cite AI-generated work

More information about AI tools beyond the classroom

Questions?

If you are unsure if a certain use of an AI-based tool is an academic integrity violation, please talk to your course instructor. Every instructor will have different expectations about AI-based tools in their classroom, so it is your responsibility to double check if you are at all unsure to make sure you are not committing an academic integrity violation.

If you have questions about information literacy or citations, please email libues@umd.edu or contact your subject specialist librarian for assistance! We can set up a video chat or assist over email.

If you have questions about how AI tools work, or the ethics of AI, check out the "learn more" links on the bottom of the pages in this module for additional resources and articles to explore.

This short introductory video below will help get you started:

One important note about communication, academic integrity, and your learning journey: University of Maryland's Code of Academic Integrity holds us all to standards of truth and academic honesty and prohibits you from receiving any unauthorized assistance on assignments. Especially since the field of AI is changing so quickly, it is your responsibility to double check the expectations of your instructors on all your assignments to make sure you are not using these AI-based tools in a prohibited way -- every instructor will have different expectations about the use of these tools in their classrooms, and even within different assignments. As you use AI-based tools, we encourage you to make sure you're using them to help you learn better and not to shortcut skills that are important for you to practice on your own.

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Research Guides: Artificial Intelligence (AI) and Information Literacy: Home

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Learn about how AI works and how to spot common errors AI tools tend to make. You'll also learn fact-checking and critical thinking strategies for AI, how to cite AI in an academic paper, and how to learn more in-depth about AI tools and issues.

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1010 - Brainstorming Research Topics Pre-Assignment - King

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Generate topics for your research paper with ChatGPT

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Generate topics for your research paper with ChatGPT

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Generate topics for your research paper with ChatGPT

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Chat with PDF and conduct your literature review faster using SciSpace. Discover 200M+ papers or upload your own PDF, highlight text or ask questions, and extract explanations and summaries.

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Considering RAG when Evaluating Generative AI Tool

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Considering RAG when Evaluating Generative AI Tool

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Not all AI tools are equal for research

Posted on May 8, 2024 in Blog Posts

Everything was easier back in November 2022 when ChatGPT came on the scene. There was one popular large language model (LLM). While we didn't know exactly what to do with it, it was really cool to play with. But a year and a half later, the novelty of AI has worn thin.

An April 2024 ACRL -Choice webinar poll revealed that 68% of university libraries are "still figuring it out" when it comes to AI. With increased pressure to figure it out, academic librarians are presented with an overwhelming number of AI tools, some of which are incredibly expensive. How do you even begin creating a rubric to evaluate these products?

Categorizing AI applications

By now, most of us know there are different flavors of consumer AI applications:

Conversational AI has crept into our lives in the form of digital assistants. While Siri and Alexa are eager to set a timer, and tell us cat jokes, they tend to stick to a basic script. They're like the gonk droid of AI.

Predictive AI is currently used in in business and health care to forecasts trends. Your bank uses a predictive AI to look at transactions and pick out fraudulent charges. Marketers use it to analyze customer buying trends and feed you online ads. Predictive AI analyzes data but does not create new knowledge.

Generative AI is far more powerful and can actually create information, like stories and fake press releases. Scientists train LLM algorithms on massive amounts of data (for example, millions of articles from the New York Times, allegedly). When the user enters into conversation with the LLM, the algorithm creates an answer based not on the rules of English grammar, but the likelihood of what the next word should be according to its training.

Most of the tools developed for libraries are generative AI applications. These are legitimate tools that help with reference management. They are research tools that automate literature review and document analysis. These tools have been created by some of the leading names in library technology: Clarivate, Elsevier, JSTOR, and SirsiDynix, just to name a few. But how do these tools square with the moral panic of students using ChatGPT to cheat on their homework? How can one AI tool be virtuous and the other evil?

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Why you need to know RAG

It all comes down to Retrieval Augmented Generation (RAG). RAG is a framework that creates an application for generative AI LLMs. While it's fun to play around with ChatGPT and Chatbot Arena, they aren't particularly useful for scholars.

The limitations of generative AI are myriad:

They don't provide citations.

They are prone to hallucinations.

There is no way to reproduce results.

There are major issues with copyright and user privacy.

