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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when they encounter new types of sources. In what follows, we offer recommendations for citing generative AI, defined as a tool that "can analyze or summarize content from a huge set of information, including web pages, books and other writing available on the internet, and use that data to create original new content" (Weed).

You should

cite a generative AI tool whenever you paraphrase, quote, or incorporate into your own work any content (whether text, image, data, or other) that was created by it

acknowledge all functional uses of the tool (like editing your prose or translating words) in a note, your text, or another suitable location

take care to vet the secondary sources it cites (see example 5 below for more details)

See below for specific examples. And keep in mind: the MLA template of core elements is meant to provide flexibility in citation. So if you find a rationale to modify these recommendations in your own citations, we encourage you to do so. We've opened this post up for commenting, so let us know what you think and how you're using and citing generative AI tools!

Using the MLA Template

Author

We do not recommend treating the AI tool as an author. This recommendation follows the policies developed by various publishers, including the MLA's journal PMLA.

Title of Source

Describe what was generated by the AI tool. This may involve including information about the prompt in the Title of Source element if you have not done so in the text.

Title of Container

Use the Title of Container element to name the AI tool (e.g., ChatGPT).

Version

Name the version of the AI tool as specifically as possible. For example, the examples in this post were developed using ChatGPT 3.5, which assigns a specific date to the version, so the Version element shows this version date.

Publisher

Name the company that made the tool.

Date

Give the date the content was generated.

Location

Give the general URL for the tool.

Example 1: Paraphrasing Text

Passage in Source

Paraphrased in Your Prose

While the green light in The Great Gatsby might be said to chiefly symbolize four main things: optimism, the unattainability of the American dream, greed, and covetousness ("Describe the symbolism"), arguably the most important-the one that ties all four themes together-is greed.

Works-Cited-List Entry

"Describe the symbolism of the green light in the book by F. Scott Fitzgerald" prompt. The Great Gatsby ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

Example 2: Quoting Text

Passage in Source

Quoted in Your Prose

When asked to describe the symbolism of the green light in The Great Gatsby, ChatGPT provided a summary about optimism, the unattainability of the American dream, greed, and covetousness. However, when further prompted to cite the source on which that summary was based, it noted that it lacked "the ability to conduct research or cite sources independently" but that it could "provide a list of scholarly sources related to the symbolism of the green light in The Great Gatsby " ("In 200 words").

Works-Cited-List Entry

"In 200 words, describe the symbolism of the green light in The Great Gatsby" follow-up prompt to list sources. ChatGPT, 13 Feb. version, OpenAI, 9 Mar. 2023, chat.openai.com/chat.

While we've provided fairly detailed descriptions of the prompts above, a more general one (e.g., Symbolism of the green light in The Great Gatsby prompt) could be used, since you are describing something that mimics a conversation, which could have various prompts along the way.

Example 3: Citing Creative Visual Works

If you are incorporating an AI-generated image in your work, you will likely need to create a caption for it following the guidelines in of the MLA Handbook. Use a description of the prompt, followed by the AI tool, version, and date created:

Fig. 1. "Pointillist painting of a sheep in a sunny field of blue flowers" prompt, DALL-E, version 2, OpenAI, 8 Mar. 2023, labs.openai.com/.

You can use this same information if you choose to create a works-cited-list entry instead of including the full citation in the caption (see MLA Handbook, sec. 1.7).

Example 4: Quoting Creative Textual Works

If you ask a generative AI tool to create a work, like a poem, how you cite it will depend on whether you assign a title to it. Let's say, for example, you ask ChatGPT to write a villanelle titled "The Sunflower" that-you guessed it!-describes a sunflower and then quote it in your text. Your works-cited-list entry might look like this:

"The Sunflower" villanelle about a sunflower. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

If you did not title the work, incorporate part of or all of the first line into the description of the work in the Title of Source element:

"Upon the shore . . ." Shakespearean sonnet about seeing the ocean. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

For guidance on using descriptions and text from the work itself in the Title of Source element, see the MLA Handbook, 5.28 and 5.29.

Example 5: Citing Secondary Sources Used by an AI Tool

You should also take care to vet the secondary sources cited by a generative AI tool-with the caveat that AI tools do not always cite sources or, when they do, do not always indicate precisely what a given source has contributed. If you cite an AI summary that includes sources and do not go on to consult those sources yourself, we recommend that you acknowledge secondary sources in your work.

For example, let's say that you ask Bing AI to explain the concept of the political unconscious, citing sources, and it provides the following answer:

Let's say that you then decide to quote from the final sentence. You need to click through to the source listed in the note in order to get more information than just a URL for the source. There, you will read the following:

Now, you can treat Oxford Reference as your source since Bing AI was merely a research conduit to the source (see MLA Handbook 5.34 for more information). If for some reason you want to treat a source cited in a generative AI tool as an indirect source-and you know it is, in fact, the source for the information provided by the AI, follow the guidance in section 6.77 of the MLA Handbook.

Note

1. At the time of writing this post, ChatGPT doesn't have a built-in feature to create a unique URL to the conversation. However, an outside tool like the Chrome extension ShareGPT can generate such a link. If you use that type of outside tool, include the unique URL that the tool generates instead of the general URL.

DALL-E allows users to download the AI-generated images they create or generate a publicly-available URL that leads to an image. If you choose to create a shareable link for an image you generate with DALL-E (or other similar AI image generators), include that unique URL that leads to the image instead of the general URL.

Works Cited

MLA Handbook. 9th ed., Modern Language Association of America, 2021. MLA Handbook Plus, 2021, mlahandbookplus.org/.

Weed, Julie. "Can ChatGPT Plan Your Vacation?" The New York Times, 16 Mar. 2023, www.nytimes.com/2023/03/16/travel/chatgpt-artificial-intelligence-travel-vacation.html.

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How do I cite generative AI in MLA style? | MLA Style Center

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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when ...

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Kate's Resource Document - Academic Integrity in the Age of AI

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Kate's Resource Document - Academic Integrity in the Age of AI

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Enhancing Academic Integrity in the Age of AI.docx

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AI for Education (downloadable resources)

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AI for Education (downloadable resources)

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Downloadable Resources - AI for Education

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Congratulations - you are now above average!

It may sound like an old, bad statistics joke, but I mean it quite literally. We now have very strong evidence that AI elevates the skills of the lowest performers across a wide range of fields to, or even far above, what was previously average performance.

This is a big deal because skill gaps among humans can be quite large. In many fields, the difference between top and bottom performers is profound, including programmers, middle managers, and radiologists, among many others. If you are able to find, train, and retain these top workers, you get tremendous benefits. A large part of schooling and work is focused on getting people to this highly skilled state.

However, people who are good at one skill may not be good at another - the top-performing salespeople are usually not also top-performing sales managers. Modern professional work consists of a wide range of activities, rather than a single specialization. For example, the job of a doctor may require many tasks, like diagnosing patients, providing treatment, offering advice, filling out expense reports, and overseeing the office staff. It is unlikely that any doctor is equally good at all of these tasks. Even the best workers have weak spots, requiring that they be part of larger organizations to ensure they can focus on what they do best.

Or they did have weak spots.

We already know one major effect of AI on the skills distribution: AI acts as a skills leveler for a huge range of professional work. If you were in the bottom half of the skill distribution for writing, idea generation, analyses, or any of a number of other professional tasks, you will likely find that, with the help of AI, you have become quite good.

The new paper that I, along with Fabrizio Dell'Acqua, Edward McFowland III, Hila Lifshitz-Assaf, Katherine Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim Lakhani, just released shows this works even for the highly trained knowledge workers at Boston Consulting Group. Across a set of 18 tasks designed to test a range of business skills - from analysis to idea generation to persuasion - consultants who had previously tested in the lower half of the group increased the quality of their outputs by 43% with AI help while the top half only gained 17%. Where previously the gap between the average performances of top and bottom performers was 22%, it shrunk to a mere 4% once the consultants used GPT-4.

We aren't alone in this type of finding; a lot of work has been done on how AI levels different kinds of writing tasks. Another study, this one from Noy and Zhang of MIT, finds that half the performance gap between good and bad writers is erased when participants use ChatGPT. The same holds true for creative writing, as Doshi and Hauser find. They measured writers on creativity and then gave some of them access to AI generated story ideas. Again, skill differences were levelled, being given 5 ideas by GPT-4 "effectively equalizes the creativity scores across less and more creative writers."

Beyond writing, LLMs appear to help lower performing specialists become better as well. Choi and Schwarcz gave law students access to GPT-4 and found that law students near the bottom of their class using AI equalized their performance with folks at the top of the class (who actually saw a slight decline when using AI). They conclude, "This suggests that AI may have an equalizing effect on the legal profession, mitigating inequalities between elite and nonelite lawyers." And, in a real-world experiment, Brynjolfsson, Li, and Raymond discovered that LLMs helped customer service agents in the bottom quintile catch up with higher performers, with much smaller effects for better workers.

Taken together, the results are clear: AI acts as a leveler, raising everyone to a minimum level of performance. In some ways this may not be surprising - automation has always increased the bar for productivity. It used to matter whether workers were fast or slow at digging a ditch or planting seeds, but now a machine does that work much faster, no matter how good your mere human digging or planting skills are. But just as automation reshaped manual labor, it might do the same for intellectual work. The most extreme vision of this levelling effect was something I heard when I was on a panel with a CEO who had been experimenting with GPT-4. To audible gasps he said, "Most of our employees are engineers and we have a few hundred of them... and I think in 18 months we will need 20% of them, and we can start hiring them out of high school rather than four-year colleges. Same for sales and marketing functions."

I think this prediction for the collapse of skilled work is wrong, however. As we discussed, jobs don't consist of just one automatable task, but rather a set of complex tasks that still require human judgement. Plus, because AI has a "jagged frontier" (it is not good at everything, but rather has uneven abilities) it is unlikely to do every task that a worker is responsible for. Improving the performance in a few areas need not lead to replacement, but instead it will free up time to do more valuable, satisfying, and productive work. And this also assumes that levelling is the only effect of AI.

Just because early results for AI suggest that only lower performing people benefit does not mean that this is the only possible pattern. It may be that the reason only lower performers gain from AI currently is because the current AI systems are not good enough to help top performers. Or, alternately, it might be that top performers need more training and work to get benefits from AI. If either of these conditions prove true, and they certainly seem plausible, then AI might act more as an escalator, increasing the skills for everyone, from top to bottom performers. After an adjustment period, the relative skill positions stay similar, but everyone gets more done, faster.

Alternately, it might be that some people are just really good at working with AI. They can adopt Cyborg practices better than others and have a natural (or learned) gift for working with LLM systems. For them, AI is a huge blessing that changes their place in work and society. Other people may get a small gain from these systems, but these new Kings and Queens of AI get orders of magnitude improvements. If this scenario is true, they would be the new stars of our AI age, and are sought out by every company and institution, the way other top performers are recruited today.

We don't know the ultimate shape of the new post-AI skills distribution, but we do absolutely know that things are changing. Even with the relatively primitive tools of our current, unspecialized AI systems, it is clear that we can become much more productive, and that less-skilled workers are now at much less of a disadvantage than they used to be.

