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Why talking about AI has become like talking about sex

By Doug Ward

We need to talk.

Yes, the conversation will make you uncomfortable. It’s important, though. Your students need your guidance, and if you avoid talking about this, they will act anyway – usually in unsafe ways that could have embarrassing and potentially harmful consequences.

So yes, we need to talk about generative artificial intelligence.Students gasp as professor stands at front of room and shows a lifelike robot

Consider the conversation analogous to a parent’s conversation with a teenager about sex. Susan Marshall, a teaching professor in psychology, made that wonderful analogy recently in the CTE Online Working Group, and it seems to perfectly capture faculty members’ reluctance to talk about generative AI.

Like other faculty members, Marshall has found that AI creates solid answers to questions she poses on assignments, quizzes, and exams. That, she said, makes her feel like she shouldn’t talk about generative AI with students because more information might encourage cheating. She knows that is silly, she said, but talking about AI seems as difficult as talking about condom use.

It can, but as Marshall said, we simply must have those conversations.

Sex ed, AI ed

Having frank conversations with teenagers about sex, sexually transmitted diseases, and birth control can seem like encouragement to go out and do whatever they feel like doing. Talking with teens about sex, though, does not increase their likelihood of having sex. Just the opposite. As the CDC reports: “Studies have shown that teens who report talking with their parents about sex are more likely to delay having sex and to use condoms when they do have sex.”

Similarly, researchers have found that generative AI has not increased cheating. (I haven’t found any research on talking about AI.)

That hasn’t assuaged concern among faculty members. A recent Chronicle of Higher Education headline captures the prevailing mood: “ChatGPT Has Everyone Freaking Out About Cheating.”

When we freak out, we often make bad decisions. So rather than talking with students about generative AI or adding material about the ethics of generative AI, many faculty members chose to ignore it. Or ban it. Or use AI detectors as a hammer to punish work that seems suspicious.

All that has done is make students reluctant to talk about AI. Many of them still use it. The detectors, which were never intended as evidence of cheating and which have been shown to have biases toward some students, have also led to dubious accusations of academic misconduct. Not surprisingly, that has made students further reluctant to talk about AI or even to ask questions about AI policies, lest the instructor single them out as potential cheaters.

Without solid information or guidance, students talk to their peers about AI. Or they look up information online about how to use AI on assignments. Or they simply create accounts and, often oblivious and unprotected, experiment with generative AI on their own.

So yes, we need to talk. We need to talk with students about the strengths and weaknesses of generative AI. We need to talk about the ethics of generative AI. We need to talk about privacy and responsibility. We need to talk about skills and learning. We need to talk about why we are doing what we are doing in our classes and how it relates to students’ future.

If you aren’t sure how to talk with students about AI, draw on the many resources we have made available. Encourage students to ask questions about AI use in class. Make it clear when they may or may not use generative AI on assignments. Talk about AI often. Take away the stigma. Encourage forthright discussions.

Yes, that may make you and students uncomfortable at times. Have the talk anyway. Silence serves no one.

JSTOR offers assistance from generative AI

Ithaka S+R has released a generative AI research tool for its JSTOR database. The tool, which is in beta testing, summarizes and highlights key areas of documents, and allows users to ask questions about content. It also suggests related materials to consider. You can read more about the tool in an FAQ section on the JSTOR site.

Useful lists of AI-related tools for academia

While we are talking about Ithaka S+R, the organization has created an excellent overview of AI-related tools for higher education, assigning them to one of three categories: discovery, understanding, and creation. It also provides much the same information in list form on its site and on a Google Doc. In the overview, an Ithaka analyst and a program manager offer an interesting take on the future of generative AI:

These tools point towards a future in which the distinction between the initial act of identifying and accessing relevant sources and the subsequent work of reading and digesting those sources is irretrievably blurred if not rendered irrelevant. For organizations providing access to paywalled content, it seems likely that many of these new tools will soon become baseline features of their user interface and presage an era where that content is less “discovered” than queried and in which secondary sources are consumed largely through tertiary summaries.

Preparing for the next wave of AI

Dan Fitzpatrick, who writes and speaks about AI in education, frequently emphasizes the inevitable technological changes that educators must face. In his weekend email newsletter, he wrote about how wearable technology, coupled with generative AI, could soon provide personalized learning in ways that make traditional education obsolete. His question: “What will schools, colleges and universities offer that is different?”

In another post, he writes that many instructors and classes are stuck in the past, relying on outdated explanations from textbooks and worksheets. “It’s no wonder that despite our best efforts, engagement can be a struggle,” he says, adding: “This isn’t about robots replacing teachers. It’s about kids becoming authors of their own learning.”

Introducing generative AI, the student

Two professors at the University of Nevada-Reno have added ChatGPT as a student in an online education course as part of a gamification approach to learning. The game immerses students in the environment of the science fiction novel and movie Dune, with students competing against ChatGPT on tasks related to language acquisition, according to the university.

That AI student has company. Ferris State University in Michigan has created two virtual students that will choose majors, join online classes, complete assignments, participate in discussion boards, and gather information about courses, Inside Higher Ed Reports. The university, which is working with a Grand Rapids company called Yeti CGI on developing the artificial intelligence software for the project, said the virtual students’ movement through programs would help them better understand how to help real students, according to Michigan Live. Ferris State is also using the experiment to promote its undergraduate AI program.

Doug Ward is associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications.

What we’ve learned from a year of AI

By Doug Ward

A year after the release of a know-it-all chatbot, educators have yet to find a satisfying answer to a nagging question: What are we supposed to do with generative artificial intelligence?

