By Doug Ward

It was a simple idea.

Bring together a group of faculty members from around campus for guided discussions about diversity and inclusion. Guide them to think deliberately and openly about making their classroom practices and pedagogy more inclusive. Then help them create plans to take what they had learned back to their departments and help colleagues do the same.

That’s the approach behind Diversity Scholars, a program that CTE began last year with 11 participants. A second class of 10 began this fall. Funding for the program was provided by the Provost’s Office.

Participants say the sessions have helped them find new types of class materials, improved discussions about social identity, and helped them challenge students to think in new ways about the intersection of course content and race, gender and ethnicity. That hasn’t always been easy, they said, but it has been encouraging, enlightening and enjoyable.

Marta Caminero-Santangelo, a professor of English and a Faculty Fellow at CTE through last spring, leads Diversity Scholars. She said there had been pent-up demand for just these types of discussions, especially with tension over race, ethnicity, and gender roiling campuses across the nation.

“People just want the time to think about the issues and talk about them with colleagues and to be very deliberate and focused about those conversations,” she said. “I don’t know that there were any huge epiphanies. I think it was just helpful to sit around with a bunch of really enthusiastic, dedicated colleagues and talk about diversity and inclusion once a month.”

Caminero-Santangelo has been joined by Darren Canady, associate professor of English, and Shannon Portillo, associate professor of public affairs and administration, in guiding the program.

The goal of the program, Caminero-Santangelo said, is to help participants redesign a course or create a new course that more deliberately infuses diversity, equity and inclusion into the content, climate and discussions. The sessions, about one a month, focus on three areas: class content, pedagogy and class climate. The areas overlap, but they also connect with and reinforce each other. Each session involves readings, facilitated conversations and group work – essentially modeling the techniques that help students learn most effectively.

Caminero-Santangelo described the discussions about class content as an evaluation of the materials that the instructors use in their courses: “Am I drawing from a diversity of scholars, a diversity of voices, a diversity of readings? If my class content is STEM and it’s not specifically related to issues of diversity, are the examples that I’m using in class really addressing the diversity of human experience?”

The pedagogy sessions help participants understand the approaches that help all students learn effectively but that have shown to be especially effective with underrepresented groups. Those techniques include such things as clarity and transparency in expectations and grading; group work; universal design for learning; scaffolding of assignments; low-stakes assessments; and out-of-class work that frees up time for in-class problem solving and discussion.

The class climate discussions flow from the other two elements, Caminero-Santangelo said.

“If your class content is not diverse, that’s already sending a message to certain students that they’re not included and they’re not registering in the production of knowledge,” she said. “And if your pedagogy is not inclusive then students might feel alienated or silenced.”

Climate also includes smaller things, she said: creating ground rules for discussion, learning your students’ names, and handling hot moments in the classroom effectively.

Caminero-Santangelo said that none of the facilitators considered themselves to be experts, especially because participants came from several disciplines.

“We had maybe a little bit more familiarity with some of the topics, but we were certainly learning as we read and found resources and then incorporated those resources,” she said.

Participants have taken many approaches in rethinking their classes. For instance:

• Ward Lyles, assistant professor of public affairs and administration, added readings on overcoming an us-vs.-them mentality and added two class periods on community building. He also created a syllabus evaluation checklist for faculty members.
• Margaret Marco, professor of music, had her recital students choose performance pieces form outside the classical canon. She added a survey to the class, asking students how likely they were to play pieces by composers from underrepresented groups. She plans to follow up with the same survey at the end of class.
• Tim Hossler, assistant professor of architecture and design, plans to integrate material about cultural appropriation into a required design history course. He hopes to help students think more deeply about how diversity and design culture come together.
• Cécile Accilien, associate professor of African and African-American studies, added more material about masculinity in her course on gender in Africa. She has had class discussions about how religion and social identity affect social justice for those in the LGBTQ community, and her students will critique an African art exhibit at the Nelson-Atkins Museum of Art in terms of masculinity.
• Kristof Kuczera, professor of chemistry, created a pre-class quiz on diversity in chemistry, and added an exercise in which students research and write about chemists from underrepresented groups.

Participants will also share their experiences with colleagues and help them develop their own plans for being more deliberate about infusing diversity into their classes and curriculums. Caminero-Santangelo called this “a sort of spider web networking effect” that will expand the reach of the Diversity Scholars program.

For those who haven’t been able to participate in a program like Diversity Scholars, Caminero-Santangelo recommended small things that can help begin a process of enlightenment. There are many resources available to help instructors make their classes more welcoming for diverse populations, improve class conversations, and help students think more deliberately about inclusivity, she said. And it’s easy to find a colleague or two and have discussions.

“Take a baby step or two,” Caminero-Santangelo said. “Look at that syllabus tool. Read up on transgender identity and issues that the transgender community is facing on campus. You’re not going to be perfect at everything – ever. And you can’t necessarily change everything at once, but you could decide, ‘OK, in this one way I’m going to set some ground rules on the first day of class. I’m going to send a message that my classroom is an inclusive classroom and that I want to hear a variety of voices and I don’t want voices to be shut down.’ ”

In other words, simple actions can lead to big changes.