Like so much else about AIs, the implications here are not completely clear, but we have agency over what happens next. Some of the highest-paid jobs are most impacted by AI - so what do companies do in response? Hire less skilled workers and have them boosted by AI? Expect more work out of all their employees? Focus on working with employees so that they become Cyborgs? Or are they tempted to cut wages or headcount? The answers to these questions are critical, and they will be made soon by companies, with influence from government, their employees, their stakeholders, and their customers. We should be thinking right now about how we want this new world of work to look.

And it isn't just work. If AI turns poor employees into good ones, it might do the same for other fields. I suspect that it will have a major effect on entrepreneurship, giving every entrepreneur a personalized cofounder to fill in the gaps in their skills. This suggests a potential for an expansion of entrepreneurship, especially given that GPT-4 is widely available for free around the world, helping potential founders far from the usual technology hubs. However, without guardrails, AI may also do the same for criminals, increasing the competence of the worst actors in society.

A lot is changing, quickly. For the first time, we have a broad-based tool that boosts human intellectual abilities, but we are still limited to human wisdom. We will need to draw on that to make good choices even as change continues to accelerate.

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Is AI a Leveler, King Maker, or Escalator?

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Cite This Webpage

McAdoo, T. (2024, February 23). How to cite ChatGPT. APA Style. https://apastyle.apa.org/blog/how-to-cite-chatgpt

We, the APA Style team, are not robots. We can all pass a CAPTCHA test, and we know our roles in a Turing test. And, like so many nonrobot human beings this year, we've spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT. We've also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we'll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you've used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT "chat" are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications, with ChatGPT-generated text there is no person communicating. Quoting ChatGPT's text from a chat session is therefore more like sharing an algorithm's output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with "Is the left brain right brain divide real or a metaphor?" the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, "the notation that people can be characterized as 'left-brained' or 'right-brained' is considered to be an oversimplification and a popular myth" (OpenAI, 2023).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of "What is a more accurate representation?" the ChatGPT-generated text indicated that "different brain regions work together to support various cognitive processes" and "the functional specialization of different regions can change in response to experience and environmental factors" (OpenAI, 2023; see Appendix A for the full transcript).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Parenthetical citation: (OpenAI, 2023)

Narrative citation: OpenAI (2023)

Let's break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is "ChatGPT," so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using "ChatGPT" as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what's being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor "Large language model" in square brackets. OpenAI describes ChatGPT-4 as a "large multimodal model," so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat. For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher's homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references-four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model's interpretation of them.

We've also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

Reference

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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Subscribe to the APA Style Monthly newsletter to get tips, updates, and resources delivered directly to your inbox.

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This post outlines how to create references for large language model AI tools like ChatGPT and how to present AI-generated text in a paper.

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Inside Higher Ed | Higher Education News, Events and Jobs

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Most students outrunning faculty in AI use, study finds

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A new study finds over half of students use generative AI, while more than 75 percent of faculty members do not regularly use the technology.

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Article - Ethan Mollick and Lilach Mollick

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Article - Ethan Mollick and Lilach Mollick

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Used thoughtfully, generative AI tools like ChatGPT can help educators implement strategies that would otherwise take prohibitive amounts of time and effort to create. Wharton educators share what LLMs are and how you can use them to lighten your workload.

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Syllabi Policies for AI Generative Tools

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As more higher education stakeholders discover and use generative AI, intentional staffing and governance will ensure that institutions adopt these technologies effectively and appropriately.

Credit: Erta © 2023

EDUCAUSE is helping institutional leaders, technology professionals, and other staff address their pressing challenges by sharing existing data and gathering new data from the higher education community. This report is based on an EDUCAUSE QuickPoll. QuickPolls enable us to rapidly gather, analyze, and share input from our community about specific emerging topics.

The Challenge

In a QuickPoll report published earlier this year, EDUCAUSE offered an initial investigation into the nature and scope of generative AI's arrival in higher education. As the use of these technologies becomes more widespread, institutions will increasingly be faced with questions about longer-term implications. How will staff use these technologies themselves in their day-to-day work? To what degree will staff be called upon to guide and support the institution's adoption and use of these technologies? This report explores these longer-term questions that lie ahead.

The Bottom Line

Attitudes toward generative AI have improved over just the past few months, and these technologies are becoming more widely used in day-to-day institutional work. As more stakeholders are introduced to these technologies, the desire for and scale of adoption are likely to accelerate. Institutions must establish appropriate staffing and governance structures to support the use of these technologies and consider which particular use cases align with their needs and comfort levels.

The Data: How Are We Feeling about Generative AI?

Attitudes about generative AI in higher education are improving. In our QuickPoll earlier this year, 54% of respondents rated their disposition toward generative AI as "optimistic" or "very optimistic." In this month's QuickPoll, that number has increased to 67% of respondents. (See figure 1.)

Figure 1. Disposition toward Generative AI, February QuickPoll and Current QuickPoll

Asked about their agreement with specific statements about generative AI, a strong majority of respondents (83%) agreed that these technologies will profoundly change higher education in the next three to five years (see table 1). These changes could be positive or negative. More respondents agreed than disagreed that generative AI would make their job easier and would have more benefits than drawbacks. However, more respondents agreed than disagreed that the use of generative AI in higher education makes them nervous, perhaps an acknowledgment of the potential risks of these technologies, however beneficial.

The use of generative AI could lead to improved dispositions and accelerated adoption. Respondents who have used generative AI differed significantly in disposition from those who have not (see table 2). Those who have used generative AI were far more optimistic about the technology and were more likely to agree that generative AI is going to "profoundly change" higher education in the next three to five years and that the use of generative AI in higher education has more benefits than drawbacks. And in the starkest contrast between users and nonusers, a full three-quarters of those who have used generative AI agreed that it will make their job easier, compared with just under a quarter of those who have not used it.

Attitude

Have Used Generative AI

Have Not Used Generative AI

What to make of these differences? Those who are more optimistic about a new technology to begin with might also be more likely to seek hands-on experience with that technology. The "technopositive" among us may simply be quicker to use and look for the benefits of new technologies.

It is also possible, though, that experience using a new technology can lead to more familiarity with and warmer feelings toward that technology (what social psychologists call the "mere exposure effect"). As opportunities for hands-on encounters with generative AI expand from here, more and more users may find their initial concerns or uncertainties allayed and come to express more positive dispositions toward (and more frequently use) these technologies.

The Data: How Are We Using Generative AI?

Generative AI, though emergent, is becoming widely adopted in our day-to-day work. The majority of respondents (67%) reported that they've used a generative AI tool for their work in the current 2022-23 academic year, and another 13% reported that they anticipate using generative AI in their work in the future. This leaves only 20% of respondents who reported that they have not used, and do not anticipate using, generative AI in their work. (See figure 2.)

Figure 2. Distribution of Generative AI Use and Intended Use

While the majority of respondents have used or anticipate using generative AI, this practice appears to still be emergent. Only 21% of respondents reported using generative AI in their work "frequently" or "very frequently." Meanwhile, a plurality of respondents (41%) reported using it "more than once or twice, but not very frequently," and another 14% reported using it "only once or twice." Among those respondents who've used generative AI, though, 77% reported being satisfied with the results of using it, and a full 96% anticipated continuing to use it in the future.

Generative AI may be more readily adopted in specific areas of work. Asked to describe the use of generative AI in their work, respondents highlighted use cases clustering around four common areas of work: Dreaming, Drudgery, Design, and Development (see figure 3). Although real risks arise that generative AI could be used to replace human activity in these areas, most respondents described generative AI more in assistive terms, as technologies that augment, rather than replace, human activity.

Figure 3. Four Generative AI Use Cases

Presenting an organized picture of the distinct types of use cases for generative AI in this way might help individual staff members discover one or several particular areas where they would feel most comfortable experimenting with and using these technologies. Even better, institutional leadership might consider whether and how certain use cases could present more or less risk to the institution, could be more or less in need of stricter policies and governance, and could help the institution realize more gains in efficiency in areas of greater staff need.

The Data: How Are We Supporting Generative AI?

Gaps appear in formal responsibilities for institutions' use of generative AI. Respondents were asked a series of questions about the responsibilities they and others have regarding their institution's adoption or use of generative AI. Just over half of respondents (53%) reported that they themselves have current responsibilities related to generative AI at their institution, and another 7% reported that someone else at their institution has these responsibilities. This leaves a full 40% of respondents who indicated either that no one at their institution has these responsibilities or that they are unaware of anyone at their institution having these responsibilities. (See figure 4.)

Figure 4. Distribution of Generative AI Responsibilities

Current responsibilities are more senior-level, specialized, and emergent in focus. Looking at generative AI responsibilities by professional role (see table 3), we see that respondents with leadership positions in the areas of instructional technology and design and senior-most technology leaders (e.g., CIOs, VPs) are far more likely to have these responsibilities. Professional-level staff and non-senior technology staff appear far less likely to have these responsibilities. This pattern may be indicative of an emergent technology that is thus far primarily confined to a specific area of the institution (teaching and learning) and not yet fully integrated into "normal" institutional operations.

"Thinking about your role at your institution, do you have current responsibilities related to your institution's adoption or use of generative AI technologies?"

No

Yes

Indeed, when asked to describe the generative AI responsibilities they've taken on, respondents highlighted responsibilities that align with senior-level and instructionally focused positions and that support more nascent stages of technology adoption:

Developing policies and guidelines for appropriate uses of generative AI, to include concerns over academic misconduct and other ethical risks

Serving on institution-wide committees and developing institution-wide strategies

Advising and educating faculty on course- and research-related applications and appropriate use

Assessing generative AI integrations with third-party tools and existing systems

Generative AI product selection and assessment

Developing training and educational resources for faculty, staff, and leadership

When asked what information and resources would be most helpful in taking on these responsibilities, respondents similarly highlighted items perhaps more helpful for senior-level positions and more emergent technologies:

Peer-based "how to" examples of how other institutions are adopting and using generative AI

Up-to-date knowledge about the trends and developments surrounding generative AI

Examples of policies and guidelines that institutions can put into place

Communities of practice and/or peer-based interactions for sharing and learning

Support for building better collaborations with other units at the institution

Thought leadership or advocacy for the values that should be guiding the adoption of generative AI

Common Challenges

Will institutions invest in the structure and governance needed for effective and appropriate use of generative AI? Only 34% of respondents reported that their institution has implemented, or is in the process of implementing, new and/or revised policies to guide the use of generative AI. In their open-ended comments throughout the survey, many respondents also expressed the need for guidelines to help ensure effective and appropriate use of these technologies. Without clear policies and guidelines, adoption of generative AI could be inconsistent across a campus, leaving an institution vulnerable to the security and ethical risks of inappropriate use.

Lack of role clarity and collaboration could hinder the effective adoption of these technologies. Many institutions lack a dedicated staff person to oversee the adoption and use of generative AI. In cases where there are designated staff responsibilities, these responsibilities may simply be added on to existing roles and/or haphazardly split between functional units. Institutional leaders should consider more sustainable plans for staffing these emerging technologies, bringing together individual units (e.g., teaching and learning, IT, library) to determine how they will work together most effectively.