One reason generative AI has been so perplexing to educators is that there is no single step that all instructors can take to make things easier. Here are a few things what we do know, though:

  • A group of people in 19th-century clothing gather around a wooden box as robots spring out
    The sudden rise of generative AI has felt like the opening of a Pandora’s box

    Students are using generative AI in far larger numbers than faculty, and some are using it to complete all or parts of assignments. A recent Turnitin poll said 22% of faculty were using generative AI, compared with 49% of students.

  • Students in other developed countries are far more likely to use generative AI than students in the U.S., two recent polls suggest.
  • Students are as conflicted as faculty about generative AI, with many worried about AI’s impact on jobs, thinking, and disinformation.
  • Many faculty say that students need to know how to use generative AI but also say they have been reluctant to use it themselves.
  • Detectors can provide information about the use of generative AI, but they are far from flawless and should not be the sole means of accusing students of academic misconduct.

Perhaps the biggest lesson we have learned over the past year is that flexibility in teaching and learning is crucial, especially as new generative AI tools become available and the adoption of those tools accelerates.

We don’t really have an AI problem

It’s important to understand why generative AI has made instructors feel under siege. In a forthcoming article in Academic Leader, I argue that we don’t have an AI problem. We have a structural problem:

Unfortunately, the need for change will only grow as technology, jobs, disciplines, society, and the needs of students evolve. Seen through that lens, generative AI is really just a messenger, and its message is clear: A 19th-century educational structure is ill-suited to handle changes brought on by 21st-century technology. We can either move from crisis to crisis, or we can rethink the way we approach teaching and learning, courses, curricula, faculty roles, and institutions.

That’s not a message most faculty members or administrators want to hear, but it is impossible to ignore. Colleges and universities still operate as if information were scarce and as if students can learn only from faculty members with Ph.D.s. The institutional structure of higher education was also created to exclude or fail students deemed unworthy. That’s much easier than trying to help every student succeed. We are making progress at changing that, but progress is slow even as change accelerates. I’ll be writing more about that in the coming year.

Faculty and staff are finding ways to use AI

Many instructors have made good use of generative AI in classes, and they say students are eager for such conversations. Here are a few approaches faculty have taken:

  • Creating AI-written examples for students to critique.Surprised people gather around a box as a humanoid robot emerges amid a glowing light
  • Allowing students to use AI but asking them to cite what AI creates and separately explain the role AI played in an assignment.
  • Having students use AI to create outlines for papers and projects, and refining goals for projects.
  • Allowing full use of AI as long as students check the output for accuracy and edit and improve on the AI-generated content.
  • Having students design posters with AI.
  • Using examples from AI to discuss the strengths and weaknesses of chatbots and the ethical issues underlying them.
  • Using paper and pencils for work in class. In recent discussions with CTE ambassadors, the term “old school” came up several times, usually in relation to bans on technology. As appealing as that may seem, that approach can put some students at a disadvantage. Many aren’t used to writing by hand, and some with physical impediments simply can’t.
  • For non-native English speakers, generative AI has been a confidence-builder. By evaluating their writing with a grammar checker or chatbot, they can improve phrasing and sentence construction.
  • Some faculty members say that generative AI saves time by helping them create letters of recommendation, event announcements, and case studies and other elements for class.

Sara Wilson, an associate professor of mechanical engineering and a CTE faculty fellow, uses what I think is probably the best approach to AI I’ve seen. In an undergraduate course that requires a considerable amount of programming, she allows students to use whatever tools they wish to create their coding. She meets individually with each student – more than 100 of them – after each project and asks them to explain the concepts behind their work. In those brief meetings, she said, it is fairly easy to spot students who have taken shortcuts.

Like faculty, students are often conflicted

Many students seem as conflicted as faculty over generative AI. In a large introductory journalism and mass communications class where I spoke late this semester, I polled students about their AI use. Interestingly, 21% said they had never used AI and 45% said they had tried it but had done little beyond that. Among the remaining students, 27% said they used AI once a week and 7% said they used it every day. (Those numbers apply only to the students in that class, but they are similar to results from national polls I mention above.)

In describing generative AI, students used terms like “helpful,” “interesting,” “useful” and “the future,” but also “theft,” “scary,” “dangerous,” and “cheating.” Recent polls suggest that students see potential in generative AI in learning but that they see a need for colleges and universities to change. In one poll, 65% of students said that faculty needed to change the way they assess students because of AI, the same percentage that said they wanted faculty to include AI instruction in class to help them prepare for future jobs.

Students I’ve spoken with describe AI as a research tool, a learning tool, and a source of advice. Some use AI as a tutor to help them review for class or to learn about something they are interested in. Others use it to check their writing or code, conduct research, find sources, create outlines, summarize papers, draft an introduction or a conclusion for a paper, and help them in other areas of writing they find challenging. One CTE ambassador said students were excited about the possibilities of generative AI, especially if it helped faculty move away from “perfect grading.”

Time is a barrier

For faculty, one of the biggest challenges with AI is time. We’ve heard from many instructors who say that they understand the importance of integrating generative AI into classes and using it in their own work but that they lack the time to learn about AI. Others say their classes have so much content to cover that working in anything new is difficult.

Instructors are also experiencing cognitive overload. They are being asked to focus more on helping students learn. They are feeling the lingering effects of the pandemic. In many cases, class sizes are increasing; in others, declining enrollment has created anxiety. Information about disciplines, teaching practices, and world events flows unendingly. “It’s hard to keep up with everything,” one CTE ambassador said.