While RAG doesn't solve all these problems, it begins to address the concerns of the academic community. It's the first step toward creating an LLM tool that is actually useful for higher education. At its core, RAG is the difference between generative AI for fun and generative AI as a legitimate research tool.

RAG enhances the LLM neural network by bringing in new information (for example, a specific dataset) and optimizing output so that users know how that output was generated (for example, citations). Say I ask Claude to tell me the social consequences of the Supreme Court cases decided in April 2024. The Claude LLM algorithm should be able to scrape the Supreme Court website for recent cases.. But it turns out the free, non-RAG version of Claude was last trained in August 2023. The chatbot freely admits that it doesn't know anything about what happened last week. This is disappointing, but at least it admits not knowing. After all, other LLMs just make stuff up.

The RAG difference

Perplexity, which uses RAG, adds value to the underlying LLM by helping users refine their prompt. It also allows users to choose a dataset, and provides citations and links for search results. Claude has limitations, but Perplexity provides users with tools that create a much more useful and trustworthy experience.

I asked Perplexity my question about the latest Supreme Court decisions and had a completely different experience. First, it prompted me to get specific about the social consequences I was interested in: civil rights, environment, or immigration. That's a good question, Perplexity.

I can still use Claude as my LLM, but RAG allows me to choose my dataset (Semantic Scholar, Reddit, or full web). It also gives me citations so I can check to make sure the information I've received is not a hallucination.

RAG is pretty powerful stuff and why subscriptions tools such as Scopus AI, Scite, Power Notes, and others add so much value to LLMs. Chatbots may be cool toys, but they are not tools for scholars. RAG technology is still new, but it's getting better with every product release. Understanding RAG and its role in enhancing LLMs is key for librarians who evaluate and make purchasing recommendations for generative AI tools at their institutions. Students and faculty also need to understand RAG when considering which AI product to use for their own research. AI has permanently changed how we do research, and a good RAG is a must in any scholar's toolkit.

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Considering RAG when Evaluating Generative AI Tools - Choice 360

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Retrieval Augmented Generation (RAG) in generative AI is the difference between an okay research tool and a great one. What you need to know.

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Ghost Citations Activity (University of Maryland Libraries)

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Ghost Citations Activity (University of Maryland Libraries)

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Think Outside the Box (Compare results from library databases & AI)

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Think Outside the Box (Compare results from library databases & AI)

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Think Outside the Box (In-class Exercise) | ACRL Framework for Information Literacy Sandbox

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Thinking Outside the Box is an in-class research exercise designed to facilitate students’ evaluation of information found in subscription databases and obtained through generative artificial intelligence tools by providing a series of questions for them to answer. For this exercise, the applicable frames from the Framework for Information Literacy include: “authority is constructed and contextual,” “information creation as process,” and “searching as strategic exploration.”

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AI Misinformation Campaign – The AI Pedagogy Project

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Guide students to misuse AI for a misinformation campaign in order to expose AI’s shortcomings and threats while encouraging them to think critically about persuasion, appeals in composition, and credibility.

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An exercise that teaches ChatGPT' s limitations. This exercise empowers students to verify the information AI generates, fostering responsible AI use.
Ask ChatGPT to generate a list of 4 academic sources on a topic of your choice, and then evaluate the credibility and usability of those sources. Now answer: What is the topic you chose? What 4 citations were generated? (Paste the citations here) THEN complete the following:
1. Are the citations actually real? Does such a journal/website/book exist? State which are not real and which are real. State whether any website used in a real citation where you found it is credible and why.

2. State where those specific real citations are available full-text (check our library databses too). List the names of the places you found them (for example, name of such-and-such webite, name of database , etc...).

3. Check the credentials of the lead author by doing a google search of their name in quotes. Are they trained in the field of the topic? State their credentials and/or academic degrees.

4. Now run their name (in quotemarks) in a library database (like ProQuest or Ebscohost), use a drop down to search for AUTHOR - do they appear? IF YES, What are their other article/s (provide the permalink URLs) about?

5. Now run a search for your same chosen topic in a library database. What are the top four most relevant (provide the four permalink URLs)? Note if they match any of the original four generated.

Bonus 1 point: Talk about paid and unpaid access to this AI tool (look at pricing for different versions on the Chatgpt website) and how YOU think it might affect what you find in any tier of paid/unpaid access.