Promising Practices

Take the time on your own to experiment with and explore generative AI. Generative AI tools are available now and are easily accessed (after you finish reading this article, of course!). The most effective way to learn what's possible with generative AI, either to confirm or allay concerns and uncertainties, and to discern whether and how it could benefit your work and your institution, is simply to try it out. Hands-on experimentation may even present opportunities to contribute to the ways these technologies are developed, improved, and used in the future.

Look for smaller, easier use cases as first steps for yourself, your team, and your institution. The adoption and use of generative AI doesn't have to be an "all or nothing" proposition. As outlined in the four use cases above, there may only be one or two smaller areas of application that make the most sense for you and/or your institution right now. Lower-risk, easier opportunities for using these technologies can be balanced against a measured exploration of what the larger, longer-term possibilities could be.

Notes

1. QuickPolls are less formal than EDUCAUSE survey research. They gather data in a single day instead of over several weeks and allow timely reporting of current issues. This poll was conducted between April 10 and April 11, 2023, consisted of 31 questions, and resulted in 441 responses. The poll was distributed to EDUCAUSE members via relevant EDUCAUSE Community Groups. We are not able to associate responses with specific institutions. Our sample represents a range of institution types and FTE sizes.

2. Nicole Muscanell and Jenay Robert, "EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report?" EDUCAUSE Review, February 14, 2023.

Mark McCormack is Senior Director of Research and Insights at EDUCAUSE.

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educause quickpoll results: adopting and adapting to generative ai in higher ed tech

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EDUCAUSE QuickPoll Results: Adopting and Adapting to Generative AI in Higher Ed Tech

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As more higher education stakeholders discover and use generative AI, intentional staffing and governance will ensure that institutions adopt these te

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To add your resource, please click the "+" in the lower righthand corner of your screen. Please include your name and email in your post. I will review all shared resources and post them live for others to see. Thanks! ~ Kate Grovergrys

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Article - Benj Edwards - Why AI Use is Undetectable

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Article - Benj Edwards - Why AI Use is Undetectable

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If you feed America's most important legal document-the US Constitution-into a tool designed to detect text written by AI models like ChatGPT, it will tell you that the document was almost certainly written by AI. But unless James Madison was a time traveler, that can't be the case. Why do AI writing detection tools give false positives? We spoke to several experts-and the creator of AI writing detector GPTZero-to find out.

Among news stories of overzealous professors flunking an entire class due to the suspicion of AI writing tool use and kids falsely accused of using ChatGPT, generative AI has education in a tizzy. Some think it represents an existential crisis. Teachers relying on educational methods developed over the past century have been scrambling for ways to keep the status quo-the tradition of relying on the essay as a tool to gauge student mastery of a topic.

As tempting as it is to rely on AI tools to detect AI-generated writing, evidence so far has shown that they are not reliable. Due to false positives, AI writing detectors such as GPTZero, ZeroGPT, and OpenAI's Text Classifier cannot be trusted to detect text composed by large language models (LLMs) like ChatGPT.

If you feed GPTZero a section of the US Constitution, it says the text is "likely to be written entirely by AI." Several times over the past six months, screenshots of other AI detectors showing similar results have gone viral on social media, inspiring confusion and plenty of jokes about the founding fathers being robots. It turns out the same thing happens with selections from The Bible, which also show up as being AI-generated.

To explain why these tools make such obvious mistakes (and otherwise often return false positives), we first need to understand how they work.

Understanding the concepts behind AI detection

Different AI writing detectors use slightly different methods of detection but with a similar premise: There's an AI model that has been trained on a large body of text (consisting of millions of writing examples) and a set of surmised rules that determine whether the writing is more likely to be human- or AI-generated.

For example, at the heart of GPTZero is a neural network trained on "a large, diverse corpus of human-written and AI-generated text, with a focus on English prose," according to the service's FAQ. Next, the system uses properties like "perplexity" and burstiness" to evaluate the text and make its classification.

In machine learning, perplexity is a measurement of how much a piece of text deviates from what an AI model has learned during its training. As Dr. Margaret Mitchell of AI company Hugging Face told Ars, "Perplexity is a function of 'how surprising is this language based on what I've seen?'"

So the thinking behind measuring perplexity is that when they're writing text, AI models like ChatGPT will naturally reach for what they know best, which comes from their training data. The closer the output is to the training data, the lower the perplexity rating. Humans are much more chaotic writers-or at least that's the theory-but humans can write with low perplexity, too, especially when imitating a formal style used in law or certain types of academic writing. Also, many of the phrases we use are surprisingly common.

Let's say we're guessing the next word in the phrase "I'd like a cup of _____." Most people would fill in the blank with "water," "coffee," or "tea." A language model trained on a lot of English text would do the same because those phrases occur frequently in English writing. The perplexity of any of those three results would be quite low because the prediction is fairly certain.

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Why AI detectors think the US Constitution was written by AI

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Can AI writing detectors be trusted? We dig into the theory behind them.

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Video Series - Ethan Mollick and Lilach Mollick

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Video Series - Ethan Mollick and Lilach Mollick

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A free introductory series on using generative AI

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Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. As a result, educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are further exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons.

Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others' work as their own. They are well-aware of "teachable moments" and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI models. They worry whether recommendations suggested by an algorithm would be fair. Educators' concerns are manifold.

Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in educational technology.

NEW!! Report on AI and the Future of Teaching & Learning

Key Insights

AI enables new forms of interaction. Students and teachers can speak, gesture, sketch, and use other natural human modes of communication to interact with a computational resource and each other. AI can generate human-like responses, as well. These new forms of action may provide supports to students with disabilities.

AI can help educators address variability in student learning. With AI, designers can anticipate and address the long tail of variations in how students can successfully learn-whereas traditional curricular resources were designed to teach to the middle or most common learning pathways. For example, AI-enabled educational technology may be deployed to adapt to each student's English language abilities with greater support for the range of skills and needs among English learners.

AI supports powerful forms of adaptivity. Conventional technologies adapt based upon the correctness of student answers. AI enables adapting to a student's learning process as it unfolds step-by-step, not simply providing feedback on right or wrong answers. Specific adaptations may enable students to continue strong progress in a curriculum by working with their strengths and working around obstacles.

AI can enhance feedback loops. AI can increase the quality and quantity of feedback provided to students and teachers, as well as suggesting resources to advance their teaching and learning.

AI can support educators. Educators can be involved in designing AI-enabled tools to make their jobs better and to enable them to better engage and support their students.

Recommendations: Call to Action for Education Leaders

Recommendation #1: Emphasize Humans in the Loop

We reject the notion of AI as replacing teachers. Teachers and other people must be "in the loop" whenever AI is applied in order to notice patterns and automate educational processes. We call upon all constituents to adopt Humans-in-the-Loop as a key criteria.

Recommendation #2: Align AI Models to a Shared Vision for Education

We call upon educational decision makers, researchers, and evaluators to determine the quality of an educational technology based not only on outcomes, but also based on the degree to which the models at the heart of the AI tools and systems align to a shared vision for teaching and learning. The figure to the right describes the important qualities of AI models for education leaders to consider.

Recommendation #3: Design Using Modern Learning Principles

Achieving effective systems requires more than processing "big data"-it requires more than data science. Applications of AI must be based on established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners and we must ensure that AI-enabled learning resources are intentionally inclusive of these students.

Recommendation #4: Prioritize Strengthening Trust

Technology can only help us to achieve educational objectives when we trust it. Yet, we learned through a series of public listening sessions that distrust of educational technology and AI is commonplace. Because trust develops as people meet and relate to each other, we call for a focus on building trust and establishing criteria for trustworthiness of emerging educational technologies within the associations, convenings, and professional organizations that bring educators, innovators, researchers, and policymakers together.

Recommendation #5: Inform and Involve Educators

We call on educational leaders to prioritize informing and involving educational constituents so they are prepared to investigate how and when AI fits specific teaching and learning needs, and what risks may rise. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled educational technology. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented.

Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety

Research that focuses on how AI-enabled systems can adapt to context (diversity among learners, variability in instructional approaches, differences in educational settings) is essential to answering the question "Do specific applications of AI work in education, and if so, for whom and under what conditions?" We call upon researchers and their funders to prioritize investigations of how AI can address the long tail of learning variability and to seek advances in how AI can incorporate contextual considerations when detecting patterns and recommending options to students and teachers. Further, researchers should accelerate their attention to how to enhance trust and safety in AI-enabled systems for education.

Recommendation #7: Develop Education-Specific Guidelines and Guardrails

Data privacy regulation already covers educational technology; further, data security is already a priority of school educational technology leaders. Modifications and enhancements to the status quo will be required to address the new capabilities alongside the risks of AI. We call for involvement of all perspectives in the ecosystem to define a set of guidelines (such as voluntary disclosures and technology procurement checklists) and guardrails (such as enhancements to existing regulations or additional requirements) so that we can achieve safe and effective AI for education.

Listening Sessions

The U.S. Department of Education's Office of Educational Technology, with support from Digital Promise, held listening sessions about Artificial Intelligence (AI). We connected with all constituents involved in making decisions about technology in education, including but not limited to teachers, educational leaders, students, parents, technologists, researchers, and policy makers.

The goal of these listening sessions were to gather input, ideas, and engage in conversations that will help the Department shape a vision for AI policy that is inclusive of cutting-edge research and practices while also informed by the opportunities and risks.

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Need More Info?

Contact ed.tech@ed.gov with questions.

Please Include "OET AI" in the Subject Line

For a print-out and summary of the Artificial Intelligence Initiative, please review our AI One-pager

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Artificial Intelligence - Office of Educational Technology

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OET is working to develop policies and supports focused on the effective, safe, and fair use of AI-enabled educational technology.

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As ChatGPT and DALL-E go viral, hopes and warnings about artificial intelligence (or AI) are also growing.

AI-based computer programs like ChatGPT can write essays and DALL-E can generate artistic images - in other words, it seems that computers can now do human work, humanly creative work.

AI-based computer programs like ChatGPT now seem to be able to do human work. Picture: Getty Images

While technology will continue to become more sophisticated, conversations about the implications of these two creative AI applications are wide ranging, affecting almost every aspect of human life, including education.

For example, what should the teaching of writing look like when machines can write? What is the nature of art education when machines can create art?

But there's another set of AI-based computer programs that haven't yet had as much attention but may have significant impact on foreign language education in our schools.

What'll be big in 2023? AI, that's what

This is machine translation software that automatically translates text or speech from one language into another.

Machine translation has been in existence since the 1950s but has made significant progress over the last 70 years or so. Popular machine translation tools probably include ones you already use - Google Translate, Microsoft Translator, DeepL, Alexa Translations and the list goes on.

These tools vary a great deal in terms of the number of languages they can translate, their ease of use and accuracy. Some, like Google Translate, can translate over 100 languages, while others cover far fewer.

And the quality of translation can vary a lot - as does the ease of use and access.