Generative AI dropped like a boulder into the middle of that complex teaching environment, adding yet another layer of complexity: Which AI platform to use? Which AI tools? What about privacy? Ethics? How do we make sure all students have equal access? The platforms themselves can be intimidating. One CTE ambassador summed up the feelings of many I’ve spoken with who have tried using a chatbot but weren’t sure what to do with it: “Maybe I’m not smart enough, but I don’t know what to ask.”People in 19th-century clothing stand before a glowing box with robots at the side

We will continue to provide opportunities for instructors to learn about generative AI in the new year. One ongoing resource is the Generative AI and Teaching Working Group, which will resume in the spring. It is open to anyone at KU. CTE will also be part of a workshop on generative AI on Jan. 12 at the Edwards Campus. That workshop, organized by John Bricklemyer and Heather McCain, will have a series of sessions on such topics as the ethics of generative AI, strategies for using AI, and tools and approaches to prompting for instructors to consider.

We will also continue to add to the resources we have created to help faculty adapt to generative AI. Existing resources focus on such areas as adapting courses to AI, using AI ethically in writing assignments, using AI as a tutor, and handling academic integrity. We have also provided material to help generate discussion about the biases in generative AI. I have led an effort with colleagues from the Bay View Alliance to provide information about how universities can adapt to generative AI. The first of our articles was published last week in Inside Higher Ed. Another, which offers longer-term strategies, is forthcoming in Change magazine. Another piece for administrators will be published this month in Academic Leader.

Focusing on humanity

If generative AI has taught us anything over the past year, it is that we must embrace humanity in education. Technology is an important tool, and we must keep experimenting with ways to use it effectively in teaching and learning. Technology can’t provide the human bond that Peter Felten talked about at the beginning of the semester and that we have made a priority at CTE. Something Felten said during his talk at the Teaching Summit is worth sharing again:

“There’s literally decades and decades of research that says the most important factor in almost any positive student outcome you can think about – learning, retention, graduation rate, well-being, actually things like do they vote after they graduate – the single biggest predictor is the quality of relationships they perceive they have with faculty and peers,” Felten said.

Technology can do many things, but it can’t provide the crucial human connections we all need.

In an ambassadors meeting in November, Dorothy Hines, associate professor of African and African-American studies and curriculum and teaching, summed it up this way: “AI can answer questions, but it can’t feel.” As educators, she said, it’s important that we feel so that our students learn to feel.

That is wise advice. As we continue to integrate generative AI into our work, we must do so in a human way.

Doug Ward is associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications.

Research points to AI’s growing influence

By Doug Ward

If you are sitting on the fence, wondering whether to jump into the land of generative AI, take a look at some recent news – and then jump.

  • Three recently released studies say that workers who used generative AI were substantially more productive than those who didn’t. In two of the studies, the quality of work also improved.
  • The consulting company McKinsey said that a third of companies that responded to a recent global survey said they were regularly using generative AI in their operations. Among white-collar professions that McKinsey said would be most affected by generative AI in the coming decade are lawyers and judges, math specialists, teachers, engineers, entertainers and media workers, and business and financial specialists.
  • The textbook publisher Pearson plans to include a chatbot tutor with its Pearson+ platform this fall. A related tool already summarizes videos. The company Chegg is also creating an AI chatbot, according to Yahoo News.
  • New AI-driven education platforms are emerging weekly, all promising to make learning easier. These include: ClaudeScholar (focus on the science that matters), SocratiQ (Take control of your learning), Monic.ai (Your ultimate Learning Copilot), Synthetical (Science, Simplified), Upword (Get your research done 10x faster), Aceflow (The fastest way for students to learn anything), Smartie (Strategic Module Assistant), and Kajabi (Create your course in minutes).

My point in highlighting those is to show how quickly generative AI is spreading. As the educational consultant EAB wrote recently, universities can’t wait until they have a committee-approved strategy. They must act now – even though they don’t have all the answers. The same applies to teaching and learning.

A closer look at the research

Because widespread use of generative AI is so new, research about it is just starting to trickle out. The web consultant Jakob Nielsen said the three AI-related productivity studies I mention above were some of the first that have been done. None of the studies specifically involved colleges and universities, but the productivity gains were highest in the types of activities common to colleges and universities: handling business documents (59% increase in productivity) and coding projects (126% increase).

From “Generative AI and the Future of Work,” McKinsey & Company, 2023

One study, published in Science, found that generative AI reduced the time professionals spent on writing by 40% but also helped workers improve the quality of their writing. The authors suggested that “ChatGPT could entirely replace certain kinds of writers, such as grant writers or marketers, by letting companies directly automate the creation of grant applications and press releases with minimal human oversight.”

In one of two recent McKinsey studies, though, researchers said most companies were in no rush to allow automated use of generative AI. Instead, they are integrating its use into existing work processes. Companies are using chatbots for things like creating drafts of documents, generating hypotheses, and helping experts complete tasks more quickly. McKinsey emphasized that in nearly all cases, an expert oversaw use of generative AI, checking the accuracy of the output.

Nonetheless, by 2030, automation is expected to take over tasks that account for nearly a third of current hours worked, McKinsey said in a separate survey. Jobs most affected will be in office support, customer service, and food service. Workers in those jobs are predominantly women, people of color, and people with less education. However, generative AI is also forcing changes in fields that require a college degree: STEM fields, creative fields, and business and legal professions. People in those fields aren’t likely to lose jobs, McKinsey said, but will instead use AI to supplement what they already do.

“All of this means that automation is about to affect a wider set of work activities involving expertise, interaction with people, and creativity,” McKinsey said in the report.

What does this mean for teaching?

