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Can You Spot an AI-Generated Image? (Be Honest) BLOG

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Can You Spot an AI-Generated Image? (Be Honest) BLOG

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Glass Half Full Consulting | Explore AI Opportunities Today

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Can You Spot an AI-Generated Image? (Be Honest) — Glass Half Full Consulting | Explore AI Opportunities Today

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by Claire L. Brady, EdD A few weeks ago, my colleague and friend Josie Ahlquist asked me a simple question: “Do you think you can spot an AI-generated image?” My answer was immediate: No. Then I paused and added, “But I can spot a bad AI image.” That distinction matters. For a l

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Dr Brady on NextGenEDU AInnovation Podcast

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Dr Brady on NextGenEDU AInnovation Podcast

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Impact of AI on Work in Higher Education- EDUCAUSE

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Impact of AI on Work in Higher Education- EDUCAUSE

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The Impact of AI on Work in Higher Education

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In recent years, the higher education community has been exploring how AI tools are impacting the ways we learn, work, and live. Focus has largely bee

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#GenZ Podcast "Gen Z's Empowered Approach to using AI"

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#GenZ Podcast "Gen Z's Empowered Approach to using AI"

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Glass Half Full Consulting | Explore AI Opportunities Today

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“Gen Z’s Empowered Approach to Using AI” #GenZ podcast — Glass Half Full Consulting | Explore AI Opportunities Today

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Gen Z’s Empowered Approach to Using AI #Gen Z Podcast (WRKdefined Podcast Network) Dr. Claire Brady joins host Dr. Meghan Grace for an engaging conversation about how Generation Z is uniquely positioned to thrive in an AI-powered world. Together, they explore how Gen Z can build AI literacy

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Transformative Potential of AI: Recommendations for Student Affairs Leaders

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Transformative Potential of AI: Recommendations for Student Affairs Leaders

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The Transformative Potential of AI in Student Affairs: Recommendations for Student Affairs Leaders

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Within two short years, interest in the use of AI in student affairs has grown exponentially alongside broad access to tools powered by the new technologies. NASPA’s 2024 Top Issues in Student Affairs survey found that 45% of respondents ranked “Leveraging artificial intelligence to advance student learning and engagement” as a very important issue to their institutions. Several respondents also shared that they are working to address this issue by forming AI committees, considering how AI relates to pedagogy and academic integrity, and preparing students to interface with AI.

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Student Guide to Artificial Intelligence

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Homepage - Student Guide to Artificial Intelligence

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Learn about Artificial Intelligence (AI) and how it’s transforming higher education. This guide explores what AI is, its applications, and how it’s shaping careers.

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Login to LinkedIn to keep in touch with people you know, share ideas, and build your career.

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The Higher Education Generative AI Readiness Assessment, done in partnership with Amazon Web Services, a 2024 EDUCAUSE Mission Partner, is designed to provide a sense of your institution's preparedness for strategic AI initiatives. Use the assessment to develop an understanding of the current state and the potential of generative AI at your institution, and as an opportunity for discussion with others.

This assessment can be approached in multiple ways:

Individually: Any individual at an institution can complete the assessment. If the assessment will be completed in either or both of the team approaches, having one person fill it out and act as the "expert" to guide the process will likely benefit the process.

With your IT team: Use the self-assessment as a group exercise with a small group of IT staff to start to hear different perspectives on your organization. You might find that others see your institution's generative AI readiness differently. This exercise can also be a conversation starter for ways to better align IT efforts with institutional strategy and mission.

With a cross-functional team: The self-assessment can be a powerful tool for helping other institutional stakeholders understand generative AI readiness and for sparking conversation about it. Completing the assessment with a cross-functional group creates an opportunity to talk about institutional challenges and goals and how you might collectively address them.

We recommend that one person complete the self-assessment to get an understanding of the institution's readiness and become familiar with the elements of the assessment before using it with others.

EDUCAUSE Mission Partners
EDUCAUSE Mission Partners collaborate deeply with EDUCAUSE staff and community members on key areas of higher education and technology to help strengthen collaboration and evolve the higher ed technology market. Learn more about Amazon Web Services, 2024 EDUCAUSE Mission Partner, and how they're partnering with EDUCAUSE to support your evolving technology needs.

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Higher Education Generative AI Readiness Assessment

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The Higher Education Generative AI Readiness Assessment is designed to provide a sense of your institution's preparedness for strategic AI initiatives

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AI generative tools are a class of algorithms that generate new and unique outputs, such as text, images, or music, based on input data or parameters. These tools can be used in various disciplines to create new and innovative products, automate tedious tasks, and make predictions or simulations.

Recently, a new set of tools using these algorithms has become much more widely available. One of the most commonly discussed and popular is ChatGPT which has been developed by and provides text output based on a prompt. However, many other AI generative tools automate content generation across all different fields, music, voice, imagery, computer code, etc.

With the rise of availability and popularity, we see the usage of these tools by students to complete assignments, provide answers to tests, generate creative content, etc. Many possibilities exist for using and examining these tools related to education efforts and inquiry. However, across the country, instructors and instit utions see a rise in using these tools to engage in academic misconduct without proper citation or attribution to these tools.

The University of Utah, in partnership with the Center for Teaching Excellence, is developing and revis ing policies regarding using these tools for our students. Below are some strategies and resources that can guide your instructional practices and assignment development to help thwart academic misconduct and increase deep learning.

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AI Tool Guidance - Center for Teaching Excellence - The University of Utah

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AI generative tools are a class of algorithms that generate new and unique outputs, such as text, images, or music, based on input data or parameters. These tools can be used in various disciplines to create new and innovative products, automate tedious tasks, and make predictions or simulations.

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Columbia Considerations for AI Tools in the Classroom

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Columbia Considerations for AI Tools in the Classroom

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Considerations for AI Tools in the Classroom

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The following resource offers instructors a brief introduction to Artificial Intelligence (AI) Tools, specifically ChatGPT, along with several strategies they might consider for navigating or engaging with these tools in their courses.

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Digital Promise: Revealing an AI Literacy Framework for Learners and Educators

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Digital Promise: Revealing an AI Literacy Framework for Learners and Educators

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What is AI Literacy?

AI literacy builds on years of work in digital readiness, media literacy, and computational thinking. Just like these more familiar domains, AI literacy applies common 21st century skills such as communication, collaboration, critical thinking, and creativity. Building on definitions of AI and generative AI from the U.S. government, Digital Promise defines AI literacy as follows:
AI literacy includes the knowledge and skills that enable humans to critically understand, use, and evaluate AI systems and tools to safely and ethically participate in an increasingly digital world.
Our AI Literacy framework, pictured below, includes three components: Understand, Use, and Evaluate.

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Revealing an AI Literacy Framework for Learners and Educators - Digital Promise

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AI Literacy framework for educators to apply in K-12 contexts.

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Output of an Artificial Intelligence system from Google Vision, performing Facial Recognition on a ... [+]
Getty Images

Right before the holidays, two US congressional representatives (Rep Lisa Blunt-Rochester - D-Del and Rep Larry Bruschon - R-Ind) introduced the AI Literacy Act - a step towards ensuring AI Literacy as a critical component of Digital Literacy for every American. You can find a summary of the bill here.
What is AI Literacy?
The Merriam-Webster dictionary defines Literacy as " the quality or state of being literate" or having "knowledge or competence ". Artificial Intelligence is the umbrella term given to all technologies that enable computers to perform tasks that we normally think of as requiring "intelligence", or as being done by human brains. Artificial Intelligence is now in every aspect of our lives, creating both massive opportunity and substantial risk. The sheer pervasiveness of the technology means that every human being is affected - thereby making "AI Literacy" a critical requirement for every person on the planet.
Does this mean we all get need to get Ph.Ds?
No. One of the challenges of AI for the average person is that many of the AI descriptions available are very technically deep, often jumping into equations, mathematical theory, or code. While these topics are certainly critical for those wanting to develop new AI technology or operate in a technical capacity, this level of AI depth is not required for the average individual who may wish to interact with AI, make responsible decisions regarding AI use, or apply AI tools in their lives and work.
What is AI Literacy then?
Two past articles provide some perspective into how I see AI Literacy. The first advocates that AI Literacy should be comprised of the 4 C's - Concepts, Context, Capability and Creativity. In short - everyone should understand the fundamental concepts of how AI works (not at the mathematical or code level, but at the concept level), how AI works in our lives, ability to build AI if they choose or use pre-built AIs safely and responsibly, and finally the ability to imagine new solutions using these technological capabilities. The second article uses two analogies, cars and nuclear power, to illustrate the importance or AI literacy and show us how every human can acquire this literacy.
What is the AI Literacy Act?
The AI Literacy act advocates for amending the Digital Literacy Act to codify the importance of AI Literacy for everyone in the US. It further highlights the importance of AI Literacy for national competitiveness, highlights the importance of supporting AI literacy at every level of education, and requires annual reports to Congress on the state of this initiative.
What should you do?
Irrespective of who you are, becoming AI literate is a good thing. A great way to get started is to check out the articles above about AI Literacy, and how you can learn AI in a way that helps you in your life, work and business.