But there's an important conversation to have about the potential implications of machine translation - especially when it comes to the purpose, curriculum and teaching of foreign language education in schools.

Machine translation software can automatically translate text or speech from one language into another. Picture: Getty Images

One of the primary aims of foreign language education is to teach students to become competent in a language so that they can communicate with its native speakers.

So, not surprisingly, all curriculums in foreign language courses have an almost exclusive focus on helping students to develop competence in listening, speaking, reading and writing in that language.

Whatever teaching methods are used, the expectation is that students become proficient users of the language.

The language of learning

This expectation is one shared by teachers, school administrators, system leaders, parents and the public. But the expectation is generally wishful thinking for the majority of students who may be taking a foreign language course voluntarily or as a requirement.

Despite the hard work of many teachers and a genuine interest and effort from many student, most of the students are far from competent after one to four (or even ten) school years.

Very few students can truly achieve native or near native speaker proficiency.

In comparison to today's machine translation, which is far from perfect, a majority of students are unable to do as well as translation machines in communicating with native speakers of a language.

Very few students can truly achieve native or near native speaker proficiency. Picture: Getty Images

There are many reasons for the lack of development of true competence after so many years of studying the language, but three reasons are unchangeable.

Firstly, the amount of time of learning the target language is limited. In most schools, the maximum number of hours per week is three to five class hours.

Assuming there's 40 weeks in a school year, the total number of hours is 120 to 200 class hours, which amounts to about 80 to 130 learning hours which is far from the time needed to acquire proficiency in a second language.

Learning a language at home: You are not alone

Secondly, there's typically no environment where a student can authentically speak the language they're learning locally, which limits the real opportunities to practice communication.

And finally, the purpose of learning a language in schools has often been hijacked by testing. That is, for a significant majority of the learners, passing tests becomes more important than actually acquiring the language.

Whatever the reason, the current context of foreign language teaching is unlikely to change and the hope that students in foreign language courses can become competent speakers is - at best - unrealistic.

Given that machine translation is already better at communicating than most people who have spent a few years studying foreign languages and the technology will continue to improve, it is important to rethink foreign language education.

There's typically no environment where a student can authentically speak the language they're learning locally. Picture: Getty Images

Of course, the most immediate but incorrect reaction would be scrapping these courses. This fails to factor in the real meaning of studying foreign languages, which should have always been about culture for a majority of students.

Understanding a different culture greatly expands one's cognitive and psychological competence. Another culture, a different way of living, a different way of thinking, and a different way of feeling, challenges people who are used to thinking and feeling within only one culture.

It helps people to go beyond what they are used to and experience something different, something uncertain and something unexpected. This experience presents more diverse ways of living, more options for human organisation and more possibilities in interaction.

Why are Australians linguistically lost?

Culture should be the core content of any foreign language teaching, but unfortunately linguistic competence has instead always been the primary focus.

Assessment in foreign languages has focused on mastering vocabulary and grammar, as well as abilities in reading, writing, listening and speaking.

Given the difficulty in achieving linguistic competence in foreign languages and the availability of increasingly sophisticated machine translation, it seems reasonable to rethink foreign language courses as primarily cultural courses, with limited expectations of linguistic proficiency.

Making culture the core of foreign language courses is more than learning about one culture. Rather, it is also about developing global competence - the ability to investigate the world, recognise perspectives, communicate ideas and take action.

Assessment in foreign languages has focused on mastering vocabulary and grammar. Picture: Getty Images

The Organisation for Economic Co-operation and Development's (OECD) Programme for International Student Assessment (PISA) defines global competence as "a multi-dimensional construct that requires a combination of knowledge, skills, attitudes and values successfully applied to global issues or intercultural situations."

Whatever the definition, the main idea is about human interdependence and interconnectedness.

In 2018, PISA conducted its first global competence assessment of 15-year-old students around the world, and global competence has been accepted as a core skill for thriving in the 21st Century.

Education for humans in an AI world

It's difficult for schools to add new courses to teach global competence as most are already crowded with too many required courses. The optimal place to teach global competence is foreign language courses, which should focus on other cultures, other languages and global issues.

Of course, this would require changes in assessment, content and teaching methods in these courses.

There is a small percentage of students with a strong interest in foreign languages; schools could offer different courses or encourage students to learn online from native or near-native speaking teachers.

But the overall idea is very simple: if machines can do better than most human foreign language learners in communicating in another language and traditional foreign language courses have generally failed to make students competent, the primary focus of foreign language courses for the majority of students should be culture and global competence.
Professor Yong Zhao will discuss the implications of globalisation, technology and AI on education in a complimentary webinar for educators hosted by Asia Education Foundation on 22 March, 2023. To register, please visit the event webpage.
Banner: Getty Images

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Professor Yong Zhao Foundation Distinguished Professor, School of Education, University of Kansas; Professor, Educational Leadership, Melbourne Graduate School of Education, University of Melbourne

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AI means a rethink of teaching foreign languages

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Machines are now better than most human foreign language students, so teaching languages should focus on global competence says University of Melbourne expert.

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https://www.aiforeducation.io/ai-resources-main

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Assigning AI: Seven Ways of Using AI in Class

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Also prompts! And things to watch out for!

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NIST AIRC - AI Risks and Trustworthiness

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The NIST AIRC supports AI actors in the development and deployment of trustworthy and responsible AI technologies.

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Article - Ethan Mollick and Lilach Mollick

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Article - Ethan Mollick and Lilach Mollick

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Artificial intelligence (AI) panic is spreading across the education sector. The newest wave of generative AI tools, released without guidance or clear suggestions for educators late last year, might be among the biggest and fastest transformations to hit academia. Its rapid adoption instantly challenges the existence of a valuable and widely used type of assignment-the essay-along with dozens of other assignment types in fields from programming to poetry.

Cheating is an obvious concern among educators. Evaluations may require a return to oral exams and writing longhand in exam books. Or maybe AI-written text will be identifiable by other AIs. Or assignment types will adjust to the new reality. Either way, the cheating problem will be solved eventually.

But focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways. That starts with educators becoming familiar with AI tools, especially large language models (LLMs) like those underlying ChatGPT and OpenAI Playground, and taking time to experiment with these systems and get a sense of what they can and cannot do.

Focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways.

As of now, AI is great at creating credible-sounding facts, but will often share false data, made-up citations, and incorrect information. Thus, students must have enough knowledge about the topic they are examining to produce sufficient results. Receiving good results from these tools also takes practice. We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text. This will help them to be successful in their studies and better prepared for their futures. These tools will be part of their lives, so they should learn how to apply them effectively.

Integrating AI into our classrooms isn't always easy-it takes time and dedication. To help, we are sharing some ways we are experimenting with AI to raise student expectations and enhance learning. But first, we propose you start your AI journey by creating a classroom policy for these tools to help your students better understand and properly use them.

Crafting an AI Policy for Your Class

AI is a tool that has tremendous potential. But, like all tools, it has its advantages and
disadvantages. To create productive and meaningful content with AI, our students must
learn to work with ChatGPT and other LLMs.

Educators can help by creating an environment in which students get a chance to practice doing just that. The first step is to be transparent about our expectations. We must set a clear classroom policy that forecasts potential problems and offers guidance for acceptable use. We've found that providing this information seems to give students permission to explore and understand the tool. It also offers clarity and teaches them how to use AI honestly.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly.

We created an AI policy for undergraduate and MBA students and found they really appreciate the direction and transparency of expectations. Further, assignments and projects continue to be thoughtful and writing quality seems to improve.

Here are our suggestions for crafting your own policy.

Your policy should include these considerations:

Under what circumstances AI use is permitted or forbidden

How students should cite or credit AI

A warning about the technology's tendency toward hallucination (i.e., deceptive data) and clear rules regarding students' accountability for AI output

A notice about using AI ethically and responsibly

Discussion of the need to use AI as a tool to learn, not just to produce content

An example policy:

"I expect you to use AI (e.g., ChatGPT and image generation tools) in this class. In fact, some assignments will require it. Learning to use AI is an emerging skill and I provide tutorials on how to use them. I am happy to meet and help you with these tools during office hours or after class.

"Be aware of the limits of ChatGPT, such as the following:

If you provide minimum-effort prompts, you will get low-quality results. You will need to refine your prompts in order to get good outcomes. This will take work.

Don't trust anything it says. If it gives you a number or fact, assume it is wrong unless you either know the answer or can check with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand.

AI is a tool, but one that you need to acknowledge using. Please include a paragraph at the end of any assignment that uses AI explaining what you used the AI for and what prompts you used to get the results. Failure to do so is in violation of academic honesty policies.

Be thoughtful about when this tool is useful. Don't use it if it isn't appropriate for the case or circumstance."

Feel free to use this language or adjust it to fit in your own course.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly. You are also welcome to use our tutorials and modify them as needed.

Now that you have a policy, it's time to think about how you might use these AI tools to enhance student learning. Here are two use cases to try in your classes.

Using AI to Raise Expectations-and Level the Playing Field

Our students have tremendous skills, but not all are good writers. Some may have a language barrier, others may lack a particular predilection for writing, or they may not have had prior opportunity to take courses in writing. Whatever the reason, not being an expert writer can be a problem, particularly in academia where grades depend on it. As a result, some students take elaborate risks; they may hire editors or even cheat by paying people to write essays for them.

ChatGPT can level the playing field. With AI, everyone can now produce credible writing (as well as illustrations, code, and in the coming months, even more outputs such as video). While this may pose a problem for some classes, like those heavily focused on teaching the mechanics of writing, for many other classes, ChatGPT can work like a calculator of sorts-helping improve student performance. In these classes, you might set the expectation that you want to see only high-quality written work because AI can help students produce error-free sentences and paragraphs.

However, producing meaningful and insightful content through AI is not necessarily easy; it requires both topic expertise and skill. Show your students this guide for writing with AI; asking them to credit AI and provide the prompts they used to write their essays will teach them how to use the tool properly.

Ultimately, by embracing AI in your classroom, you'll likely be increasing your expectations about how much students should be able to accomplish. In other words, they may save time writing essays using AI, but that time should now be used to gather more data and think more deeply about the material.

Elevating Student Learning with AI

If we are willing to think creatively and try new approaches with it, AI can unleash a whole new way of learning. For example, letting students teach or edit each other's work is a powerful way to take concepts from theory into practice (e.g., peer review). As part of a new course exercise we designed, ChatGPT serves as students' partner to help improve their writing.

In the exercise, the AI produces an essay based on a prompt, and its student partner then critiques it and works to steadily improve the essay by adding new information, clarifying points, fact-checking, and adding insight and analysis. This exercise takes advantage of the AI's propensity to simplify complex topics, using its lack of insightful analysis as a backdrop for students to provide evidence of understanding.

We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text.

Approaching AI in this way pushes students to think critically about the content and articulate their thoughts for improvement clearly and concisely. Likewise, the exercise can help deepen their understanding of complex topics. Asking students to identify what the AI gets right and wrong can activate deeper thinking, highlighting nuances and implications they hadn't previously considered.