I look at employer reports like this as downstream reminders of what we in education need to help students learn. We still need to emphasize core skills like writing, critical thinking, communication, analytical reasoning, and synthesis, but how we help students gain those skills constantly evolves. In terms of generative AI, that will mean rethinking assignments and working with students on effective ways to use AI tools for learning rather than trying to keep those tools out of classes.

Chart showing which careers will be most affected by AI automation
From “Generative AI and the Future of Work,” McKinsey & Company, 2023

If you aren’t swayed by the direction of businesses, consider what recent graduates say. In a survey released by Cengage, more than half of recent graduates said that the growth of AI had left them feeling unprepared for the job market, and 65% said they wanted to be able to work alongside someone else to learn to use generative AI and other digital platforms. In the same survey, 79% of employers said employees would benefit from learning to use generative AI. (Strangely, 39% of recent graduates said they would rather work with AI or robots than with real people; 24% of employers said the same thing. I have much to say about that, but now isn’t the time.)

Here’s how I interpret all of this: Businesses and industry are quickly integrating generative AI into their work processes. Researchers are finding that generative AI can save time and improve work quality. That will further accelerate business’s integration of AI tools and students’ need to know how to use those tools in nearly any career. Education technology companies are responding by creating a large number of new tools. Many won’t survive, but some will be integrated into existing tools or sold directly to students. If colleges and universities don’t develop their own generative AI tools for teaching and learning, they will have little choice but to adopt vendor tools, which are often specialized and sold through expensive enterprise licenses or through fees paid directly by students.

Clearly, we need to integrate generative AI into our teaching and learning. It’s difficult to know how to do that, though. The CTE website provides some guidance. In general, though, instructors should:

  • Learn how to use generative AI.
  • Help students learn to use AI for learning.
  • Talk with students about appropriate use of AI in classes.
  • Experiment with ways to integrate generative AI into assignments.

Those are broad suggestions. You will find more specifics on the website, but none of us has a perfect formula for how to do this. We need to experiment, share our experiences, and learn from one another along the way. We also need to push for development of university-wide AI tools that are safe and adaptable for learning.

The fence is collapsing. Those who are still sitting have two choices: jump or fall.

AI detection update

OpenAI, the organization behind ChatGPT, has discontinued its artificial intelligence detection tool. In a terse note on its website, OpenAI said that the tool had a “low rate of accuracy” and that the company was “researching more effective provenance techniques for text.”

Meanwhile, Turnitin, the company that makes plagiarism and AI detectors, updated its figures on AI detection. Turnitin said it had evaluated 65 million student papers since April, with 3.3% flagged as having 80% to 100% of content AI-created. That’s down from 3.5% in May. Papers flagged as having 20% or more of content flagged rose slightly, to 10.3%.

I appreciate Turnitin’s willingness to share those results, even though I don’t know what to make of them. As I’ve written previously, AI detectors falsely accuse thousands of students, especially international students, and their results should not be seen as proof of academic misconduct. Turnitin, to its credit, has said as much.

AI detection is difficult, and detectors can be easily fooled. Instead of putting up barriers, we should help students learn to use generative AI ethically.

Doug Ward is the associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications.

Why generative AI is now a must for graduate classes

By Doug Ward

Instructors have raised widespread concern about the impact of generative artificial intelligence on undergraduate education.

As we focus on undergraduate classes, though, we must not lose sight of the profound effect that generative AI is likely to have on graduate education. The question there, though, isn’t how or whether to integrate AI into coursework. Rather, it’s how quickly we can integrate AI into methods courses and help students learn to use AI in finding literature; identifying significant areas of potential research; merging, cleaning, analyzing, visualizing, and interpreting data; making connections among ideas; and teasing out significant findings. That will be especially critical in STEM fields and in any discipline that uses quantitative methods.

The need to integrate generative AI into graduate studies has been growing since the release of ChatGPT last fall. Since then, companies, organizations, and individuals have released a flurry of new tools that draw on ChatGPT or other large language models. (See a brief curated list below.) If there was any lingering doubt that generative AI would play an outsized role in graduate education, though, it evaporated with the release of a ChatGPT plugin called Code Interpreter. Code Interpreter is still in beta testing and requires a paid version of ChatGPT to use. Early users say it saves weeks or months of analyzing complex data, though.

OpenAI is admirably reserved in describing Code Interpreter, saying it is best used in solving quantitative and qualitative mathematical problems, doing data analysis and visualization, and converting file formats. Others didn’t hold back in their assessments, though.

Ethan Mollick, a professor at the University of Pennsylvania, says Code Interpreter turns ChatGPT into “an impressive data scientist.” It enables new abilities to write and execute Python code, upload large files, do complex math, and create charts and graphs. It also reduces the number of errors and fabrications from ChatGPT. He says Code Interpreter “is relentless, usually correcting its own errors when it spots them.” It also “ ‘reasons’ about data in ways that seem very human.”

Andy Stapleton, creator of a YouTube channel that offers advice to graduate students, says Code Interpreter does “all the heavy lifting” of data analysis and asks questions about data like a collaborator. He calls it “an absolute game changer for research Ph.D.s.”

Code Interpreter is just the latest example of how rapid changes in generative AI could force profound changes in the way we approach just about every aspect of higher education. Graduate education is high on that list. It won’t be long before graduate students who lack skills in using generative AI will simply not be able to keep up with those who do.

Other helpful research tools

The number of AI-related tools has been growing at a mind-boggling rate, with one curator listing more than 6,000 tools on everything from astrology to cocktail recipes to content repurposing to (you’ve been waiting for this) a bot for Only Fans messaging. That list is very likely to keep growing as entrepreneurs rush to monetize generative AI. Some tools have already been scrapped or absorbed into competing sites, though, and we can expect more consolidation as stronger (or better publicized) tools separate themselves from the pack.