Follow me on Twitter or LinkedIn.

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The AI Literacy Act - What Is It And Why Should You Care?

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What is AI Literacy? How should you become AI literate? What is the AI Literacy Act? AI Regulations in the United States.

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EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report?

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Generative AI is arriving in higher education, but stakeholders are uncertain about its potential opportunities and challenges. One thing is clear: th

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8 universityes leveraging AI to drive student success (Digital Marketing Institute)

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8 universityes leveraging AI to drive student success (Digital Marketing Institute)

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Artificial intelligence (AI) has been around for decades, but the technology has evolved dramatically to disrupt industries - including the education sector - and AI in digital marketing is now a given.

The use of AI in higher education is now a reality, and even a necessity, as it will transform the ways institutions teach and how students learn. In fact, an IDC report found that 99 percent of U.S. higher education institutions believe AI will be key to driving competitiveness in the next few years.

While there are huge opportunities to use AI to improve efficiencies and drive student success, there's a flip side to the technology. It also poses new academic, ethical, and legal challenges that need to be considered.

So, how are third-level institutions using AI to rise above the competition and leverage it to drive student success?

In this blog, we'll look at 5 great ways (plus real-life examples) to help you understand the potential of the technology to:

1. Assess and Predict Student Success

2. Drive Personalized Learning

3. Boost Student Engagement

4. Answer Student Inquiries

5. Improve Student Retention

Assess & Predict Student Success

The ultimate goal of universities and colleges is to see students graduate with relevant skills that make them employable. That's why being able to assess and predict the success of a student is so important.

If a student is struggling or falling behind, technology that can help to intervene at an early stage can change the outcome for that person and also reduce the number of non-graduates at an institution.

1) Ivy Tech Community College: Project Student Success

In Indiana, Ivy Tech Community College conducted a pilot study using AI to get data from 10,000-course sections. By identifying 16,000 students at risk of failing in the first two weeks of the semester, the college assigned outreach workers to call each student and offer support.

By the end of the semester, 3,000 students were saved from failing - 98% of the contacted students obtained a C grade or better. Through Project Student Success, the college has already assisted 34,712 students.

"We had the largest percentage drop in bad grades (Ds and Fs) that the college had recorded in fifty years," said Lige Hensley, Chief Technology Officer, Ivy Tech. "That one phone call wasn't everything but it certainly made a bigger dent than we had ever seen."

2) University of Michigan: M-Write program

One time-consuming area of teaching is assessing student work and providing feedback. A range of tools are in development in this area for institutions, including M-Write, incubated by the University of Michigan.

M-Write is a technology designed to help faculty tackle writing activities at scale. It uses an algorithm to identify areas of a writing piece in which students are struggling and the issues that could contribute to weaker writing.

As part of the M-Write program, senior lecturer Brenda Gunderson introduced a series of writing prompts. These are targeted to elicit specific responses that indicate how well students grasp the concepts covered in class.

Students who participated in the program completed the writing assignments, submitted them, and received three of their peers' assignments for review. This automated system then used this data to create course-specific algorithms that could identify struggling students.

Drive Personalized Learning

One of the things educators are most excited about when it comes to AI is the potential to help personalize learning.

This not only offers ways for students to get one-to-one tutoring but also allows faculty to use AI to create lesson plans and identify students who may need additional help based on their performance and interactions.

3) Walden University: Julian

As early as 2018, Walden University had eyes on artificial intelligence as a way to improve its offering to students and trump its peers. With this in mind, Walden partnered with Google Cloud to develop an online tutor to offer personalized learning and student support, now known as Julian.

Steven Tom, Chief Customer Officer at Walden's parent organization Adtalem Global Education, says, "I had seen choreographed, scripted tutors earlier that year, but the real challenge was how to drive personalized, dynamic tutoring at scale. I wondered, how can we deliver education in places where students need more individualized help? As AI evolved, we started to believe there was a real opportunity to realize this vision."

Julian offers a 24/7 tutor and allows students to gain knowledge in their programs to reinforce concepts and identify learning gaps. The technology allows students to see a new set of activities every time they interact with the tool and it creates notes that can be referenced throughout the program.

As time passes and technology becomes more advanced, Julian will only continue to evolve to meet the personalized needs of students.

4) Khan Academy: Khanmigo

The Founder & CEO of Khan Academy believes that AI can spark the "greatest positive transformation education has ever seen."

This personalized tutoring system uses adaptive learning to give students access to an AI-powered personal tutor and allows teachers to lean on an AI teaching assistant. Welcome to Khanmigo!

The chatbot, Khanmigo, offers guidance to students on math, science, and humanities problems, a debate tool with suggested topics like student debt cancellation, and a writing tutor that helps students create a story.

Khanmigo also allows students to chat with a growing list of AI-powered historical figures from George Washington to Martin Luther King Jr., and literary characters like Hamlet.

Khanmigo chatbot

Boost Student Engagement

Many educators see the potential of using AI for student success primarily for driving engagement. It provides the opportunity to help students learn and new ways to create and consume content.

When an EDUCAUSE poll asked the higher education community how students use generative AI, the majority were unsure (probably due to the infancy of use) but many did identify key areas. Those in the top 4 were submitting generated material, ideating, and entertainment.

Students using generative AI

Let's look at how some universities and colleges are using it to drive student engagement.

5) Georgia State University: Pounce

While Georgia State's artificial intelligence-enhanced chatbot 'Pounce' was established as a tool to help incoming students get started in college (i.e. finances and registration) it's shown to boost student performance in classes.

The university found that students who used Pounce to receive texts about assignments, academic support, and course content were more likely to earn a B grade or higher. For first-generation immigrant students, it helped them earn final grades about 11 points higher than their peers.

"While we've seen that students from all kinds of different demographic groups benefit, what we've seen consistently is that students who struggle the most overall benefit the most from the chatbot intervention," said Timothy Renick, Founding Executive Director of the National Institute for Student Success at Georgia State.

6) California State University Polytechnic: Packback

At California State University Polytechnic, Pomona, faculty found that many students did not internalize material and share new ideas. Instead, they were writing questions and responses that repeated what was in their textbook or assignment.

Using Packback - an instructional AI platform for student writing and engagement - students wrote between 5 and 7 paragraphs of thoughtful discussion questions and responses that analyzed primary sources and shared new ideas.

Packback tool

"There is no other software that provides so much while also improving student writing and participation," said Ayana Jamieson, Professor at California State University Polytechnic. "Packback Instruct encourages students at every level to think critically and engage with both their assigned readings and their peers in meaningful ways."

Packback has also developed a Writing Lab feature to use interactive writing coaching to help students improve their writing and build AI literacy skills.

"67% of people have a good understanding of what AI is, but only half know which products and services use AI " Ipsos survey

Answer Student Queries

For many institutions, student queries can take up a lot of time and often go unanswered in the evenings or weekends if there's no staff on duty. AI technologies like digital colleague Ida and voice and chat platform n-powered are changing that, by answering questions on-demand.