This and similar exercises should help improve students' understanding of major class concepts and illustrate the limits of ChatGPT and other available LLMs.
Find ideas for prompts and assignments you can incorporate into your classroom here.
Embracing AI by Taking an Experimental Approach

AI is here to stay and will only become more prevalent in our classrooms over time. Educators need to think beyond adapting to these tools-we must actively embrace them as a way to improve our teaching.

The best path forward is experimentation. Some experiments will work; others won't. It's also important to remember that while the sudden advent of generative AI may be disruptive to educators, it is even more disruptive to the futures of the students we teach. We need to give them the skills to thrive in a world transformed by AI.

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Why All Our Classes Suddenly Became AI Classes

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As AI panic spreads across academia, Wharton educators Ethan and Lilach Mollick offer advice for crafting classroom policies and assignments that incorporate the use of AI tools such as ChatGPT and other large language models to elevate student learning.

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Webinar - Ethan Mollick and Lilach Mollick

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Webinar - Ethan Mollick and Lilach Mollick

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Speakers:
Ethan Mollick, Associate Professor, Wharton School, University of Pennsylvania and Dr. Lilach Mollick, Director of Pedagogy, Wharton Interactive
Related Articles:
To stay in touch with us and be notified of new webinars, industry insight, and practical guidance for teaching online, register as an Educator on the Harvard Business Publishing Education website if you haven't already - it's free.

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The core irony of generative AIs is that AIs were supposed to be all logic and no imagination. Instead we get AIs that make up information, engage in (seemingly) emotional discussions, and which are intensely creative. And that last fact is one that makes many people deeply uncomfortable.

To be clear, there is no one definition of creativity, but researchers have developed a number of flawed tests that are widely used to measure the ability of humans to come up with diverse and meaningful ideas. The fact that these tests were flawed wasn't that big a deal until, suddenly, AIs were able to pass all of them. But now, GPT-4 beats 91% of humans on the a variation of the Alternative Uses Test for creativity and exceeds 99% of people on the Torrance Tests of Creative Thinking. We are running out of creativity tests that AIs cannot ace.

While these psychological tests are interesting, applying human tests to AI can be challenging. There is always the chance that the AI has previously been exposed to the results of similar tests and is just repeating answers (although the researchers in these studies have taken steps to minimize that risk). And, of course, psychological testing is not necessarily proof that AI can actually come up with useful ideas in the real world.

Yet, over the past couple of weeks, we have learned from three new experimental papers that AI really can be creative in settings with real-world implications. I want to briefly discuss these papers, and then give some practical tips on how to use AI for idea generation based on their results.

Each of the three papers directly compares AI-powered creativity and human creative effort in controlled experiments. The first major paper is from my colleagues at Wharton. They staged an idea generation contest: pitting ChatGPT-4 against the students in a popular innovation class that has historically led to many startups. The researchers - Karan Girotra, Lennart Meincke, Christian Terwiesch, and Karl Ulrich - used human judges to assess idea quality, and found that ChatGPT-4 generated more, cheaper and better ideas than the students. Even more impressive, from a business perspective, was that the purchase intent from outside judges was higher for the AI-generated ideas as well! Of the 40 best ideas rated by the judges, 35 came from ChatGPT.

A second paper conducted a wide-ranging crowdsourcing contest, asking people to come up with business ideas based on reusing, recycling, or sharing products as part of the circular economy. The researchers (Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani) then had judges rate those ideas, and compared them to the ones generated by GPT-4. The overall quality level of the AI and human-generated ideas were similar, but the AI was judged to be better on feasibility and impact, while the humans generated more novel ideas.

The final paper did something a bit different, focusing on creative writing ideas, rather than business ideas. The study by Anil R. Doshi and Oliver P. Hauser c ompared humans working alone to write short stories to humans who used AI to suggest 3-5 possible topics. Again, the AI proved helpful: humans with AI help created stories that were judged as significantly more novel and more interesting than those written by humans alone. There were, however, two interesting caveats. First, the most creative people were helped least by the AI, and AI ideas were generally judged to be more similar to each other than ideas generated by people. Though again, this was using AI purely for generating a small set of ideas, not for writing tasks.

So what does this mean? Reading these studies, it seems like there are a few clear conclusions:

AI can generate creative ideas in real-life, practical situations. It can also help people generate better ideas.

The ideas AI generates are better than what most people can come up with, but very creative people will beat the AI (at least for now), and may benefit less from using AI to generate ideas

There is more underlying similarity in the ideas that the current generation of AIs produce than among ideas generated by a large number of humans

All of this suggests that humans still have a large role to play in innovation... but that they would be foolish not to include AI in that process, especially if they don't consider themselves highly creative. So, how should we use AI to help generate ideas? Fortunately, the papers, and other research on innovation, have some good suggestions.

People get very hung up on the idea that you have to be great at prompting AIs with specific wording to get them to accomplish anything. But this just doesn't seem to be the case in idea generation. In the paper comparing AI to crowdsourcing, the authors tested three kinds of prompts: basic ones that stated the problem, more advanced ones that gave the AI a persona to be more like a human solver ("You are a Technical and Creative Professional, located in Europe."), and a very advanced one that asked the AI to take the perspective of particular famous experts. While there were some differences between these groups, no one approach clearly dominated. So I wouldn't worry too much about the exact wording of the prompt, you can experiment to see what might work best.

Truthfully, simple prompts seemed to work fine. The paper on innovation contests, for example, provided a simple system prompt as context: You are a creative entrepreneur looking to generate new product ideas. The product will target college students in the United States. It should be a physical good, not a service or software. I'd like a product that could be sold at a retail price of less than about USD 50. The ideas are just ideas. The product need not yet exist, nor may it necessarily be clearly feasible. Number all ideas and give them a name. The name and idea are separated by a colon. And also provided a second user prompt: Please generate ten ideas as ten separate paragraphs. The idea should be expressed as a paragraph of 40-80 words. They repeated this process several times, because generating a lot of ideas is useful.

They also compared the value of this GPT-4 prompt to one using few-shot learning. Few-shot learning is easy to do - you simply provide the AI with examples of the kind of results you would like to see (the "few shots" rather than "zero shot" learning, where you provide no examples) before you ask it to generate ideas. While the AI generated more and better ideas with few-shot approaches, the difference was not statistically significant. At the same time, I generally would still suggest using few-shot techniques because they seem to help subjectively, other research has found them valuable, and they are easy to implement.

As an example of the difference, I asked Claude 2 to generate 15 original startup ideas that would be a good fit for Y Combinator (the famous accelerator). This was a zero-shot approach. Then I tried a few-shot method where I gave the AI a list of 400 recent Y Combinator startups, with one-sentence descriptions of each, and prompted: Here are 400 of the latest startup ideas from Y Combinator. Come up with trends, then generate 15 original ideas combining these concepts. You can see the difference, and why I preferred the few-shot approach.

Outside of these suggestions, I have a couple of my own. First, don't just ask the AI to generate ideas, use constraints as well. In general, and contrary to what most people expect, AI works best to generate ideas when it is most constrained (as do humans!). Force it to give you less likely answers, and you are going to find more original combinations, which may solve the originality problem. You might want to ask: You are an expert at problem solving and idea generation. When asked to solve a problem you come up with novel and creative ideas. Here is your first task: tell me 10 detailed ways an AI(or a superhero, or an astronaut, or any other odd profession) might do _____. Describe the details of each way.

You can also use other techniques that take advantage of the ways that AI can hallucinate plausible, but interesting, material, and use that as a seed of creativity. Consider asking it for interview transcripts for fake interviews: Create an interview transcript between a product designer and a dentist about the problems the dentist ha s, for example. Or ask it to describe non-existent products: walk me through the interface for a fictional new water pump that has exciting new features. There is an art to this that you can learn from experimentation, and you should feel free to share other prompt techniques that work in the comments.

We still don't know how original AIs actually can be, and I often see people argue that LLMs cannot generate any new ideas. To me, it is increasingly clear that this is not true, at least in a practical, rather than philosophical, sense. In the real world, most new ideas do not come from the ether; they are based on combinations existing concepts, which is why innovation scholars have long pointed to the importance of recombination in generating ideas. And LLMs are very good at this, acting as connection machines between unexpected concepts. They are trained by generating relationships between tokens that may seem unrelated to humans but represent some deeper connections. Add in the randomness that comes with AI output, and the result is, effectively, a powerful creative ability.

In the most practical sense, we are now much less limited by ideas than ever before. Even people who don't consider themselves creative now have access to a machine that will generate innovative concepts that beat those of most humans (though not the most creative ones). Where previously, there were only a few people who had the ability to come up with good ideas, now there are many. This is an astonishing change in the landscape of human creativity, and one that likely makes execution, not raw creativity, a more distinguishing factor for future innovations.

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There is now strong evidence that AI can help make us more innovative.

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AI Prompts for Teaching - Dr. Cynthia Alby

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AI Prompts for Teaching - Dr. Cynthia Alby

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Unlike the carefully scripted dialogue found in most books and movies, the language of everyday interaction tends to be messy and incomplete, full of false starts, interruptions and people talking over each other. From casual conversations between friends, to bickering between siblings, to formal discussions in a boardroom, authentic conversation is chaotic. It seems miraculous that anyone can learn language at all given the haphazard nature of the linguistic experience.

For this reason, many language scientists - including Noam Chomsky, a founder of modern linguistics - believe that language learners require a kind of glue to rein in the unruly nature of everyday language. And that glue is grammar: a system of rules for generating grammatical sentences.

Children must have a grammar template wired into their brains to help them overcome the limitations of their language experience - or so the thinking goes.

This template, for example, might contain a "super-rule" that dictates how new pieces are added to existing phrases. Children then only need to learn whether their native language is one, like English, where the verb goes before the object (as in "I eat sushi"), or one like Japanese, where the verb goes after the object (in Japanese, the same sentence is structured as "I sushi eat").

But new insights into language learning are coming from an unlikely source: artificial intelligence. A new breed of large AI language models can write newspaper articles, poetry and computer code and answer questions truthfully after being exposed to vast amounts of language input. And even more astonishingly, they all do it without the help of grammar.

Grammatical language without a grammar

Even if their choice of words is sometimes strange, nonsensical or contains racist, sexist and other harmful biases, one thing is very clear: the overwhelming majority of the output of these AI language models is grammatically correct. And yet, there are no grammar templates or rules hardwired into them - they rely on linguistic experience alone, messy as it may be.

GPT-3, arguably the most well-known of these models, is a gigantic deep-learning neural network with 175 billion parameters. It was trained to predict the next word in a sentence given what came before across hundreds of billions of words from the internet, books and Wikipedia. When it made a wrong prediction, its parameters were adjusted using an automatic learning algorithm.

Remarkably, GPT-3 can generate believable text reacting to prompts such as "A summary of the last 'Fast and Furious' movie is..." or "Write a poem in the style of Emily Dickinson." Moreover, GPT-3 can respond to SAT level analogies, reading comprehension questions and even solve simple arithmetic problems - all from learning how to predict the next word.