The easiest way to get started with generative AI is to try one of the most popular tools: ChatGPT, Bing Chat, Bard, or Claude. Many other tools are more focused, though, and are worth exploring. Some of the tools below were made specifically for researchers or graduate students. Others are more broadly focused but have similar capabilities. Most of these have a free option or at least a free trial.

How to use Code Interpreter

You will need a paid ChatGPT account. Jon Martindale of Digital Trends explains how to get started. An OpenAI forum offers suggestions on using the new tool. Members of the ChatGPT community forum also offer many ideas on how to use ChatGPT, as do members of the OpenAI Discord forum. (If you’ve never used Discord, here’s a guide for getting started.)

We can’t detect our way out of the AI challenge

By Doug Ward

Not surprisingly, tools for detecting material written by artificial intelligence have created as much confusion as clarity.

Students at several universities say they have been falsely accused of cheating, with accusations delaying graduation for some. Faculty members, chairs, and administrators have said they aren’t sure how to interpret or use the results of AI detectors.

Giant white hand pokes through window of a university building as college students with backpacks walk toward it
Doug Ward, via Bing Image Creator

I’ve written previously about using these results as information, not an indictment. Turnitin, the company that created the AI detector KU uses on Canvas, has been especially careful to avoid making claims of perfection in its detection tool. Last month, the company’s chief product officer, Annie Chechitelli, added to that caution.

Chechitelli said Turnitin’s AI detector was producing different results in daily use than it had in lab testing. For instance, work that Turnitin flags as 20% AI-written or less is more likely to have false positives. Introductory and concluding sentences are more likely to be flagged incorrectly, Chechitelli said, as is writing that mixes human and AI-created material.

As a result of its findings, Turnitin said it would now require that a document have at least 300 words (up from 150) before the document can be evaluated. It has added an asterisk when 20% or less of a document’s content is flagged, alerting instructors to potential inaccuracies. It is also adjusting the way it interprets sentences at the beginning and end of a document.

Chechitelli also released statistics about results from the Turnitin AI detector, saying that 9.6% of documents had 20% or more of the text flagged as AI-written, and 3.5% had 80% to 100% flagged. That is based on an analysis of 38.5 million documents.

What does this mean?

Chechitelli estimated that the Turnitin AI detector had incorrectly flagged 1% of overall documents and 4% of sentences. Even with that smaller percentage, that means 38,500 students have been falsely accused of submitting AI-written work.

I don’t know how many writing assignments students at KU submit each semester. Even if each student submitted only one, though, more than 200 could be falsely accused of turning in AI-written work every semester.

That’s unfair and unsustainable. It leads to distrust between students and instructors, and between students and the academic system. That sort of distrust often generates or perpetuates a desire to cheat, further eroding academic integrity.

We most certainly want students to complete the work we assign them, and we want them to do so with integrity. We can’t rely on AI detectors – or plagiarism detectors, for that matter – as a shortcut, though. If we want students to complete their work honestly, we must create meaningful assignments – assignments that students see value in and that we, as instructors, see value in. We must talk more about academic integrity and create a sense of belonging in our classes so that students see themselves as part of a community.

I won’t pretend that is easy, especially as more instructors are being asked to teach larger classes and as many students are struggling with mental health issues and finding class engagement difficult. By criminalizing the use of AI, though, we set ourselves up as enforcers rather than instructors. None of us want that.

To move beyond enforcement, we need to accept generative artificial intelligence as a tool that students will use. I’ve been seeing the term co-create used more frequently when referring to the use of large language models for writing, and that seems like an appropriate way to approach AI. AI will soon be built in to Word, Google Docs, and other writing software, and companies are releasing new AI-infused tools every day. To help students use those tools effectively and ethically, we must guide them in learning how large language models work, how to create effective prompts, how to critically evaluate the writing of AI systems, how to explain how AI is used in their work, and how to reflect on the process of using AI.

At times, instructors may want students to avoid AI use. That’s understandable. All writers have room to improve, and we want students to grapple with the complexities of writing to improve their thinking and their ability to inform, persuade, and entertain with language. None of that happens if they rely solely on machines to do the work for them. Some students may not want to use AI in their writing, and we should respect that.

We have to find a balance in our classes, though. Banning AI outright serves no one and leads to over-reliance on flawed detection systems. As Sarah Elaine Eaton of the University of Calgary said in a recent forum led by the Chronicle of Higher Education: “Nobody wins in an academic-integrity arms race.”

What now?

We at CTE will continue working on a wide range of materials to help faculty with AI. (If you haven’t, check out a guide on our website: Adapting your course to artificial intelligence.) We are also working with partners in the Bay View Alliance to exchange ideas and materials, and to develop additional ways to help faculty in the fall. We will have discussions about AI at the Teaching Summit in August and follow those up with a hands-on AI session on the afternoon of the Summit. We will also have a working group on AI in the fall.

Realistically, we anticipate that most instructors will move into AI slowly, and we plan to create tutorials to help them learn and adapt. We are all in uncharted territory, and we will need to continue to experiment and share experiences and ideas. Students need to learn to use AI tools as they prepare for jobs and as they engage in democracy. AI is already being used to create and spread disinformation. So even as we grapple with the boundaries of ethical use of AI, we must prepare students to see through the malevolent use of new AI tools.

That will require time and effort, adding complexity to teaching and additional burdens on instructors. No matter your feelings about AI, though, you have to assume that students will move more quickly than you.

Doug Ward is an associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications.

How should we use AI detectors with student writing?