Universities and colleges need to be able to answer questions at all stages, whether that's before enrollment or when someone is a student. Let's look at some great examples of how institutions are using AI technology to deal with student queries.

7) University of Murcia

The University of Murcia in Spain implemented an AI-powered chatbot called Lola to assist students with inquiries about the campus and academic programs.

The results were impressive as the chatbot managed to communicate with 4,609 students and show proficiency by answering over 38,708 questions correctly more than 91 percent of the time. Keep in mind that this was before the widespread adoption of ChatGPT, so we can only see these metrics improve.

Using Lola not only provided the university with a way to help students outside of standard office hours but it allowed staff to free up the time they usually spent responding to queries to do other tasks.

Improve Student Retention

The retention rate for public, four-year higher education institutions is 76%, according to the National Student Clearinghouse. That means approximately 1 in 4 students left their university or college to study elsewhere - or nowhere at all.

The solution? Improve your targeting so that the resources and energy to keep students can be put towards those most likely to enroll. AI-powered technology is proving to be valuable in this capacity.

8) Nova Southeastern University: Aible

Nova Southeastern University in Florida had valuable data in-house but wasn't sure how to use it properly to improve student retention or optimize student welfare.

With that in mind, it reached out to Aible, an AI technology that uses models and prescribed interventions. Using the software, the university identified the students who were most likely to leave so it could target and prioritize its efforts for the most at-risk students.

Nova Southeastern University using Aible

As a r esult of using Aible, Nova Southeastern University achieved a 17 percent reduction in student attrition in just 15 days.

"During a one-hour meeting, we went from a raw dataset to exploring insights in the data automatically highlighted by Aible, to creating and even deploying a predictive model. The collaboration with academic and financial aid advisors helped us further optimize the models and made them more useful." - Don Rudawsky, VP of Institutional Effectiveness at Nova Southeastern University.

What are the Challenges of using AI in higher education?

As we mentioned at the start of this article, while artificial intelligence can offer huge opportunities for universities and colleges, it can also pose challenges.

Some of those include a bias in AI models due to biased data, lack of personalization, the potential for errors, plagiarism and a fear of job losses for faculty.

So, when using AI, it's important for institutions to factor in these challenges to ensure that the technology enhances outcomes, supports teachers and benefits learners rather than becoming a hindrance.

"Technology offers the prospect of universal access to increase fundamentally new ways of teaching," said the Graduate School of Education at Stanford University, Dean Daniel Schwartz at the AI+ Education Summit. "I want to emphasize that a lot of AI is also going to automate really bad ways of teaching. So [we need to] think about it as a way of creating new types of teaching."

Conclusion: AI in Higher Education

Artificial intelligence has the potential to revolutionize the way teachers teach and students learn. The technology also has the potential to create 97 million jobs by 2025 according to The World Economic Forum.

The rapid adoption of AI will result in major changes to the curriculum as institutions need to integrate AI into their programs from journalism (chatbots can now create content easily and to scale) to healthcare (are virtual nurses a reality?) to be relevant and create job-ready graduates.

However, automated work will never replace the creativity and innovation that a human can bring. The role of universities and colleges is to teach people how to use AI and provide the skills required of a modern workforce to drive efficiency and engagement.

Keep your curriculum industry aligned and up to date

The Digital Marketing Institute helps universities and colleges keep their marketing curriculum relevant and up-to-date by collaborating with representatives from 30+ leading brands, from Google to IBM, and Microsoft.

Learn more about DMI University Partnership & Accreditation today.

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8 Universities Leveraging AI to Drive Student Success | Digital Marketing Institute

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The use of AI in higher education is now a reality, and even a necessity, as it will transform the ways institutions teach and how students learn. Explore how 8 universities are using AI to drive student engagement, offer personalized learning and improve retention.

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How AI and Data could personalize Higher Education

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How AI and Data could personalize Higher Education

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Artificial intelligence (AI) is transforming various industries, and academia is no exception. With AI-powered tools like ChatGPT, DALL-E, and Elicit revolutionizing the way students complete assignments, universities across the country are grappling with the ethical implications and practical challenges of integrating these tools into their curriculums. We'll explore the current AI use policies at universities nationwide, delving into the more polarizing policies and discussing how software or tooling can help facilitate responsible AI adoption in the world of education.

Polarizing AI Use Policies

AI use policies at universities range from embracing AI tools in certain contexts to restricting or even prohibiting their use. While some institutions see AI as an inevitable part of the future, others are centrally focused on deterring cheating. This has led to two major categories of AI use policies:

Open and inclusive policies: These universities encourage the responsible use of AI tools for idea generation, brainstorming, and information synthesis. They emphasize the importance of skill development, ethical considerations, and proper attribution while allowing students to explore the potential of AI technology. These institutions often have specific guidelines for AI-generated content, including clear identification and citation requirements.

Restrictive and prohibitive policies: These universities prioritize academic integrity and worry about the potential for AI tools to facilitate cheating. As a result, they have stricter policies that limit the use of AI-generated content, sometimes even banning it outright. These institutions often have stringent consequences for policy violations, including academic penalties, course failures, or even expulsion.

Most AI Policies Lean Towards Responsible & Ethical Use

It seems as though there is consensus among most Institutions that AI is here to stay. Inclusive AI use policies in universities focus on embracing the potential of AI tools and integrating them into the educational experience in a responsible and ethical manner. These policies often encourage the use of AI for idea generation, brainstorming, and information synthesis while emphasizing skill development, ethical considerations, and proper attribution. Institutions with inclusive policies usually provide specific guidelines for using AI-generated content, including clear identification and citation requirements. By fostering an environment where students can explore AI technology responsibly and ethically, these universities aim to prepare students for the future while maintaining academic integrity.

Why Detection Won't Solve The Problem

While some universities establish strict consequences for policy violations, it's important to consider whether AI detection of AI-generated content is a zero-sum game. History of these kinds of policies in the educational system shows that individuals who are intent on cheating will cheat (we call these 'dedicated cheaters'), and with enough determination find ways to circumvent detection systems. Instead of focusing solely on punitive measures, universities may benefit from fostering an environment that encourages responsible AI use and promotes a culture of academic integrity.

Themes Found Across Inclusive AI Use Policies

1. Emphasis on original work: Many of the policies stress that assignments must be the student's own original work, and using AI-generated content without proper attribution is considered plagiarism.

2. Guidelines for acceptable AI use: Some policies outline specific situations where AI tools are permitted, while others prohibit their use altogether. When allowed, students are often advised to use AI tools for brainstorming or idea generation rather than directly submitting the AI-generated content.

3. Clear attribution and citation: If AI-generated content is used, many policies require students to clearly indicate which parts were generated by AI and provide proper citations, including the specific AI tool used.

4. Penalties for violating AI policies: The consequences for violating AI use policies range from receiving a zero on the assignment to potential disciplinary action or failing the course. Some policies mention that violations may be treated as academic misconduct or plagiarism.

5. AI tool limitations: Several policies mention the limitations of AI tools, such as the potential for inaccurate, incomplete, or biased information. Students are warned to use AI tools cautiously and to verify the information they generate.

6. Encouragement for responsible AI use: A few policies encourage the responsible use of AI tools while acknowledging their potential benefits for idea generation and learning. These policies often provide guidance on when and how to use AI tools effectively and ethically.

7. Academic integrity: Many policies highlight the importance of upholding academic integrity when using AI tools. Students are reminded that submitting AI-generated work without proper attribution is a violation of academic integrity policies.

8. Instructor discretion and guidance: Some policies defer to the instructor's discretion in determining acceptable AI tool usage or provide specific guidance on when AI tools may be used in the course.

9. Adapting to new technologies: A few policies recognize the evolving nature of AI tools and their potential impact on future careers. They also acknowledge the need for society to determine when and how these tools should be used in educational settings.

10. AI as a learning tool: AI, including ChatGPT and image generation tools, is considered an emerging skill and an important pedagogical opportunity in education. Learning to use these tools responsibly is essential.