This assignment tracks to the ACRL Information Literacy framework:
"Information has Value"

CC Attribution License CC-BY

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ACRL Framework for Information Literacy Sandbox

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An exercise that teaches ChatGPT' s limitations. This exercise empowers students to verify the information AI generates, fostering responsible AI use. Ask ChatGPT to generate a list of 4 academic sources on a topic of your choice, and then evaluate the credibility and usability of those sources.

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Hidden Layer: Intellectual Privacy and Generative AI

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Hidden Layer: Intellectual Privacy and Generative AI

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Community of Online Research Assignments

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Hidden Layer: Intellectual Privacy and Generative AI

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The Hidden Layer Workshop introduces key generative AI (genAI) concepts through a privacy lens. Participants probe the possibilities and limitations of genAI while considering implications for intellectual privacy, intellectual property, data sovereignty, and human agency. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms in the neural networks underlying LLMs and their implications for machine bias and AI hallucination.

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Ethical and Responsible Use of Generative AI

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Ethical and Responsible Use of Generative AI

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Photo by
Steve Johnson on Unsplash

Ethical and Responsible Use of Generative AI

Learning Goal: To acquire the knowledge and skills necessary to navigate generative AI tools responsibly and ethically.

Learning Charter Pursuit: Being able to locate, understand, evaluate and use information effectively, ethically, legally
and with cultural appropriateness.

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Research Guides: Academic Integrity Tutorial: Understanding Generative AI

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Research Guides: Academic Integrity Tutorial: Understanding Generative AI

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Brainstorming with AI; Evaluating AI Generated Content

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As AI tools become more prevalent and accessible, it is important to guide users toward responsible and ethical use. This lesson will ask students to use generative AI to think through topic development and research planning. The lesson will introduce AI platforms and ask students to prompt two separate AI platforms to ask them questions about their research topics. Students will evaluate and compare responses, while also adapting and adjusting their own topics based on those responses.

Beth Carpenter, University at Buffalo

Students will be able to perform a basic request from a generative AI tool

Students will be able to evaluate responses generated

Students will be able to examine and edit their research questions and topic ideas

First-year undergraduates, transfer students

This 50-minute lesson will ideally take place in an early college/university course. Many institutions have a required first-year course, and this session would fit well into either a one-shot session or in scaffolded instruction for that course. Independent of a required class for all students, this session would also work well with any class that has a research project as part of the syllabus. The lesson itself is independent of previous library or information literacy instruction, but will reinforce concepts that are taught in both. This lesson would be best taught in a synchronous format, and could work well both in-person or virtual, but all students should have access to a computer or device (such as a tablet). Paramount to this lesson's success will be buy-in from the course instructor, as the use of generative AI is still a hot-button issue.

Equipment Necessary:

Instructor computer and/or projector (if in-person)

Laptops or devices for all students

Access to at least three (3) generative AI platforms - at least one should not require account creation or login. Listed below are some examples:

For students who do not want to make their own accounts, Perplexity will work for the lesson. ChatGPT will also provide answers without needing an account to log in, but there may be a limit to the number of answers it provides before asking for a login.

Instructors may want to share the links to the class ahead of time to allow students to set up accounts beforehand. This will save time during the introduction section of the lesson

If working with an instructor, embedding either a link to the exit ticket, or embed the exit ticket, into the course's LMS

Nearpod or other interactive teaching platform (not required, but helpful)

Dry erase boards/Padlet - shared writing space

If accessibility tools are needed or desired, here are some suggestions:

Speech-to-Text Software: Dragon NaturallySpeaking, or built-in systems such as Windows Speech Recognition or Apple Dictation

Voice Assistants: Siri, Google Assistant, Alexa

Magnification Tools: ZoomText, or built-in magnifier

Instructors may want a few example prompts in mind - both good and bad examples - to demonstrate to students. Questions can, and perhaps should, be tailored to the class visit, but if it's a general instruction session, having some ready-to-go ideas can be helpful. My go-to's have been:

Opening prompt:

Main Exercise prompt:

Can you ask me questions about romantic comedies? / Can you ask me research questions about romantic comedies

I'm writing a research paper on philosophy, can you ask me questions about philosophy?

Can you ask me research questions about climate change

Introduction (5 minutes)

The instructor will welcome students to the class, and prompt them to log onto computers/turn on computers/access devices.