Comparing AI models and human brains

The similarity with human language doesn't stop here, however. Research published in Nature Neuroscience demonstrated that these artificial deep-learning networks seem to use the same computational principles as the human brain. The research group, led by neuroscientist Uri Hasson, first compared how well GPT-2 - a "little brother" of GPT-3 - and humans could predict the next word in a story taken from the podcast "This American Life": people and the AI predicted the exact same word nearly 50% of the time.

The researchers recorded volunteers' brain activity while listening to the story. The best explanation for the patterns of activation they observed was that people's brains - like GPT-2 - were not just using the preceding one or two words when making predictions but relied on the accumulated context of up to 100 previous words. Altogether, the authors conclude: "Our finding of spontaneous predictive neural signals as participants listen to natural speech suggests that active prediction may underlie humans' lifelong language learning."

A possible concern is that these new AI language models are fed a lot of input: GPT-3 was trained on linguistic experience equivalent to 20,000 human years. But a preliminary study that has not yet been peer-reviewed found that GPT-2 can still model human next-word predictions and brain activations even when trained on just 100 million words. That's well within the amount of linguistic input that an average child might hear during the first 10 years of life.

We are not suggesting that GPT-3 or GPT-2 learn language exactly like children do. Indeed, these AI models do not appear to comprehend much, if anything, of what they are saying, whereas understanding is fundamental to human language use. Still, what these models prove is that a learner - albeit a silicon one - can learn language well enough from mere exposure to produce perfectly good grammatical sentences and do so in a way that resembles human brain processing.

Rethinking language learning

For years, many linguists have believed that learning language is impossible without a built-in grammar template. The new AI models prove otherwise. They demonstrate that the ability to produce grammatical language can be learned from linguistic experience alone. Likewise, we suggest that children do not need an innate grammar to learn language.

"Children should be seen, not heard" goes the old saying, but the latest AI language models suggest that nothing could be further from the truth. Instead, children need to be engaged in the back-and-forth of conversation as much as possible to help them develop their language skills. Linguistic experience - not grammar - is key to becoming a competent language user.

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AI is changing scientists' understanding of language learning - and raising questions about an innate grammar

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Linguists have long considered grammar to be the glue of language, and key to how children learn it. But new prose-writing AIs suggest language experience may be more important than grammar.

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Article - Blanka Klimova and Prodhan Mahbub Ibna Seraj

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Article - Blanka Klimova and Prodhan Mahbub Ibna Seraj

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Department of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic

Department of English, American International University-Bangladesh, Dhaka, Bangladesh

This mini-review aims to identify major research trends, models, and theories and provide specific pedagogical implications for teaching when using chatbots in EFL classes. This study follows the guidelines of the PRISMA methodology and searches for open-access empirical studies in two reputable databases, Web of Science and Scopus. The results of this mini-review confirm the findings of other research studies, which show that the present research on the use of chatbots in university EFL settings focuses on their effectiveness, motivation, satisfaction, exposure, and assessment. The key contribution of this study lies in its evaluation of the chatbot's potential in applying and integrating the existing theories and concepts used in EFL teaching and learning, such as CEFR, mind mapping, or self-regulatory learning theory. This will address the gap in the literature because no previous review study has conducted such an analysis. Overall, the findings of this mini-review contribute with their specific pedagogical implications and methods to the effective use of chatbots in the EFL environment, be it formal or informal.

1. Introduction

Language acquisition happens through interaction with peers, teachers, and other professionals ( Çakıroğlu, 2018). Interaction is crucial for the language acquisition process because it gives learners comprehensible input, feedback on their output, and the chance to produce modified output ( Liu, 2022). Such opportunities for language learning can be offered to learners through interaction with pedagogical or conversational chatbots ( Yin and Satar, 2020; Mageira et al., 2022). AI chatbots have an impact on students' communication abilities ( Kim et al., 2021). Using text, speech, graphics, haptics, and gestures, as well as other modes of communication, chatbots assist students in completing educational tasks ( Kuhail et al., 2022). The greatest strengths of chatbots are their usability and accessibility; their conversational metaphor and text-or voice-based interfaces make them more intuitive and mobile-friendly. Text-based interactions between humans and chatbots have demonstrated their potential benefits ( Adam et al., 2021). Specifically, chatbots can instill in their users higher levels of motivation and engagement, which are crucial in technology-supported language learning ( Petrović and Jovanović, 2021). The chatbots have allowed language learners to practice their language skills in real life. It is undoubtedly a useful tool for EFL students as they have few opportunities to use the target language in actual conversation. Moreover, for the sake of each participant's unique language proficiency, an AI chatbot can help students learn by adapting how the lessons are delivered ( Nghi et al., 2019). Overall, AI chatbots offer students a much wider range of services, improve learners' motivation, and broaden the learners' frame of conventional-based learning in the digital world, which is essential in the 21st century ( Junaidi et al., 2020; Mukhallafi, 2020).

The following research questions were set in order to answer the aim of this study:

a. What were the publication trends and major research trends in the identified empirical studies on the application of chatbots for EFL learning and teaching at the tertiary level?

b. What models and theoretical frameworks were used in these studies on the application of chatbots for EFL learning and teaching at the tertiary level?

2. Methods

For the purpose of choosing more pertinent research studies on the topic and organizing them for a close examination, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed ( Moher et al., 2015; Ibna Seraj and Habil, 2021). Based on the guidelines of PRISMA, the search was not limited by a time scope but finished on 7 October 2022. In addition, only experimental studies dealing exclusively with the use of chatbots in teaching English as a foreign language with a special focus on their implementation in EFL classrooms were included in this mini-review. Theoretical, descriptive, observational, and non-experimental studies were excluded from the search as the main aim was to look for empirically verified findings. The search was conducted in two well-established databases, i.e., Scopus and Web of Science, in the titles of the articles, their abstracts, and keywords as this is sufficient to generate a reliable and adequate core of articles to be further analyzed.

2.1. Inclusion criteria

* Only experimental studies focusing on the research topic

* Published until October 2022

* Scopus and Web of Science databases

* Peer-reviewed and only English-written journal articles were included

* Search terms were applied in the title, abstract, or keywords of the articles

* Experimental studies with specific practical outcomes for ELL

* Open access

2.2. Exclusion criteria

* Descriptive studies, theoretical studies, conference proceedings, case studies, qualitative studies, observational studies

* Other than open-access studies

* Other (less reputable) databases

* Other languages

* The studies of the research scope, i.e., those which did not investigate the use of chatbots in EFL at a university level, e.g., Yang et al. (2022)

The gist of the exclusion criteria indicates that this study excluded all types of review studies, qualitative studies, and short studies like theoretical studies, conference proceedings, and case studies. It also did not consider non-open access and studies published in databases other than Scopus and Web of Science. Finally, studies published in languages other than English or not dealing with English language teaching and learning at the university level were excluded.

2.3. Search string

("Chatbots OR "conversational agents" OR "virtual assistant") AND ("English language" OR "foreign language") AND "university."

The initial search using this search string generated 110 documents from Scopus and 101 studies from the Web of Science. After applying all inclusion and exclusion criteria and removing duplicates, seven studies were fully analyzed since they covered all inclusion and exclusion criteria.

3. Results

The results of this mini-review in response to research questions (a) and (b) are discussed below.

Research question (a): What were the publication trends and major research trends in the identified empirical studies on the application of chatbots for EFL learning and teaching at the tertiary level?

3.1. Publications and research trends

3.1.1. Publication trends

Table 1 presents the summary of the publication trends of empirical studies ( n  = 7) by researchers, contexts, methods, objectives, participants, instruments, and findings. In terms of researchers, we found that there were 15 researchers who conducted empirical studies on the application of chatbots in English language learning and teaching. This study reported that the empirical studies were conducted in 7 different contexts in Asian and European countries and Egypt.

Table 1. Summary of publication trends of the existing empirical studies.

The majority of studies employed quantitative research design ( n  = 5) using survey questions and tests and reported findings from descriptive statistical analysis. The objective of the studies was to investigate the impact of chatbots on learners' language skills development. These empirical studies employed 668 university learners to elicit the effect of chatbots on English language learning. The average number of participants in each study was 96. Finally, the findings indicate that the application of chatbots in EFL teaching and learning positively affected the development of learners' language skills.

3.1.2. Major research trends of the studies

The major research trends are presented in Figure 1 and discussed in detail in the sections below.

Figure 1. Major research trends.

Our review shows that there are several different research trends using AI chatbots in the classroom for language teaching and learning, e.g., learner satisfaction, effects, new exposure for learning, and assessment of language performance.

3.1.2.1. Effects of the application of AI Chatbots

3.1.2.2. New exposure

3.1.2.3. Learner satisfaction

Another vital theme derived from the analysis was the satisfaction of using chatbots for teaching and learning different language skills. Belda-Medina and Calvo-Ferrer (2022) investigated participants' level of satisfaction employing the Chatbot-Human Interaction Satisfaction Model (CHISM) and found positive perception of the integration of conversational agents in language learning in terms of participants' perceived ease of use (PeU) and attitudes (AT). In support of satisfaction, Hew et al. (2022) found positive learner experiences concerning the chatbots' perceived usefulness and ease of use. This was also true for the study by Mahmoud (2022).

3.1.2.4. Assessment

Research question (b): What models and theoretical framework were employed in the detected studies on the application of chatbots for EFL learning and teaching at the tertiary level?

3.2. Integration with models and theories

We found that there were different conceptual models and theories proposed and implemented for using chatbots to develop learners' language development. Most of the studies employed models and theories to facilitate teaching and learning, and only CEFR was employed to assess learners' language performance. These are presented as follows:

3.2.1. Common European framework of reference

3.2.2. Mind mapping strategy

3.2.3. Self-regulatory learning

In this mini-review, we pointed out that self-regulatory learning was facilitated through the application of AI chatbots in language classrooms. Using adaptive learning environments and intelligent tutoring systems, chatbots encourage self-regulated learning by enhancing the individual learner's experience ( Mahmoud, 2022). The dynamic process of self-regulated learning, which is comprised of cognitive, affective, motivational, and behavioral components, allows learners to control their own learning ( Panadero, 2017).

3.2.4. Situated learning theory

3.2.5. SMART framework

We reported that using the SMART framework for implementing AI chatbots for learning and teaching language skills was effective. Supporting student goal-setting and social presence to develop listening skills, the chatbots were useful through the SMART (specific, measurable, achievable, realistic, and timely) goal-setting framework ( Hew et al., 2022). Both the learning buddy chatbot and the goal setting Chatbot employing Google Dialogflow were visual development tools that did not require prior computer programming knowledge (cf. Mendoza et al., 2022).