11. Importance of academic honesty and attribution: Many policies emphasize the need for proper attribution when using AI-generated content. Failure to acknowledge AI assistance may result in a violation of academic honesty policies.

12. Recognition of AI limitations: AI tools have limitations, such as providing inaccurate information or requiring refined prompts to produce quality results. Students are advised to verify any facts or data provided by AI tools.

13. Instructor guidance and permissions: Some policies require students to obtain permission from the instructor before using AI tools in assignments. This ensures that students are aware of appropriate use and adhere to course expectations.

14. Co-creation of class agreements: In some cases, students and instructors co-create agreements on AI tool usage, ensuring equal access, understanding of benefits and limitations, and alignment with academic integrity policies.

15. Ethical considerations: Several policies highlight the importance of maintaining high ethical standards when using AI tools in educational settings.

16. Suggestions for appropriate AI use: Policies provide examples of how AI can be used effectively, such as refining research questions, brainstorming ideas, drafting outlines, or improving grammar and style.

The Role of Software and Tooling in Promoting Responsible AI Use

As universities adapt to the rise of AI tools, software and tooling can play a vital role in promoting responsible AI integration in academic settings. Here are a few ways that technology can help:

1. Attribution and citation assistance: Software can be developed to automatically generate proper citations for AI-generated content, making it easier for students to comply with attribution requirements.

2. Activity logs: Version control gives us a good mental model for understanding how a product takes shape. Similar things can be done in tools to track progress over time.

3. Insights & Scoring: AI-powered tools can provide Educators with the insights needed to assess progress, as well as confidence scores around a student's proof-of-work, which is central to the learning process.

4. Flexibility: Software designers can choose to build features that provide more power to the professor, including which language models are used and provide controls for limiting their use to follow acceptable standards set by the Professor.

5. Responsible copyright and data use policies: Universities care a lot about how services may use student data, especially their own original writing. Software developers need to consider students' rights and develop ethical and transparent data use policies to ensure these rights are represented.

6. Content quality guidance: AI-powered tools can be used to help evaluate the quality, accuracy, and relevance of both human-generated and AI-generated content, encouraging students to critically examine their work, the work born from AI, and develop essential skills they'll carry into the future.

7. AI-powered feedback: Tools that provide feedback on AI-generated content can help students understand the limitations and biases inherent in AI-generated work, enabling them to make informed decisions about when and how to use AI tools.

8. Educator support: Software and tooling can also assist educators in monitoring AI usage, guiding students in responsible AI adoption, and adjusting their teaching methods to address the challenges posed by AI integration.

9. Honor pledges: Building in commitments made by students to uphold the university's policies on academic integrity and the responsible use of AI tools. By signing this pledge when creating or submitting work, students affirm that their work is produced in an authorized manner, and adheres to the institution's guidelines. The pledge serves as a reminder of the importance of ethical behavior in the academic environment and encourages students to take responsibility for maintaining the highest standards of academic honesty.

Conclusion

As the world of education continues to grapple with the rise of AI tools, it's clear that striking the right balance between embracing AI's potential and addressing its limitations will be key to shaping the future of education in the age of artificial intelligence. By examining the more polarizing policies and considering the role of software and tooling in promoting responsible AI use, universities can better navigate the integration of AI technology into their curriculums and foster an academic environment that upholds integrity, encourages critical thinking, and promotes ethical considerations.

‍Disclaimer: AI was used to edit this post and generate the cover image.

Wilson Tsu is the Founder of PowerNotes and since 2017, has been committed to helping institutions improve their students' research, writing, and learning process.

Over the years, our academic partners have been an essential voice in helping to steer and mold PowerNotes into the robust kit of tools that it is today.

Today, we're seeing an opportunity to shift how schools and universities approach student assessment in the face of AI. If you'd like to be a part of these key conversations around the future of AI in education please reach out and let us know!

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The Great AI Debate in Education: How Universities Are Navigating the AI Revolution | PowerNotes

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We explore the early policies being developed around AI in academia. Most policies prioritize responsible and ethical use of AI, such as clear attribution and citation requirements, while some universities focus on detection and punishment of policy violations.

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Behind the Hype: Capturing the Potential of AI and gen AI in tech, media, and telecom

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Behind the Hype: Capturing the Potential of AI and gen AI in tech, media, and telecom

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Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom

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This collection presents the top insights on gen AI from McKinsey and its AI arm, QuantumBlack, providing a detailed examination of the significant opportunities and challenges it offers for leaders looking to steer their organizations into the future.

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Beyond Efficiency: A Human-first AI Adoption Strategy

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Beyond Efficiency: A Human-first AI Adoption Strategy

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When we first started researching the use of AI in nonprofit organizations, we described the key benefit of AI as the "dividend of time." As we wrote in The Smart Nonprofit: Staying Human-Centered in an Age of AI, when used responsibly, AI can help nonprofit staff reduce the countless hours they spend doing labor-intensive and often energy-draining tasks-so they can focus instead on mission-critical work. With many nonprofit workplaces taking up generative AI over the past year, what costs and benefits are staff seeing?

No longer an abstract concept, many nonprofit staff are experiencing the "dividend of time" as they adopt tools such as Copilot and ChatGPT for scheduling and managing meetings, processing email, writing drafts, summarizing long reports, analyzing data, and creating efficiencies in other common workflows.

Researchers have measured the time savings created by generative AI. A recent Harvard Business School study compared two groups of consultants-one using AI tools and the other not-performing the same workplace tasks such as writing, analysis, and strategy. The group that used AI tools on average completed 12% more tasks, 25% faster, with 40% higher quality. Other studies have found similar results, including this study from MIT that found that generative AI tools substantially increased average productivity for professional writing tasks.

The human risks of over-reliance on AI tools

There is a risk, however, that the availability of low-cost AI tools and the productivity gains they provide could lead to an over-reliance on the technology. Nonprofit leaders could use them to have staff do the same tasks they've always done, just faster, or shift jobs from full-time work to part-time or contract work. Yet using AI adoption primarily to maximize time and cost efficiency will only exacerbate the burnout crisis in the sector.

One area with a potential high risk of over-reliance on AI tools is internal collaboration and communication. We're beginning to hear about how AI tools could erode workplace relationships if used only to speed up work. Increasing staff interactions with AI tools could reduce human interactions internally. This will be especially detrimental for junior staff, who need human-to-human feedback to learn and improve their job performance.

If staff interact more with AI tools than with one another to complete tasks, it reduces the well-documented benefits of informal conversations of building an effective workplace culture, onboarding new staff, and improving staff retention. We know from workplace research that having a strong sense of community and connection with workplace peers has many benefits to well-being, including mental health.

Putting humans first

There is an alternative approach: a human-first AI adoption strategy. Here are the core elements:

Redefining "productivity." Centering humans begins with expanding the definition of productivity-as the ability to both complete existing tasks (or learn new tasks) faster and better and reallocate time from transactional activities to activities that deepen relationships. There is a balance to be struck internally-in which humans and AI each do what they do best, with humans always in charge of the final product or action. This is called "co-botting" or "co-piloting" with AI.

The most important skill nonprofit leaders can learn and teach their teams is to know when to use AI and when to use human skills. This process begins with learning technical skills such as "prompting," which involves constructing the right questions to ask generative AI tools in order to get useful answers. It also involves learning when and how to use AI responsibly, based on acceptable use guidelines. More importantly, increasing human-to-human engagement must be a part of any AI adoption strategy.

Disrupting job tasks, not eliminating jobs. During the recent Microsoft for Nonprofits "Global Nonprofit Leaders Summit," Meg Garlinghouse, LinkedIn's head of social impact, and Karin Kimbrough, chief economist, shared some striking data from the LinkedIn Economic Graph about how the technology will specifically impact jobs in the nonprofit workplace: 12% of nonprofit job tasks will be "augmented or changed," and 39% of nonprofit job tasks will be "disrupted."