Doug Ward is the associate director of the Center for Teaching Excellence and an associate professor of journalism. You can follow him on Twitter @kuediting.

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.

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.

Nearly a decade ago, the Associated Press began distributing articles written by an artificial intelligence platform.

Not surprisingly, that news sent ripples of concern among journalists. If a bot could turn structured data into comprehensible – even fluid – prose, where did humans fit into the process? Did this portend yet more ominous changes in the profession?

I bring that up because educators have been raising many of the same concerns today about ChatGPT, which can not only write fluid prose on command, but can create poetry and computer code, solve mathematical problems, and seemingly do everything but wipe your nose and tuck you into bed at night. (It will write you a bedtime story if you ask, though.)

In the short term, ChatGPT definitely creates challenges. It drastically weakens approaches and techniques that educators have long used to help students develop foundational skills. It also arrives at a time when instructors are still reeling from the pandemic, struggling with how to draw many disengaged students back into learning, adapting to a new learning management system and new assessment expectations, and, in most disciplines, worrying about the potential effects of lower enrollment.

In the long term, though, we have no choice but to accept artificial intelligence. In doing so, we have an opportunity to develop new types of assignments and assessments that challenge students intellectually and draw on perhaps the biggest advantage we have as educators: our humanity.

Lessons from journalism

That was clearly the lesson the Associated Press learned when it adopted a platform developed by Automated Insights in 2014. That platform analyzes data and creates explanatory articles.

For instance, AP began using the technology to write articles about companies’ quarterly earnings reports, articles that follow a predictable pattern:

The Widget Company on Friday reported earnings of $x million on revenues of $y million, exceeding analyst expectations and sending the stock price up x%.

It later began using the technology to write game stories at basketball tournaments. Within seconds, reporters or editors could make basic stories available electronically, freeing themselves to talk to coaches and players, and create deeper analyses of games.

The AI platform freed business and financial journalists from the drudgery of churning out dozens of rote earnings stories, giving them time to concentrate on more substantial topics. (For a couple of years, I subscribed to an Automated Insights service that turned web analytics into written reports. Those fluidly written reports highlighted key information about site visitors and provided a great way to monitor web traffic. The company eventually stopped offering that service as its corporate clients grew.)

I see the same opportunity in higher education today. ChatGPT and other artificial intelligence platforms will force us to think beyond the formulaic assignments we sometimes use and find new ways to help students write better, think more deeply, and gain skills they will need in their careers.

As Grant Jun Otsuki of Victoria University of Wellington writes in The Conversation: “If we teach students to write things a computer can, then we’re training them for jobs a computer can do, for cheaper.”

Rapid developments in AI may also force higher education to address long-festering questions about the relevance of a college education, a grading system that emphasizes GPA over learning, and a product-driven approach that reduces a diploma to a series of checklists.

So what can we do?

Those issues are for later, though. For many instructors, the pressing question is how to make it through the semester. Here are some suggestions:

Have frank discussions with students. Talk with them about your expectations and how you will view (and grade) assignments generated solely with artificial intelligence. (That writing is often identifiable, but tools like OpenAI Detector and CheckforAI can help.) Emphasize the importance of learning and explain why you are having them complete the assignments you use. Why is your class structured as it is? How will they use the skills they gain? That sort of transparency has always been important, but it is even more so now.

Students intent on cheating will always cheat. Some draw from archives at greek houses, buy papers online or have a friend do the work for them. ChatGPT is just another means of avoiding the work that learning requires. Making learning more apparent will help win over some students, as will flexibility and choices in assignments. This is also a good time to emphasize the importance of human interaction in learning.

Build in reflection. Reflection is an important part of helping students develop their metacognitive skills and helping them learn about their own learning. It can also help them understand how to integrate AI into their learning processes and how they can build and expand on what AI provides. Reflection can also help reinforce academic honesty. Rather than hiding how they completed an assignment, reflection helps students embrace transparency.

Adapt assignments. Create assignments in which students start with ChatGPT and then have discussions about strengths and weaknesses. Have students compare the output from AI writing platforms, critique that output, and then create strategies for building on it and improving it. Anne Bruder offeres additional suggestions in Education Week, Ethan Mollick does the same on his blog, and Anna Mills has created a Google Doc with many ideas (one of a series of documents and curated resources she has made available). Paul Fyfe of North Carolina State provides perhaps the most in-depth take on the use of AI in teaching, having experimented with an earlier version of the ChatGPT model more than a year ago. CTE has also created an annotated bibliography of resources.

We are all adapting to this new environment, and CTE plans additional discussions this semester to help faculty members think through the ramifications of what two NPR hosts said was startlingly futuristic. Those hosts, Greg Rosalsky and Emma Peaslee of NPR’s Planet Money, said that using ChatGPT “has been like getting a peek into the future, a future that not too long ago would have seemed like science fiction.”

To that I would add that the science fiction involves a robot that drops unexpectantly into the middle of town and immediately demonstrates powers that elicit awe, anxiety, and fear in the human population. The robot can’t be sent back, so the humans must find ways to ally with it.

We will be living this story as it unfolds.

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.

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.

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?

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.

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.

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.

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).

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.

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.