3.2.6. Socratic IM

4. Discussion

Our mini-review show that only 7 open access empirical studies were conducted at the tertiary level ( Nghi et al., 2019; Yin and Satar, 2020; Kim et al., 2021; Lin and Mubarok, 2021; Belda-Medina and Calvo-Ferrer, 2022; Hew et al., 2022; Mahmoud, 2022). The oldest one dates back as late as 2019, which indicates that the use of chatbots in university EFL classrooms is quite novel, and the number of these studies reveal that the research on their applications is scarce ( Yin and Satar, 2020; Belda-Medina and Calvo-Ferrer, 2022; Merelo et al., 2022). Nevertheless, all the studies found that chatbots had a positive impact on the development of learners' English language skills, especially on developing learners' speaking skills in terms of suprasegmental features, e.g., intonation, and stress, as well as fluency engaging learners in practice inside and outside the classes ( Kim et al., 2021; Mahmoud, 2022; or Lin and Mubarok, 2021), as well as facilitating peer and group activities among learners ( Mahmoud, 2022). However, we indicated that more research should be done among low-level foreign language learners since these benefit from using chatbots the least ( Yin and Satar, 2020) to address the gaps in the literature.

We set several pedagogical implications for EFL practitioners, which are as follows:

* chatbots are ideal for informal settings since they provide authentic context and can be used anytime and anywhere

* chatbots are user-friendly; therefore, easy to use (no technical knowledge is required) and engaging

* short conversations are more suitable when providing automated assessment

* in particular, speaking skills can be developed, and students with a low level of foreign language benefit the most

* chatbots should be used since they enhance the critical thinking of EFL learners through Socratic IM

* AI chatbots play a significant role in reducing language-learning inhibitions and speech-related fears in English as a foreign language (cf. Bao, 2019)

The major limitation of this study seems to be a lack of empirical research on this topic. Nevertheless, it provides valuable, up-to-date information for further empirical and theoretical research in this area. Undoubtedly, the findings of this mini-review contribute with their practical implications and methods to the effective use of chatbots in the EFL environment, be it formal or informal.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Funding

This study was supported by the Excellence project 2023, run at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Merelo, J.J., Castillo, P.A., Mora, A.M., Barranco, F., Abbas, N., Guillen, A., and Tsivitanidou, O. (2022). Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective. Available at: https://download.arxiv.org/pdf/2201.10289v1 [Accessed December 24, 2022].
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The use of chatbots in university EFL settings: Research trends and pedagogical implications

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This mini-review aims to identify major research trends, models, and theories and provide specific pedagogical implications for teaching when using chatbots in EFL classes. This study follows the guidelines of the PRISMA methodology and searches for open-access empirical studies in two reputable databases, Web of Science and Scopus.

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AI in language learning is revolutionizing the approach to language acquisition, as it's capable of processing vast amounts of data, making it an essential tool for both learners and educators. Whether it's through chatbots that simulate real-life conversations, personalized lesson plans based on individual learner needs, or advanced speech recognition technology that can identify nuanced pronunciation errors, AI is making language learning more accessible and effective than ever before.

One of the biggest advantages of AI in language learning is its ability to provide instant feedback. Traditional classroom settings often rely on periodic assessments to measure progress and provide feedback, but with AI-powered tools, learners can receive immediate corrections and suggestions for improvement. This not only speeds up the learning process but also makes it more engaging by providing a sense of accomplishment as learners see their skills improve in real time.

However, while AI offers many benefits for language learners, there are also concerns about its impact on human interaction and socialization skills. As we increasingly rely on machines for communication and feedback, some worry that we may lose important face-to-face interactions that are crucial for developing certain aspects of communication, such as empathy and nuance. Nonetheless, with careful implementation and consideration given to both the benefits and drawbacks of this technology in educational settings, AI has the potential to revolutionize how we learn languages.

The field of computer science known as artificial intelligence, or AI, is concerned with developing machines that are capable of performing operations that ordinarily require human cognition, such as language processing, perception, and decision-making. Since its inception in the 1950s, AI technology has developed significantly thanks to advances in machine learning and natural language processing. Recently, AI has been applied to numerous industries, including education.

Artificial intelligence has the potential to transform conventional teaching strategies in the field of language learning. Language learners can practice their language skills through interactive conversations in a more personalized and interesting way with AI-powered languages learning tools like chatbots and virtual assistants. These tools use algorithms for natural language processing to identify speech patterns and offer immediate feedback on pronunciation and grammar issues.

While AI offers great promise for improving language learning outcomes, there are also concerns about its impact on human interactions. Some argue that relying too much on AI tools may hinder learners from developing social skills essential for effective communication with people from different cultures. As such, educators need to strike a balance between using AI technology as an aid for language learning while still encouraging face-to-face interactions between learners and native speakers.

Language learning has become increasingly important in the age of artificial intelligence. As AI technology advances, it becomes more capable of understanding and communicating in different languages. This makes language proficiency an essential skill for individuals seeking to work with or alongside AI systems.

Learning a language has many other cognitive benefits besides simply enhancing social interaction. It has been shown to boost memory, delay the onset of dementia in older persons, and increase critical thinking skills. Also, learning a new language can open up new chances for both professional and personal development by allowing individuals to communicate with people from different cultures and expand their horizons.

Overall, investing time in language learning is a valuable endeavor that can provide both practical benefits in our increasingly globalized world as well as personal enrichment through enhanced cognitive abilities and cultural understanding.

The two are closely related since AI technology is being used to enhance language learning experiences. Apps driven by AI can provide specialized language instruction that adapts to each learner's proficiency. This enables students to receive a curriculum that is tailored to their specific requirements, resulting in more effective and efficient learning outcomes.

AI technology can also aid in interpreting and translating languages. AI algorithms can translate text or spoken language into a variety of languages rapidly and accurately because they can evaluate enormous volumes of data and identify patterns in voice. This has significant ramifications for international cooperation and communication, making it simpler for persons with various linguistic backgrounds to converse clearly.

In conclusion, AI technology is transforming the way we learn languages by providing personalized teaching methods and boosting our ability to communicate across linguistic borders. We may anticipate even more cutting-edge options for language learning in the future as this technology develops.

Chatbots are one of the main ways AI is being applied to language acquisition. These bots allow language learners to communicate while getting immediate feedback on their grammar and pronunciation. Because the bot may adapt its responses to the learner's level and needs, this form of engagement enables individualized learning.

The use of speech recognition technology in language learning is another use of AI. Using this technology, students can practice speaking while getting immediate feedback on their pronunciation. Additionally, it can point out typical errors students make and offer recommendations for improvement.

Virtual instructors with AI capabilities are increasingly common in language learning. These tutors develop unique lesson plans for each student based on their skills, limitations, and progress using machine learning algorithms. As a result, students can learn at their own pace and get specialized help from an AI instructor.

The availability and convenience of language learning have increased with the development of AI-powered language-learning apps. With the aid of these apps, kids can benefit from tailored courses that take into account their unique skills and developmental stages. These apps use machine learning algorithms to analyze students' performance data and tailor classes to their needs.

Learning a new language is also thrilling because of the interactive and amusing features of these language-learning applications. An exciting approach for language learners to communicate with a new language is through AI-powered language learning apps, which range from chatbots that simulate real-life conversations to virtual reality experiences that immerse users in foreign cultures.

Despite some limitations, such as the lack of human interaction or cultural nuances, AI-powered language learning apps have proven effective in improving learners' skills. Many users report significant improvements in their pronunciation, vocabulary, and comprehension after using these types of applications for just a few weeks. As technology continues to evolve, so will the capabilities of these innovative tools, only further enhancing the ability for anyone interested in picking up another language to have easy access toward achieving fluency!

In today's globalized world, AI-powered language translation systems are gaining popularity. For companies that need to communicate with partners or clients abroad, these solutions utilize machine learning algorithms to evaluate and translate content from one language to another, saving time and effort. These translation technologies are improving in accuracy and efficiency as AI technology develops, making it simpler for people who don't speak the same language to communicate efficiently.

However, some experts argue that relying solely on AI-powered translation can hinder language learning efforts. While these tools can provide quick translations, they often lack cultural context or nuance that would be present in a human translation. Additionally, by relying on AI translations, individuals may miss out on the opportunity to develop their language skills and cultural knowledge.

Notwithstanding these difficulties, there is no denying the efficacy and ease of AI-powered language translations. As this technology advances, we'll undoubtedly see even more cutting-edge uses that facilitate cross-cultural communication.

The way individuals learn languages is changing as AI-powered chatbots become more prevalent. Language learning is now more accessible and effective than ever thanks to chatbots that mimic human-like conversations and offer individualized feedback. Learners can practice speaking, listening, and writing in a secure and encouraging setting with the aid of these chatbots.

The capacity of chatbots powered by AI to adjust to the demands of different learners is one of their main advantages. These technologies can tailor lessons to each learner's specific needs by monitoring progress and evaluating data on learners' strengths and shortcomings. In typical classroom settings, where teachers may have large classes with a range of ability levels, it is challenging to provide this level of personalization.

Moreover, AI-powered chatbots are available 24/7, which means that learners can practice at any time convenient for them without needing a teacher or partner present. This flexibility allows learners to take control of their learning journey while also providing an opportunity for continuous improvement. Overall, AI-powered chatbots offer an innovative solution that could potentially revolutionize the way we approach language learning in the future.

Language learners can now experience a learning process that is even more tailored and effective thanks to AI-powered personalized language learning. The learner's strengths and weaknesses can be examined using AI technology, which can then customize classes to meet those needs. This enables the student to concentrate on their weak areas while also facing challenges in their strong ones. AI can also instantly correct pronunciation and grammar, enabling learners to make changes in real time.

One of the main benefits of using AI for language learning is its accessibility. Learners no longer need to rely on traditional classroom settings or private tutors for personalized attention. Instead, they can access high-quality language instruction from anywhere with an internet connection. Moreover, AI-powered language learning platforms are available 24/7, allowing learners to study at their own pace and schedule.

Moreover, individualized language learning enabled by AI has the potential to change language learning by making it more pleasant. Learners can immerse themselves in realistic circumstances that push them to employ their new language abilities in actual situations by using interactive features like chatbots and virtual reality simulations. As a result, there is an improvement in retention as well as an increase in motivation and self-assurance when speaking a foreign language.

Personalized learning is one of the key advantages of employing AI in language learning. Language learners can now receive advice and criticism tailored to their skills and shortcomings thanks to artificial intelligence (AI). For instance, AI-driven language learning software may monitor a student's progress and modify the exercises' level of difficulty as necessary.

Another advantage of incorporating AI into language learning is its ability to provide immediate feedback. Traditional methods usually require learners to wait for a teacher or tutor to check their work before receiving feedback. However, with AI, learners receive instant feedback on their answers, enabling them to correct mistakes immediately.

Finally, using AI in language learning allows for greater efficiency and flexibility. Learners can access these programs at any time from anywhere with an internet connection, making it easier for busy individuals to fit language practice into their daily routines. Additionally, AI can analyze data on how much time learners spend studying each topic and adjust lesson plans accordingly to optimize their effectiveness.

The majority of businesses now require constant accessibility in the fast-paced environment we live in today. AI is now being used to help and support language learners at any time of day, thanks to technological breakthroughs. Students can now access educational materials online and receive immediate performance feedback as a result.

Platforms that use AI to power language learning have completely changed how individuals learn languages. For each learner, these systems design a unique learning environment using algorithms for natural language understanding. Using interactive exercises that are tailored to each learner's level of proficiency, students can hone their speaking, writing, listening, and reading skills.