Disrupting job tasks is not the same as eliminating jobs. It means that the job description and tasks might shift and require using or learning different skills. For example, a fundraiser whose job is "disrupted" may spend much less time on desk prospect research and have more time to talk to donors and other cultivation strategies.

Investing in "soft skills." A human-first AI adoption strategy also requires an investment in "soft skills." Leaders are going to need a talent development strategy for upskilling employees. In other words, if using AI tools can help save an employee 10 hours per week, this is an opportunity to provide training to do unique tasks that require human empathy and intelligence, such as giving and receiving feedback, mentoring for improved performance, listening and reflection, and building psychological safety. Prioritizing and fostering human skills among staff will not only help the organization become more human-centered, it will help alleviate the anxiety AI adoption can cause staff.

LinkedIn data suggests that the most in-demand skills for nonprofits that adopt AI will be person-to-person communication, continuous learning and adaptability, and problem-solving skills. While generative AI tools can do many things, they do not yet possess human reasoning or empathy. In other words, working effectively with AI tools requires more human-centered and -centering skills, not less.

The use of AI in the nonprofit workplace will create deep and profound changes. Nonprofit leaders must dig into the technology now, understand what it is and how it works, how it will change job tasks, and, most importantly, how to ensure that people always come first. Leading with a human-first approach to encourage the use of AI in a way that improves workplace relationships will create a renewed sense of purpose, and ultimately more impact.

Photo credit: gorodenkoff via Getty Images

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Beyond efficiency: A human-first AI adoption strategy

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Candid Blog | Beth Kanter and Allison Fine weigh nonprofit AI tools' risks and benefits, illustrating why a human-first AI adoption strategy is key to unlocking efficiency sustainably and responsibly.

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How AI, ML, and Simulation Work Together

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How AI, ML, and Simulation Work Together

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How AI, ML, and Simulation Work Together

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Ansys Chief Technology Officer Dr. Prith Banerjee explains how better, faster decisions are made possible when AI, ML, and simulation align.

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McKinsey Implementing AI with Speed and Safety

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McKinsey Implementing AI with Speed and Safety

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Implementing generative AI with speed and safety

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Generative AI poses both risks and opportunities. Here’s a road map to mitigate the former while moving to capture the latter from day one.

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How AI is Personalizing Education for Every Student

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How AI is Personalizing Education for Every Student

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How AI Is Personalizing Education For Every Student

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Using AI for personalized education can help you to understand each student’s needs and goals and adjust lessons accordingly.

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Transforming community colleges to transform their communities by accelerating more equitable student outcomes requires institutional leaders to adopt a new data mindset that is fully focused on building better access to college, creating momentum for timely completion, and ensuring equitable mobility for graduates along career paths paying a family-sustaining wage.

In this episode, we hear from three students who use AI to help them with their coursework. They also share their thoughts about how AI may serve their careers once they graduate.

Gartner Group Vice President Steve Wenc talks about ways campuses can harness data for student success.

Students are using artificial intelligence and other tools to assist them in their academic careers. Three students share their viewpoints on the tools they use and how using these tools helps them in their coursework and prepares them for the professional world.

Gateway math and statistics courses tend to have high dropout and failure rates. The University of North Carolina at Charlotte is responding with adaptive design.

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Contributing Editor: Kathe Pelletier, Director, Teaching and Learning Program, EDUCAUSE

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EDUCAUSE: The AI-First Student Experience

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EDUCAUSE: The AI-First Student Experience

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Key Takeaways

Students dealing with the size and complexity of a college or university and trying to navigate its systems can become frustrated or even drop out if the barriers within its required processes hamper their progress.

Thoughtful application of artificial intelligence can support advisors and faculty in guiding students to graduation and help students themselves stay on track and take full advantage of the resources available.

Using AI technologies to mediate the interaction between the student and the institution will not only eliminate many obstacles to student success, but will empower the professionals who have made it their life's work to help students succeed.

Higher education institutions employ a broad spectrum of strategies to improve the basic metrics of student success. Some approaches rely on sophisticated data analytics to proactively identify students in need of intervention; others focus on developing students' social and emotional resources to help them persist in meeting their educational goals.

As designers, we see the "user experience" students have with an institution as a frequently overlooked area of opportunity in higher education. Colleges and universities can be both intimidating and bewildering. Students rely on institutional services and resources to contend with financial, career, and lifestyle decisions that many have never dealt with before. Moreover, these support services can themselves be confusing and hard to navigate.

In our experience institutions aspire to serve students as individuals, to guide them on their journeys toward outcomes they find both fulfilling and enriching. We see great potential in artificial intelligence (AI) technologies to enable both the institution to better support students, and students to fully engage with the institution. Thoughtful application of AI will augment the ability of advisors and faculty to meaningfully help students, and help students both stay on track and take full advantage of the resources available to them. We created a short video to show what an AI-first approach to the student-experience might look like:

When students encounter barriers to critical resources, information, and transactions, they can become frustrated, and their confidence in the institution erodes. For nontraditional learners, the outcomes can be worse. In our research with online adult learners and with first-generation students in the Rio Grande Valley of Texas, we saw that any friction in the experience can result in disastrous outcomes.

While most colleges and universities make services and resources available digitally, we believe they should now consider emerging AI technologies to capitalize on those investments and offer an experience that will ably guide students to completion. It's time to start moving toward an "AI-first" approach to student engagement.

A promising example is provided by an edtech start-up called AdmitHub. AdmitHub has built an AI-powered chatbot that guides students through a university's existing FAQ or admissions resources (see figure 1). The chatbot communicates with students directly through the native text messaging app on their phone. Georgia State used AdmitHub to significantly decrease summer melt (where admitted students fail to enroll for their first semester) through proactive outreach.

The AI-First Model

Building systems and experiences around AI's ability to interpret natural language, detect context, and anticipate needs will empower colleges and universities to unite their services and proactively engage students. The fundamental elements of this model include conversational interfaces, contextual user interaction, nudges, and modes.

Conversational Interfaces

One of the most engaging elements of chatbots is their reliance on conversation as the primary interface. Conversational interfaces like these let students interact with often complex services via messaging, something they do every day. In this case AI interprets the natural language user input (the text) and associates it with the correct response. By offering such a simple interface, institutions can help students find the specific information they need without searching through a complex online student portal.

Other industries with similarly complex services and support are exploring this approach. For example, to improve the experience of buying and owning a car, Volvo has piloted an AI-based chatbot that assists customers through the buying process. Certain new Volvo vehicles come with an app that uses the same technology to deliver a set of premium services, such as on-demand roadside refueling.

Contextual User Interfaces

Conversational interfaces have significant limits. For one, discoverability is severely limited. Without indicators of what information and functionality are available to them, users can only ask for what they know about. Second, even marginally complex interactions, such as narrowing a list of options, are quite slow in dialogue form. What might take no more than two clicks of a button requires several exchanges of questions and answers.

By blending the conversational model with more traditional graphic user interfaces (buttons, lists, images, etc.), institutions can serve students the information and functionality they need in precisely the moment they need them. As an example, a student would receive a notification that a lab fee is due. A specialized payment applet embedded in that notification would let the student pay the fee right away. Figure 2 shows an example of a contextual graphic interface.

Nudges

AI's most promising ability is interpreting context. Just as natural language processing algorithms leverage statistical models to interpret speech, algorithms can be trained to understand student success needs. By integrating student data such as behavior, pace, and progress with institutional policies, it's possible to anticipate intervention needs and either nudge individual students toward their next, best action, or escalate to an advisor. In our video, for example, a student attempts to drop a class. Because they are a freshman, the system prompts them to connect with their advisor and serves them a scheduling applet.

Modes

Defining a set of discrete use modes will help students take advantage of the diversity of resources available to them. Modes can be tied to location or tasks. As a student approaches a campus shuttle stop, for example, the system would surface routes and schedule information. For a distance-learning student researching a topic, the system would present a user interface for browsing relevant publications. Handoffs between modes help students focus on the task at hand (see figure 3).

What Would an AI System Need?