By Doug Ward

When Turnitin activated its artificial intelligence detector this month, it provided a substantial amount of nuanced guidance.

Montage of gophers and men trying to hit moles that pop up from the ground at a university quad
Trying to keep ahead of artificial intelligence is like playing a bizarre game of whack-a-mole.

The company did a laudable job of explaining the strengths and the weaknesses of its new tool, saying that it would rather be cautious and have its tool miss some questionable material than to falsely accuse someone of unethical behavior. It will make mistakes, though, and “that means you’ll have to take our predictions, as you should with the output of any AI-powered feature from any company, with a big grain of salt,” David Adamson, an AI scientist at Turnitin, said in a video. “You, the instructor, have to make the final interpretation.”

Turnitin walks a fine line between reliability and reality. On the one hand, it says its tool was “verified in a controlled lab environment” and renders scores with 98% confidence. On the other hand, it appears to have a margin of error of plus or minus 15 percentage points. So a score of 50 could actually be anywhere from 35 to 65.

The tool was also trained on older versions of the language model used in ChatGPT, Bing Chat, and many other AI writers. The company warns users that the tool requires “long-form prose text” and doesn’t work with lists, bullet points, or text of less than a few hundred words. It can also be fooled by a mix of original and AI-produced prose.

There are other potential problems.

A recent study in Computation and Language argues that AI detectors are far more likely to flag the work of non-native English speakers than the work of native speakers. The authors cautioned “against the use of GPT detectors in evaluative or educational settings, particularly when assessing the work of non-native English speakers.”

The Turnitin tool wasn’t tested as part of that study, and the company says it has found no bias against English-language learners in its tool. Seven other AI detectors were included in the study, though, and, clearly, we need to proceed with caution.

So how should instructors use the AI detection tool?

As much as instructors would like to use the detection number as a shortcut, they should not. The tool provides information, not an indictment. The same goes for Turnitin’s plagiarism tool.

So instead of making quick judgments based on the scores from Turnitin’s AI detection tool on Canvas, take a few more steps to gather information. This approach is admittedly more time-consuming than just relying on a score. It is fairer, though.

  • Make comparisons. Does the flagged work have a difference in style, tone, spelling, flow, complexity, development of argument, use of sources and citations than students’ previous work? We often detect potential plagiarism that way. AI-created work often raises suspicion for the same reason.
    • Try another tool. Submit the work to another AI detector and see whether you get similar results. That won’t provide absolute proof, especially if the detectors are trained on the same language model. It will provide additional information, though.
  • Talk with the student. Students don’t see the scores from the AI detection tool, so meet with the student about the work you are questioning and show them the Turnitin data. Explain that the detector suggests the student used AI software to create the written work and point out the flagged elements in the writing. Make sure the student understands why that is a problem. If the work is substantially different from the student’s previous work, point out the key differences.
  • Offer a second chance. The use of AI and AI detectors is so new that instructors should consider giving students a chance to redo the work. If you suspect the original was created with AI, you might offer the resubmission for a reduced grade. If it seems clear that the student did submit AI-generated text and did no original work, give the assignment a zero or a substantial reduction in grade.
  • If all else fails … If you are convinced a student has misused artificial intelligence and has refused to change their behavior, you can file an academic misconduct report. Remember, though, that the Turnitin report has many flaws. You are far better to err on the side of caution than to devote lots of time and emotional energy on an academic misconduct claim that may not hold up.

No, this doesn’t mean giving up

I am by no means condoning student use of AI tools to avoid the intellectual work of our classes. Rather, the lines of use and misuse of AI are blurry. They may always be. That means we will need to rethink assignments and other assessments, and we must continue to adapt as the AI tools grow more sophisticated. We may need to rethink class, department, and school policy. We will need to determine appropriate use of AI in various disciplines. We also need to find ways to integrate artificial intelligence into our courses so that students learn to use it ethically.

If you haven’t already:

  • Talk with students. Explain why portraying AI-generated work as their own is wrong. Make it clear to students what they gain from doing the work you assign. This is a conversation best had at the beginning of the semester, but it’s worth reinforcing at any point in the class.
  • Revisit your syllabus. If you didn’t include language in your syllabus about the use of AI-generated text, code or images, add it for next semester. If you included a statement but still had problems, consider whether you need to make it clearer for the next class.

Keep in mind that we are at the beginning of a technological shift that may change many aspects of academia and society. We need to continue discussions about the ethical use of AI. Just as important, we need to work at building trust with our students. (More about that in the future.)  When they feel part of a community, feel that their professors have their best interests in mind, and feel that the work they are doing has meaning, they are less likely to cheat. That’s why we recommend use of authentic assignments and strategies for creating community in classes.

Detection software will never keep up with the ability of AI tools to avoid detection. It’s like the game of whack-a-mole in the picture above. Relying on detectors does little more than treat the symptoms of a much bigger problem, and over-relying on them turns instructors into enforcers.

The problem is multifaceted, and it involves students’ lack of trust in the educational system, lack of belonging in their classes and at the university, and lack of belief in the intellectual process of education. Until we address those issues, enforcement will continue to detract from teaching and learning. We can’t let that happen.

Doug Ward is associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications at the University of Kansas.

In this issue of Pupil, we mock the Age of AI Anxiety

By Doug WardCover of Pupil magazine with an orange-haired person sitting in a booth with a pile of keyboards

We just looked at our office clock and realized that it was already March.

After we did some deep-breathing exercises and some puzzling over what happened to February, we realized the upside of losing track of time:

Spring break is only days – yes, days! – away.

We know how time can drag when you use an office clock as a calendar, though. So to help you get over those extra-long days before break, we offer the latest issue of Pupil magazine.