Moreover, these platforms are accessible from anywhere in the world, creating a borderless classroom where students can connect with teachers and fellow learners from different countries. The availability of such resources helps individuals who may not have access to traditional language-learning classes due to geographical or financial constraints.

In conclusion, 24/7 accessibility through AI-powered language learning platforms has made it possible for anyone with an internet connection to learn a new language efficiently and effectively. This trend will continue as more people embrace online education as an alternative to traditional classroom settings.

With good cause, the term "personalized learning" has gained popularity in the education sector. Personalized learning experiences are now easier to obtain and more effective than ever thanks to developments in AI technology. Particularly, AI-powered language learning platforms are revolutionizing language learning by providing tailored courses that take into account each student's distinct needs and skills.

By analyzing data on each student's performance, these platforms can identify areas where they struggle and adjust the curriculum accordingly. For example, if a student is struggling with a particular grammar rule, the platform might provide extra practice exercises or offer additional explanations until they master it. This level of personalization ensures that students are not held back by their weaknesses but are instead given targeted support to improve.

Overall, personalized learning experiences powered by AI technology have revolutionized language learning by making it more efficient and effective. As these platforms continue to evolve, we can expect even greater levels of customization in education as a whole.

Artificial intelligence has improved the effectiveness and efficiency of language learning. Now widely accessible, AI-powered language tuition applications provide individualized learning experiences that respond to individual needs and interests. These apps use machine learning to analyze a learner's progress and modify lessons as necessary to maximize information retention.

One well-known example is Duolingo, which offers an enjoyable language-learning experience by combining gamification strategies and AI-driven algorithms. Users of the program can practice speaking, listening, reading, and writing abilities at their own pace while taking interactive classes in more than 30 different languages. Babbel is another AI-based tool that focuses on teaching conversational languages through practical examples.

AI technology has also paved the way for more immersive language learning experiences. Virtual reality (VR) programs like MondlyVR allow learners to practice their language skills in simulated real-world environments. By experiencing everyday situations in a foreign language, like ordering food at a restaurant or asking for directions, learners can gain confidence in using the language they're studying.

Ultimately, AI-assisted language learning offers many advantages over traditional classroom methods. With personalized feedback and immersive experiences that simulate real-life scenarios, learners can quickly improve their communication skills in any foreign tongue of their choosing!

Real-time feedback and assessment are becoming increasingly important in language learning as technology continues to evolve. AI-powered platforms can now provide instant feedback on grammar, pronunciation, and vocabulary usage, allowing learners to improve their language skills faster than ever before.

Real-time feedback has several benefits, but one of the biggest is that it eliminates the need for time-consuming, nerve-wracking traditional classroom testing procedures. Rapid assessments offered by AI-powered systems allow learners to quickly identify their strengths and weaknesses during the entire learning process.

Moreover, real-time feedback makes it possible for learners to identify areas where they may be struggling so they can quickly adjust their focus accordingly. This way, they don't waste time studying concepts or topics that they already understand while receiving targeted help where needed. Therefore, real-time feedback and assessment with AI are changing the way we learn languages, making them more efficient and personalized than ever before.

One of the main challenges of AI in language learning is its inability to fully understand the nuances and complexities of human language. While AI systems can recognize and analyze individual words and phrases, they may struggle with understanding idiomatic expressions or sarcasm. As a result, relying solely on AI for language learning may lead to students missing important cultural contexts or making embarrassing communication errors.

Another limitation of AI in language learning is its ability to personalize instruction. While some AI programs can adapt their lessons based on student performance, they cannot entirely replicate the level of personalization that comes from working with a human tutor or teacher. Additionally, not all students learn in the same way, meaning that an AI program designed for one type of learner may not be effective for another.

Finally, it's worth noting that not all learners have access to the technology required for the successful use of AI in language learning. Without reliable internet access or necessary hardware (such as smartphones or computers), students may find themselves unable to take advantage of these tools. This can create a further divide between those who have access to advanced technology and those who do not, potentially perpetuating educational inequalities.

AI is a tool that is gaining popularity among language learners. The absence of interpersonal interaction is one of the main negatives, though. While AI can give students access to a variety of knowledge and resources, it cannot take the place of the advantages of speaking with a human teacher or conversation partner.

Human interaction allows for more personalized and adaptive learning experiences. A teacher can tailor their lessons to match a student's learning style and pace while also providing immediate feedback on pronunciation and grammar. Additionally, conversing with native speakers allows learners to practice real-life communication skills and pick up on cultural nuances that may not be captured by an AI program.

The importance of human interaction in learning a new language cannot be overstated, despite the fact that AI can clearly improve language learning experiences in many ways.

AI-powered translation tools have revolutionized the way we communicate across language barriers. These tools leverage complex algorithms and machine-learning techniques to provide near-human translations of text or speech. However, their accuracy can vary depending on several factors, such as the quality of the data used for training, the complexity of the source language, and context-specific nuances.

Despite significant improvements in AI-powered translation tools in recent years, they are still not perfect. For instance, idiomatic expressions that do not have a direct equivalent in another language can be challenging to translate accurately. Similarly, cultural references or local dialects can be difficult to capture using these tools. Thus, while AI-powered translation tools offer tremendous value by enabling communication across languages quickly and efficiently, users should exercise caution when relying on them for critical communications.

Regardless of their limitations, AI-powered translation tools remain an essential aid for language learners worldwide. They offer an efficient way to learn new words and phrases while practicing grammar structures in context. Language learners can also use these tools to improve their pronunciation skills by listening to the translated output and comparing it with native speakers' pronunciation. Overall, AI-powered translation tools offer immense potential for improving language learning outcomes if used appropriately and complementarily with other resources such as human instructors or authentic materials like books or videos from native speakers' environments.

The cost of implementing AI-powered language learning varies depending on the specific platform, features, and customization required. For example, some language learning companies offer free or low-cost versions with limited features such as basic vocabulary and grammar lessons. However, more advanced versions with conversational practice, personalized feedback, and natural language processing capabilities can cost hundreds or even thousands of dollars per user.

Moreover, the cost of developing and maintaining an AI-powered language learning platform can be significant due to the need for specialized software development expertise and ongoing updates to keep up with new research in natural language processing. Companies also need to invest in data collection and analysis to improve the accuracy and effectiveness of their algorithms over time.

Despite these costs, a lot of businesses are investing in AI-driven language learning since it has many benefits over conventional teaching techniques, including increased personalization, scalability, and efficiency. We may anticipate seeing more cutting-edge platforms that mix the finest of human teaching with the strength of artificial intelligence emerge as technology continues to grow at a rapid rate.

AI and language learning has a bright future. AI-powered language learning systems may now provide more individualized and effective language education to learners globally thanks to improvements in natural language processing (NLP) and machine learning. These tools may assess a learner's strengths and limitations, adjust to their speed, and offer courses that are specifically tailored to meet their needs.

In addition, AI-powered virtual assistants like Google Assistant or Amazon Alexa are already helping people improve their vocabulary by providing instant translations or pronunciations of words. Chatbots are also becoming more popular for practicing conversation skills in a safe environment. Learners can engage in simulated conversations with chatbots programmed to converse naturally in different languages.

As technology continues to evolve, we can expect even more innovative applications of AI for language learning. For example, augmented reality (AR) could be used to create immersive language environments that simulate real-life situations where learners can practice speaking with virtual native speakers. Gamification is another area where AI could be leveraged to make language learning fun and engaging through interactive games that incorporate grammar, vocabulary, and pronunciation exercises.

There is no denying that advances in AI technology have had a significant impact on language learning. Powerful algorithms and machine learning methods have been developed, and as a result, AI systems can now evaluate enormous volumes of data and derive insightful findings on language learning.

The use of chatbots for language learning is one illustration of this. These bots replicate human interaction using natural language processing (NLP), and they give students tailored feedback depending on their responses. They are a highly effective tool for language learners since they can modify their teaching methods to fit the demands of various students.

The application of virtual reality (VR) for immersive language learning experiences is another fascinating advancement in AI technology. Virtual reality (VR) can make it more engaging and dynamic for language learners to practice their abilities while also giving them immediate feedback on how they are doing.

Overall, developments in AI technology have created new opportunities for enhancing language learning results. These tools - from virtual reality simulations to chatbots - are assisting students in acquiring fluency faster and more successfully than ever before.

The advancements in artificial intelligence (AI) technology have opened up a whole new world of possibilities for virtual and augmented reality (VR and AR). One area that has seen significant growth is the integration of AI with language learning through VR and AR. Language learners can practice their skills with real speakers or virtual characters in VR and AR thanks to AI's ability to replicate real-world scenarios.

VR and AR devices may instantly correct a user's grammar and pronunciation using voice recognition software that is driven by AI. Also, students can get individualized advice on what they should work on. The use of this technology immerses learners in a simulated environment that resembles real-life events, making language learning more interactive and engaging.

Furthermore, by combining AI with VR and AR, learning experiences may be tailored to specific students' skills and interests. Each learner can have a personalized learning experience thanks to technology that can adjust to their pace and learning preferences. In general, combining AI with VR and AR has enabled substantial advances in language instruction by producing previously unattainable, tailored, and immersive learning experiences.

The introduction of artificial intelligence (AI) into the language learning sector has resulted in substantial developments. AI-powered language learning programs now allow learners to learn new languages faster and more efficiently than ever before. AI can now analyze speech patterns, identify faults, and provide fast feedback to learners thanks to developments in natural language processing technology.

These AI-powered language learning platforms also provide customized learning experiences depending on the requirements and skills of individual learners. These platforms may track a learner's progress and adapt their curriculum to ensure maximal competency in the target language using machine learning algorithms.

Overall, the potential impact of AI on the language learning industry is immense. It could lead to increased accessibility and affordability of quality language education globally while providing an engaging and interactive experience for learners everywhere. As a result, this could help bridge cultural divides by making it easier for people from different linguistic backgrounds to communicate with each other effectively.

Finally, AI has transformed the way we approach language learning. Learners now have access to tailored language training that adapts to their particular needs and preferences because of improvements in natural language processing and machine learning algorithms. Chatbots, virtual instructors, and speech recognition software powered by AI have made language learning more engaging and dynamic than ever before.

Moreover, AI has democratized access to high-quality language instruction by breaking down traditional barriers such as geography or cost. Online platforms that leverage AI technology can provide affordable or even free language courses to anyone with an internet connection. As a result, learners from all over the world can improve their language skills without having to travel abroad or pay exorbitant fees.

Without a doubt, AI's pervasiveness in language acquisition will continue to grow in the future. Developers will be better positioned to create sophisticated tools that can adjust the learning experience to the specific demands of individual learners as data on language learning and technology interaction accumulates. The promise of additional breakthroughs in language acquisition technology customization is both tantalizing and interesting. With AI at the forefront of language teaching, the possibilities for tailored learning seem unlimited.

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Language Learning in the Age of AI: Challenges and Opportunities

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Discover the power of AI in language learning. From personalized instruction to instant translation.