While it's tempting to think of AI-based systems as monolithic intelligences, in reality they are assemblies of specialized algorithms linked to other capabilities. As an example, Google's Photos app uses algorithms trained in natural language processing, image recognition, facial recognition, and others to enable users find a photo they're looking for simply by describing it out loud. The key building blocks necessary for our model are intent discernment, intelligent routing, existing data, and trained models.

Intent Discernment

The AI-first model depends on natural language processing (NLP) to determine a student's intent, which it then maps to corresponding actions, scripts, or modeled responses. As Shane Mac of Assist [http://www.assi.st/] points out in a recent Medium post, there are lots of ways to say the same thing, and locking people into decision trees of canned questions often resembles filling out a form more than it does an actual conversation (see figure 4).

A better way to parse intent is to listen for both intent and parameters that map to the corresponding action within the flow of a conversation. For example, saying "I want to renew a library book" and "I want to renew Gulliver's Travels" should initiate the same book renewal action, and the system should take into account Gulliver's Travels as a parameter for that action. The subsequent response from the system should be to fill in missing parameters: "I'll renew Gulliver's Travels for you. When do you want to return it?" Figure 5 illustrates mapping natural language to actions and parameters, or Random Access Navigation.

Intelligent Routing

Once intent has been mapped to an action, the system needs to complete the action by routing the student to a resource, returned result, or additional piece of functionality. Sometimes this might be a question targeted at getting a missing parameter (such as the book title). Other times it might be a simple confirmation that the action has been completed, or it might initiate a more complex action via an in-line applet.

Intelligent routing also applies to intents that don't correspond to an action in the system. If the system can't perform an action, it should at least recognize what the student wants to do and contextually direct them to an appropriate person or resource (see figure 6).

One of the most encouraging things about this model is that it can be layered atop a university's existing infrastructure, serving as a bridge between siloed services and resources. In other words, it can make the most of what's already there. Similarly, we imagine that an AI-first student experience could be implemented with a phased approach that initially aims to connect and coordinate existing resources, and then progressively integrates them (see figure 7).

Existing Data

Another important aspect involves leveraging existing data about individual students, the student body, and other resources so that the system knows where a student is in their college career and delivers the right piece of functionality at the appropriate time. This capability could simplify tasks like paying a bills by pre-populating payment information on file so that the student can pay with one touch. Or the system might generate a personalized orientation schedule to ease a freshman into her first week (see figure 8). The lack of accessibility of this data is currently one of the greatest limiting factors to serving students effectively.

Trained Models

Existing data could also be used to build predictive models about student interests, campus activity, and all sorts of other things. Models like these are critical to providing recommendations, generating personalized content, and mapping particular intents to the most relevant actions, resources, or services. This becomes especially important when the system tries to account for changes over time - new training data yields new recommendations, new personalized content, and new routing logic.

Final Thoughts

True solutions for student success are systemic and require orchestration of people and resources from every facet of the institution. We believe that using AI technologies to mediate the interaction between the student and the institution will not only eliminate many meaningful obstacles to student success, but will liberate the energies of professionals who have made it their life's work to help students succeed.

Acknowledgments

This article is a slightly revised version of our post " Imagining an AI-First Student Experience," Big Tomorrow, Medium, March 3, 2017. The AdmitHub team have provided inspiration as well as invaluable guidance and ideas in our work as expressed in this article.

Aric Cheston is a founder of Big Tomorrow.

Drew Stock is lead designer for Big Tomorrow.

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Thoughtful application of artificial intelligence can help advisors and faculty help students, and help students themselves stay on track and take ful

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AI in Consulting: How to Future-proof your Consulting Business

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AI in Consulting: How to Future-proof your Consulting Business

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AI in Consulting: How to Future-proof Your Consulting Business

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Learn how to get ahead of the curve by using generative AI to boost your productivity and future-proof your independent consulting business.

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Blogs focusing on executive leadership, AI in higher education, and professional excellence.

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This is part 2 of 2 of a blog focused on how to best use Generative AI tools to update and improve your application materials for an...

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Inside Higher Ed | Higher Education News, Events and Jobs

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In the latest annual survey by Inside Higher Ed, participating provosts said the vast majority of their institutions have not yet published policies governing the use of artificial intelligence (AI), even as its power and disruptive potential increase. And most institutions haven't reviewed curriculum to ensure it will prepare students for AI's rise in the workplace.

The email survey, conducted in February and March in conjunction with Hanover Research, asked about the multiple challenges that face higher education following a tumultuous year. At a time when diversity, equity and inclusion programs are under attack, around 70 percent of the respondents rated race relations on their own campuses as good or excellent-yet only about 30 percent said the same for higher education as a whole. And, as Israel's war in Gaza continues and a presidential election looms, nearly four in 10 provosts said world events have stressed their institutions' speech policies to the point they may need revisions.

These are just some results of the wide-ranging 2024 Survey of College and University Chief Academic Officers, which also polled provosts about typical academic and university management issues that have become atypically fraught in a time of tight budgets, enrollment declines and political heat. Should degree programs be cut? How viable is tenure? Has employee turnover increased? The 331 provosts who returned full or partial surveys-respondents were about evenly split between public and private nonprofit institutions-gave their views on those questions along with a range of challenges higher education faces.

Artificial Intelligence

As with Inside Higher Ed's 2024 presidents' poll, this marks the first time our annual provosts' survey asked about AI. The questions come at an important moment: Generative AI has advanced rapidly over the past year, raising the specter of widespread abuse by both students and faculty members who can use it to produce journal articles that read like unassisted humans wrote them.

Are provosts and their institutions ready to meet this moment? By one measure, not yet: Only one in five said their college or university has published a policy governing the use of AI, including in teaching and research.

Gregor Thuswaldner, provost at Whitworth University and president of the Association of Chief Academic Officers, said that percentage does seem low "at first glance." But he noted that "for many institutions, their plagiarism policies kind of govern also generative AI-that is, students are forbidden to use anything that is not their own work." Whatever the case, many provosts may soon catch up: 63 percent said an AI policy is in the works at their institutions.

Only about one in seven provosts said their colleges or universities had reviewed the curriculum to ensure it will prepare students for AI in their careers. Thuswaldner said that number needs to rise. "AI is here to stay, and we cannot put our heads in the sand," he said. "Our world will be completely dominated by AI and, at this point, we ain't seen nothing yet."

Nearly half of the provosts said they're moderately concerned about generative AI's threat to academic integrity, with a further 26 percent saying they're very or extremely concerned. George Justice, the University of Tulsa's provost, said he has one dean who's "terrified about the implications for academic integrity" and wants to ban the use of generative AI on any assignments in her college-while another dean is surveying every course to see whether it's maximizing AI's use to prepare students for the future.

Justice said it's good for AI policies to allow "widely divergent approaches" in classrooms. But he cautions that faculty members who merely ban AI and don't change how they teach in response to it are going to face widespread cheating. "Not because students are evil," he said, but because they have a "bullshit detector, and if professors are pretending that the real world isn't happening then they become cynical about the requirements in a class."

Thuswaldner advocated for more in-class writing and focusing on "process rather than product." If AI can produce what universities are asking students to do, he said, "then we're probably asking the wrong question."

Provosts don't see AI merely as a threat to be kept at arm's length. In fact, only 2 percent of respondents said they're not at all enthusiastic about its potential benefit to their institutions' capabilities, and 45 percent say they're using it for chatbots and virtual chat assistants. Employees appear to be clamoring for help with the technology-nine in 10 provosts said faculty and staff members have requested more training related to generative AI developments.

The provosts said they're responding to that demand: Nearly 80 percent said their colleges and universities have offered training in response to faculty concerns or questions within the last year and a half.

Campus Speech and the 2024 Election

Raucous campus protests over the war in Gaza, faculty members' controversial social media posts about the violence and even congressional interrogations of university presidents over antisemitism have drawn the nation's eyes upon academe. And the war is just one issue in the upcoming elections featuring a rematch between Joe Biden and Donald Trump for the presidency. "There will be more student protests and, yeah, this will be certainly a fall to remember on campuses," Thuswaldner said.