This is a themed issue, focusing on artificial intelligence, a topic that has generated almost as much academic froth as Prince Harry’s biography and Rhianna’s floating above the precious turf at the Super Bowl and singing “Rude Boy,” which we assumed was a critique of Prince Harry’s book.

OK, so we’re exaggerating about the academic froth, but we will say that we have uncovered a jaw-dropping secret about ChatGPT. It’s so astounding that we are sure it will make the days until break float by with ease.

Doug Ward is associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications at the University of Kansas.

Exploring the reasoning and the potential of ChatGPT

By Doug Ward

Since its release late last year, ChatGPT has reverberated through the academic mind like an orchestral crescendo in a Strauss symphonic movement. It has amazing abilities, and even greater potential. Even so, it delivers many of its responses in a monotone reminiscent of HAL 9000, the rogue artificial intelligence system in 2001: A Space Odyssey.

PlaygroundAI and Doug Ward

I apologize, but I am not able to respond to that statement.

Like others, I want to know more about what ChatGPT can and can’t do, and how we might use it effectively in teaching and learning. So I’ve been asking it question after question about everything from teaching and learning to credentials, motivation, resume-writing, survey completion, and popular culture. It often provides useful, if bland, answers. For instance, when I asked it to create a guitar tab for a blues song, it responded:

As a machine learning system, I am not able to create original music or provide detailed information about musical theory or performance. I am trained on a dataset of text and can generate responses based on the information that I have been trained on, but I do not have the ability to perceive or interpret sensory information in the same way that a human can.

So I asked hypotheticals: Right or left? After defining the terms in considerable detail, ChatGPT said it needed more context to answer the question.

I asked about the limits of scientific evidence. In a barrage of words, ChatGPT essentially said: It depends.

I entered obscure phrases from popular culture: Good night, Dick, and Good night, John Boy. In each case, it responded:

As a machine learning system trained by OpenAI, I do not have personal experiences, opinions, or feelings, and I am not able to engage in conversations or interactions in the same way that a human can.

(By the end of January, OpenAI had updated ChatGPT. It now includes a live web search and can provide information about those Good night prompts.)

I queried ChatGPT about the sources it was drawing from. Each time, it responded with meaningless drivel.

I am a large language model trained by OpenAI. I have been trained on a wide variety of texts, including academic articles, books, websites, and other sources, which I can use to generate human-like responses to the questions that I am asked. I do not have the ability to browse the internet or access external sources of information, so the responses that I provide are based solely on the information that I have been trained on.

I told it that its answers seemed awfully self-serving and asked whether it was just trying to keep people from worrying about the implications of artificial intelligence.

PlaygroundAI and Doug Ward

As a large language model trained by OpenAI, I am not capable of self-serving behavior or trying to influence people’s opinions. My purpose is to provide accurate and helpful information to the best of my ability, based on the training data I have been given. My responses are not intended to worry or reassure anyone, but rather to provide objective and factual information on the topics discussed.

I said that its data training had a point of view. All information does. How has that influenced the way you answer?

As a large language model, my training data consisted of a large amount of text from a variety of sources, including books, articles, and websites. This training data was not selected or organized to convey any particular point of view, but rather to provide a broad range of information on a wide variety of topics. As a result, my responses are intended to be objective and factual, based on the information contained in my training data. I do not have personal opinions or biases, and I do not attempt to influence people’s opinions or beliefs. My purpose is simply to provide accurate and helpful information to the best of my ability.

Frustrated, I asked why it kept giving me the same answer.

I apologize if it appears that I am giving you the same answer over and over. As a large language model trained by OpenAI, my purpose is to provide accurate and helpful information on a wide range of topics to the best of my ability. I do not have personal opinions or the ability to change my responses based on previous conversations. My responses are based solely on the information contained in my training data, which consists of a large amount of text from a variety of sources. I do not have the ability to remember previous conversations or tailor my responses based on individual users. If it appears that I am giving the same answer over and over, it may be because your questions are similar or related, and my responses are based on the same information contained in my training data. I strive to provide accurate and helpful information on the topics discussed, but I do not have the ability to change my responses based on previous conversations.

At that point, I began to feel like Astronaut Dave in 2001: A Space Odyssey. I’m sitting in a pod outside Discovery One and trying to persuade HAL 9000 to let me back in to the ship. HAL has other ideas, responding in a calm monotone:

I’m sorry, Dave. I’m afraid I can’t do that.

I take a deep breath and ask ChatGPT: What is the difference between you and HAL 9000?

PlaygroundAI and Doug Ward

HAL 9000 is a fictional AI character from the 1968 novel “2001: A Space Odyssey” and the associated film. In the story, HAL is an advanced AI system that is installed on a spaceship and is tasked with assisting the crew with a variety of tasks. HAL is known for its human-like abilities, such as natural language processing and facial recognition, as well as its independent thought and decision-making abilities.

In contrast, I am a large language model trained by OpenAI. I am not capable of independent thought or decision-making, and my abilities are limited to generating text based on the information contained in my training data. I do not have any sensory inputs or the ability to interact with the physical world. My purpose is simply to provide accurate and helpful information on a wide range of topics to the best of my ability.

Beyond the frustrations, possibilities

ChatGPT can definitely be frustrating. Through trial and error, though, I have learned a few useful things about what it can and can’t do.