If there are time concerns with having enough time to sign up for AI tools during the class period, the instructor should share links ahead of time, and then prompt students to access the generative AI platforms they have chosen.

If the instructor has not shared links ahead of time, write or share links via projector or whiteboard. If using Nearpod, Mentimeter, etc, this can also be a time to gauge student familiarity with AI using a poll, or other participatory feature.

The instructor should also hand out the worksheet, or share a QR code or link to the worksheet/exit ticket. This could be a paper worksheet, it could be a quiz/survey integrated into your institution's learning management system, or a Google or Microsoft form shared via QR code or easy-to-share URL (TinyURL, bit.ly, etc).

Warm-Up Activity (5-7 minutes)

Ask students to prompt the AI tool of their choice to write a short haiku/short sentence on any topic. Have a few students share out, and discuss how easy or difficult it was to navigate the platform. If they had time to use multiple platforms, there can be discussion about the different responses, and if that tells the user anything about the systems.

Demonstrate Using AI Tools for Topic Development (5-7 minutes)

Explain the type of prompting that will occur in class - asking the AI tool to ask questions, rather than just give information. This process is mirrored when students begin research, both in Google or library resources. Basic searches yield some results, but refining keywords, and learning vocabulary, will help focus those searches.

The instructor will choose two AI tools to demonstrate using them for topic development. It can help to start with a broad prompt (e.g. "Please ask me questions about romantic comedies") and then adjust the question (e.g. "Please ask me research questions about romantic comedies") to show how little adjustments can make a difference to the answers. Do the same with the second platform, and point out any differences you see; ask the students if they notice any differences or similarities to the approach.

Students Use AI Tools On Their Own (10 minutes)

When the demonstration is complete, let the students begin their own work using the AI tools of their choosing. If using a tool such as Nearpod, students can enter their thoughts in a Collaborate Board. If not, then have students make notes on dry-erase boards or on a padlet with their topics. Being able to have a shared writing space to refer to for discussion will help students share out.

Have students also keep track of their prompts on the worksheet, along with any adjustments they made.

Demonstrate refining the prompts to the tools (5 minutes)

Narrowing down to one AI tool, demonstrate adjusting the prompt using previous responses. In the romantic comedy example, if "When Harry Met Sally" or "Nora Ephron" gets mentioned, adjusting the prompt to ask for research questions regarding those specific elements; the same might work for a specific trope or commonality. The instructor can feel free to prepare this adjustment ahead of time, or go on the fly, but note that answers given by AI will change.

Students Work On Their Own (10 minutes)

Give students more time to work and adjust prompts. Have them make note of new concepts or terms they've learned, and if there are any changes to their research topic since starting the exercise.

Discussion (5-7 minutes)

Pulling from the Nearpod/Padlet/dry-erase boards, ask students to share their thoughts - did they have a favorite tool from the ones they used? Did they feel successful with the responses they got? Did they learn anything new about their topic?

Conclusion (2-3 minutes)

End by reminding students about the worksheet/exit ticket. If digital, ensure students submit before leaving.

If using an interactive teaching platform, like Nearpod, reports can be pulled from the session to gain qualitative and anecdotal information from the students. The exit tickets can also act as a way to gain further data from the lesson for iteration and development.

This lesson would work well for students new to doing research, and would work with many disciplines. It could be adjusted for a higher-level course by simply changing the prompts to be more specific to the discipline. A curricular context that may need more adaptation would be when working with international students. AI tools may be extremely helpful if English is not their first language, but may also cause confusion around this process.

This could also be taught in conjunction with an introduction to searching, if the class period is longer, or if there's a workshop to be held. This would show how the skills learned using the AI tools adapt to the research process, aligning with the ACRL Frame "Searching as Strategic Exploration."

The greatest difficulty in developing this lesson was wanting more time - there is so much to discuss with AI, such as ethical use, privacy, etc, and there can be mentions of it, but it's difficult to devote as much time as I'd like to all the areas that are worth talking about.

In addition, the lack of AI platforms that are available for use without account creation is an ongoing struggle. Many students and faculty do not want to create accounts for things where their information is shared with the platform, and what their information will be used for is unclear.

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The Robots Are Helpful: Using Generative AI With Undergraduates

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This lesson asks students to use generative AI to think through topic development and research planning.