More than half of the provosts surveyed said they're very or extremely concerned about this year's election results affecting their institution's climate for free inquiry. And then there's all the time leading up to the election, when Thuswaldner said he's sure there will be more campus unrest. "I don't think you have to be a prophet to make that prediction," he said.

More on the Survey

Inside Higher Ed's 2024 Survey of College and University Chief Academic Officers was conducted by Hanover Research. The survey included 331 presidents from public and private institutions for a margin of error of 5.04 percent. A copy of the free report can be downloaded here.

On May 15, at 2 p.m. E.T., Inside Higher Ed will present a webcast to discuss the results of the survey. Please register here.

This Inside Higher Ed Survey of College and University Chief Academic Officers was made possible by support from ACUE, Watermark, Coursedog, Interfolio from Elsevier, Honorlock and The Honor Society of Phi Kappa Phi.

So how do provosts assess the free speech situation, as of now? More than 60 percent of respondents did rate the climate for open inquiry and dialogue on their campuses as good or excellent, but only 27 percent said the same for higher education writ large. "It is kind of amusing that we all think we're doing it right," Justice said. But what "open inquiry" means is itself contested, he noted, and there's so much contention "over what doing it right might mean."

For instance, Justice said he and his institution adhere to the Chicago Principles, the University of Chicago's widely adopted free expression statement. The statement says, in part, that "concerns about civility and mutual respect can never be used as a justification for closing off discussion of ideas, however offensive or disagreeable those ideas may be to some." However, Justice said other provosts might justify a different approach by saying "we've created structures that exclude outrageous speech that actually distorts what open inquiry at a university is supposed to be."

Despite the overall rosy view of open inquiry on their own campuses, nearly 40 percent of provosts said world events have stressed their institutions' speech policies to the point they may need revisiting. And about three-quarters of respondents said their colleges and universities have already taken some action to teach students and employees about the importance of free speech and to prepare them to engage with those they disagree with. "Pluralism and democracy go hand in hand, that's exactly what we need to prepare students for," Thuswaldner said.

We also asked provosts whether their institutions had clear response plans for responding to various speech-related concerns. Among the findings: Only 36 percent said they had a clear policy to respond to student complaints about professors' extramural speech, and just 31 percent said they had a policy to deal with online trolling or other outside attacks on professors-a not-uncommon situation that faculty members have faced after posting about the war in Gaza.

Diversity, Equity and Inclusion

About 60 percent of respondents said the Supreme Court decision last year banning race-based admissions will decrease student racial diversity in higher education as a whole. But only 11 percent said the same for their own specific institutions. This may indicate that some institutions that previously practiced race-conscious admissions have found workarounds or otherwise adapted to the decision. But there's also the fact that only a sliver of institutions, typically highly selective, ever practiced race-conscious admissions in the first place.

Most provosts-slightly more than 70 percent-rated race relations on their own campuses as good or excellent, while just 2 percent said they were poor. Yet, asked about higher education as a whole, slightly fewer than 30 percent said race relations were good or excellent, and more than 70 percent characterized them as fair or poor.

Justice said these findings tell him that "many of us believe we are making good-faith efforts on our campus, regardless of whether we have DEI offices or not." Republican lawmakers have targeted DEI offices and programs at public institutions in multiple states, with Florida banning the use of state and federal money on DEI programming and Texas outright banning the programs statewide.

While racism persists in society, young people are "fairly harmonious ... at least on our campuses," Justice said. "I think that we have a kind of a local perspective, but a national fear, and I'm hopeful that the local perspective is the true perspective," he said.

Thuswaldner said that provosts may see their own campuses through "rose-colored glasses." With DEI being curtailed in the South and elsewhere, he said "I think provosts have reason to be pessimistic that things will not necessarily develop in a positive way going forward."

About a tenth of provosts said they had scaled back diversity and inclusion efforts beyond admissions in response to the high court decision or other pressure coming from lawmakers. But that percentage grows to 41 percent when just looking at the responses from provosts at Southern public institutions.

Academic Program Cuts

The West Virginia University Board of Governors' vote last year to eliminate of all of its foreign language degree programs and other offerings sent a shockwave through academe, which hadn't seen a public flagship research university carry out that level of cuts in modern memory. But multiple smaller institutions have also eliminated offerings in response to declining enrollment and other challenges.

More cuts may be coming. More than 40 percent of provosts who participated in the survey agreed or strongly agreed that their institutions should cut their number of academic programs by the end of next academic year, while 35 percent said their college or university was actually likely to do so. Meanwhile, the prospects for new or expanded academic programs appear dim: About 75 percent of provosts agree or strongly agree that most "new" program spending at their institutions will have to come from reallocating dollars.

Faculty and Staff Turnover

The targeting of tenure, shared governance and certain academic disciplines by conservative lawmakers and university trustees has fed concerns that faculty members will flee states where this is occurring, though it's still unclear whether this is coming to pass. Nationwide, there's also another possible source of attrition: Inflation has cut into pay, and workers want raises.

Around two-thirds of provosts who responded to the survey said their institutions were seeing higher-than-usual turnover rates for staff specifically. Thirty-five percent of provosts said the same of faculty members. And most-60 percent-said recruiting faculty members is harder now than before the pandemic.

A greater share of respondents from private nonprofit colleges and universities reported seeing higher employee turnover, compared to provosts at public institutions. At the same time, a lower rate of private nonprofit provosts said their institutions had recently changed compensation, and a lower rate said their institutions were doing more than before the pandemic to retain and engage faculty members.

Across higher education, only about 40 percent of provosts say their college or university is doing more now to retain and engage faculty members than before COVID-19.

"What you described there doesn't surprise me at all," said Todd Benson, executive director of the Collaborative on Academic Careers in Higher Education at Harvard University. His data shows academics are starting to look more broadly at careers outside of academe, where they can find better compensation and work-life balance. Faculty members are facing increasing demands, he noted, including more teaching expectations, a longer process for publications and grants and a higher bar to earn tenure.

"This generation of faculty that are coming up now are more cognizant of the fact that getting a tenure line is a bit more of a unicorn than it used to be, and so they don't have the same sense of opportunity and security that they may have had 10 or 20 years ago," Benson said. He said "they're going into it with much more open eyes and so they're being much more flexible with themselves."

Tenure or Long-Term Contracts?

The proportion of higher education faculty members who are not on the tenure track has been growing for decades. Respondents gave an interesting mix of answers on the future of tenure.

Provosts were polled on how "viable" tenure is within their institutions, and were asked to think when answering about "how well tenure works at your institution along with its likelihood to endure."

More than 60 percent responded that tenure is very or extremely viable. Half of the provosts said it's very or extremely important to their institutions' health. Yet more than half also said they favor a system of long-term contracts for tenure-track and tenured faculty members over the existing tenure system.

Provosts may in principle support tenure but in practicality be seeking more flexibility than it traditionally provides. Benson said long-term contracts allow university leaders to make cuts more easily when budgets get tight, but they also send "a tacit signal to faculty that you may not be here" in the future. "Tenure was the institution's promise to faculty, and the more we erode that, the less that faculty feel like they have a commitment from the institution," he said.

Noting the declining public perception of higher education alongside the increased work demands, Benson said "more and more faculty are not feeling that sense of value." Only 22 percent of provosts said their institution is very good at recruiting and retaining talented faculty members.

Non-tenure track faculty members don't seem to be going away soon. Nearly two-thirds of provosts said their institution will rely just as much on them in two years from now or in the near future. And a further 23 percent said they expect to rely on them more.

About 46 percent of provosts agreed or strongly agreed that graduate programs admit too many doctoral students given the job market. Those most in agreement with this sentiment were provosts at public doctoral institutions, where 61 percent felt the programs admit too many people.

Recognizing Mental Health Needs

Huge majorities of the responding provosts said they're either somewhat or very aware of the general state of mental health across various campus constituencies: 97 percent when it comes to undergraduates and 95 percent for faculty members.

Forty-five percent of provosts said their institution offers leaders optional training to help them support faculty and staff members' mental health, but only 20 percent said training was required.