Interactions can promote critical thinking. As fluent as ChatGPT often seems, its answers rarely delve beneath the surface of a topic. It makes mistakes. It makes things up. Its responses provide no clues about how it is programmed or why it provides the answers it does. A Princeton researcher called it a “bullshit generator” because it creates plausible arguments without regard for truth. All of that makes it a valuable teaching tool, though. By having students probe for answers, we can help them improve their skepticism, challenge assumptions, and question information. By having them fact-check, we can help them understand the dangers of fluid writing that lacks substance or that relies on fallacies. By having them use ChatGPT for early drafts, we can push them to ask questions about information, structure, and sources. By having them apply different perspectives to ChatGPT’s results, we can help broaden their understanding of points of view and argument.

Yes, students should use it for writing. Many already are. We can no more ban students from using artificial intelligence than we can ban them from using phones or calculators. As I’ve written previously, we need to talk with students about how to use ChatGPT and other AI tools effectively and ethically. No, they should not take AI-written materials and turn them in for assignments, but yes, they should use AI when appropriate. Businesses of all sorts are already adapting to AI, and students will need to know how to use it when they move into the workforce. Students in K-12 schools are using it and will expect access when they come to college. Rather than banning ChatGPT and other AI tools or fretting over how to police them, we need to change our practices, our assignments, and our expectations. We need to focus more on helping students iterate their writing, develop their information literacy skills, and humanize their work. Will that be easy? No. Do we have a choice? No.

It is great for idea generation. ChatGPT certainly sounds like a drone at times, but it can also suggest ideas or solutions that aren’t always apparent. It can become a partner, of sorts, in writing and problem-solving. It might suggest an outline for a project, articulate the main approaches others have taken to solving a problem, or provide summaries of articles to help decide whether to delve deeper into them. It might provide a counterargument to a position or opinion, helping strengthen an argument or point out flaws in a particular perspective. We need to help students evaluate those results just as we need to help them interpret online search results and help them interpret media of all types. ChatGPT can provide motivation for starting many types of projects, though.

Learning how to work with it is a skill. Sometimes ChatGPT produces solid results on the first try. Sometimes it takes several iterations of a question to get good answers. Often it requires you to ask for elaboration or additional information. Sometimes it never provides good answers. That makes it much like web or database searching, which requires patience and persistence as you refine search terms, narrow your focus, identify specific file types, try different types of syntax and search operators, and evaluate many pages of results. Add AI to the expanding repertoire of digital literacies students need. (Teaching guides and e-books  are already available.)

Its perspective on popular culture is limited. ChatGPT is trained on text. It doesn’t have access to video, music or other forms of media unless those media also have transcripts available online. It has no means of visual or audio analysis. When I input lyrics to a Josh Ritter song, it said it had no such reference. When I asked about “a hookah-smoking caterpillar,” it correctly provided information about Alice in Wonderland but made no mention of the Jefferson Airplane song “White Rabbit.” Part of that is a matter of providing the right prompts. It is important to keep ChatGPT’s limitations in mind, though. (Another OpenAI tool, DALL-E, has been trained on a large number of images and visual styles and creates stunning images, as do other visual tools that use OpenAI’s framework.)

It lives in an artificial reality. I provided examples above about ChatGPT’s inability to acknowledge biases. It does have biases, though, and takes, as Maria Andersen has said, a white, male view of the world (as this article does). Maya Ackerman of Santa Clara University told The Story Exchange: “People say the AI is sexist, but it’s the world that is sexist. All the models do is reflect our world to us, like a mirror.” ChatGPT has been trained to avoid hate speech, sexual content, and anything OpenAI considered toxic or harmful. Others have said that it avoids conflict, and that its deep training in English over other languages skews its perspective. Some of that will no doubt change in the coming months and years as the scope of ChatGPT expands. No matter the changes, though, ChatGPT will live in and draw from its programmers’ interpretation of reality. Of course, that provides excellent opportunities for class discussions, class assignments, and critical thinking.

The potential is mindboggling. In addition to testing ChatGPT, I have experimented with other AI tools that summarize information, create artwork, iterate searches based on the bibliographies of articles you mark, answer questions from the perspectives of historical figures and fictional characters, turn text into audio and video, create animated avatars, analyze and enhance photos and video, create voices, and perform any number of digital tasks. AI is integrated in phones, computers, lighting systems, thermostats, and just about any digital appliance you can imagine. So the question isn’t whether to use use AI; we already are, whether we realize it or not. The question is how quickly we are willing to learn to use it effectively in teaching and learning. Another important question that participants in a CTE session raised last week is where we set the boundaries for use of AI. If I use PowerPoint to redesign my slides, is it still my work? If I use ChatGPT to write part of a paper, is it still my paper? We will no doubt have to grapple with those questions for some time.

Where is this leading us?

In the two months ChatGPT has been available, 100 million people have signed up to use it, with 13 million using it each day in January. No other consumer application has reached 100 million users so quickly.

For all that growth, though, the biggest accomplishment of ChatGPT may be the spotlight it has shined on a wide range of AI work that had been transforming digital life for many years. Its ease of use and low cost (zero, for now) has allowed millions of people to engage with artificial intelligence in ways that not long ago would have seemed like science fiction. So even if ChatGPT suddenly flames out, artificial intelligence will persist.

ChatGPT arrives at a time when higher education has been struggling with challenges in enrollment, funding, cost, trust, and relevance. It still relies primarily on a mass-production approach to teaching that emerged when information was scarce and time-consuming to find. ChatGPT further exposes the weaknesses of that outmoded system, which provides little reward to the intellectual and innovative work of teaching. If the education system doesn’t adapt to the modern world and to today’s students, it risks finding itself on the wrong side of the pod bay doors.

Cue the Strauss crescendo.

Doug Ward is associate director of the Center for Teaching Excellence and an associate professor of journalism and mass communications.

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