Course Materials Models

The provosts' views on textbooks may be welcome news for cash-strapped students: Around 60 percent agreed or strongly agreed that free, open educational resources are of high enough quality that they should be used in most general education courses. And 70 percent of the respondents said their institution had adopted these materials.

About a third of respondents said their institutions had adopted an "inclusive access" model, where the learning materials are digital-first with an opt-out option.

Institutional Effectiveness and the Provost's Role

Provosts gave their institutions high marks at providing quality undergraduate education: 72 percent said their colleges and universities were very effective, and another 27 percent said somewhat effective. But they were more muted, while still majority positive, about the job their institutions were doing in preparing students for work: 57 percent very effective, 41 percent somewhat effective.

About 40 percent of provosts somewhat or strongly agree that their jobs focus more on financial management than academics. But nearly 90 percent say, nonetheless, that they're glad they pursued administrative work.

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Provosts' survey shows need for AI policies, speech worries

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Many institutions are not yet prepared to help their faculty members and students navigate artificial intelligence. That's just one of multiple findings from Inside Higher Ed's annual survey of chief academic officers.

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Duke University AI Guidelines & Considerations

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Duke University AI Guidelines & Considerations

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The Duke Community Standard is still the university's strongest measure against plagiarism and cheating, but the issue around whether and how AI-use falls into these categories is a complex one. As a baseline, faculty can emphasize that along with use of uncredited ideas and content created by persons, AI-generated content falls under the definition of plagiarism at Duke.

We encourage all faculty to thoughtfully consider their stance on AI use, even if you adopt existing policy language. In this shift in higher education and the rapidly changing AI market, standardized, one-size-fits-all, AI policies probably will not be sustainable in the long term. They may also not account for the varying stances instructors will take regarding AI use in their classrooms. Furthermore, because generative AI is becoming more ubiquitous, you'll need to consider your personal stance on AI in your own work and in your classrooms. Establishing an AI policy for your class allows you to have meaningful discussions with students on this topic. Being specific about how AI is or isn't allowed makes the rules clear for students and faculty if there are academic integrity violations as well.

The following guidelines for developing an AI policy for your syllabus include examples of generative AI policies from instructors, universities, and centers of teaching and learning. For the most part, they have been curated from a crowd-sourced document that we encourage you to explore to find AI policies developed by instructors in your field and which represent your level of comfort with the use of AI. Other sources are cited in the text itself.

Do your homework

Kevin Gannon (Queens College in Charlotte) argues in a recent article titled "Should I Write an AI Policy" that faculty need to read in a balanced approach to the subject before settling on their syllabus policy. He offers a list of varied sources that provide a primer on generative AI, pros and cons of AI use, and a grounding in how generative AI can be incorporated into teaching.

We encourage you to try one of the tools listed below to see what they are like. A good way to start exploring is to enter the prompts that you give students for an assignment into the AI tool, and see what the tool returns. Keep an open and curious mindset when considering whether such tools could be helpful to some or all of your students. There are two easy entry options. They both have cost-free options.

1. Open AI is the company that developed the content generator ChatGPT and image generator DALL-E2. Open AI offers internal documentation with training tips and

2. Try Google's products Bard and Workspace which offer a comparable experience to Open AI.

Share the rationale behind your policy

As you define your individualized AI syllabus statement, your rationale will no doubt be grounded in the intellectual work of the course, your discipline or your understanding of critical thought. Communicating these principles can be an important aspect of how you discuss your policies with your students. What will students lose (or gain) by using generative AI? Why did you choose your particular policy? What do you want students to understand about AI and their intellectual development?

In one extensive policy example from Joel Gladd (University of Western Idaho) he mentions two guiding principles in a course that will allow the use of AI: "1) Cognitive dimension: Working with AI should not reduce your ability to think clearly. We will practice using AI to facilitate-rather than hinder-learning. 2) Ethical dimension: Students using AI should be transparent about their use and make sure it aligns with academic integrity."

Lis Horowitz (Salem State University) shares a practical reason behind a zero tolerance policy for generative AI in their writing course. "Since writing, analytical, and critical thinking skills are part of the learning outcomes of this course, all writing assignments should be prepared by the student. Developing strong competencies in this area will prepare you for a competitive workplace. Therefore, AI-generated submissions are not permitted and will be treated as plagiarism."

By defining the idea of integrity, Megan McNamara (UC Santa Cruz) points out what is at stake when we talk about academic honesty and personal growth. As she states, "Integrity - other people's perception of your word as true - is one of the most valuable assets you can cultivate in life. Being attentive to integrity in academic settings allows others to trust that you have completed work for which you are taking credit." Her course allows students to use AI in limited ways and this rationale sets the foundation for this use.

Support AI literacy

While a never-ending list of rules won't encourage students to read your policy thoroughly, students do need more guidance than we might expect. Generative AI is a brand-new source for information and the rules for citation and the general use of AI are still under formation. It is important to highlight what will happen if you suspect an academic integrity violation and what the consequences will be if the allegation is true. But in addition, students need to understand how to become AI literate. Here are a few considerations for helping them:

When students have information about the limitations, bias, and inaccuracies of generative AI, it underscores why AI is not a shortcut to good results. Ethan Mollick (Harvard's Wharton School) frames it this way, "[d]on't trust anything it says. If it gives you a number or fact, assume it is wrong unless you either know the answer or can check in with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand." The library at University of Northwestern St. Paul has a guide for students with an overview of AI's shortcomings, plus information on how to verify sources and double check AI responses.

If you explain to students how to cite content generated from AI sources it reduces the cognitive load of students unsure of how to act within the Duke Community Standard. This may encourage them to cite and note AI use more thoroughly. Many academic style guides have already formulated citation practices for generative AI. Monash University has curated an extensive list of the various AI citation formats.

You have the option to personalize your approach to citations. Some educators are instructing students to submit a transcript of the conversation with generative AI as an appendix to their work. Another alternative might be a reflective piece as a companion to an assignment, as Pam Harland (Plymouth State University) has done by providing guiding questions for students: "What was your prompt?" "Did you revise the AI model's original output for your submission?" "Did you ask follow-up questions?" "What did you learn?"

Define acceptable use

You may decide that you see room for the use of generative AI models in your courses. If so, clarify for your students the circumstances in which use of AI is allowable. For example, you may be comfortable with students using tools like Grammarly to help with spelling and grammar. Kim Sydow Cambell (University of Texas) offers an example policy that expands upon the tasks that students can ask AI to perform:

"Because the effective use of Artificial Intelligence (AI) tools is increasingly important to the work of technical communicators, their use is sometimes required or allowed in course assignments. AI tools can support a content creator during all phases of their work:

pre-writing: before content is created, writers can use some tools to research topics, collect genre samples, brainstorm ideas, craft outlines, etc.

drafting: some tools support the generation of content

revising: after content is generated, many tools aid writers in identifying and altering style/tone, spelling, punctuation, grammar, etc."

From Howard University of Law, Howard Bruckner explains to students what tasks are acceptable and underscores the students' responsibilities for ethical use. "Generative AI tools can be invaluable for generating ideas, identifying sources, synthesizing text, and starting to understand what is essential about a topic. But YOU must guide, verify and craft your work product; do not just cut and paste without understanding."

You may discover that if you allow AI use in different assignments, specific guidelines may need to accompany each assignment.

Explore the continuum of policies

You may wish to review these policy statements from the University of Delaware's Center for Teaching and Assessment of Learning, which distill the four basic approaches that instructors can take in their syllabi. We invite you to consider them as starting points in your exploration of what your AI policy will be.
Use prohibited
Students are not allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course. Each student is expected to complete each assignment without substantive assistance from others, including automated tools.
Use only with prior permission
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course if instructor permission is obtained in advance. Unless given permission to use those tools, each student is expected to complete each assignment without substantive assistance from others, including automated tools.
Use only with acknowledgement
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course if that use is properly documented and credited. For example, text generated using ChatGPT-3 should include a citation such as: "Chat-GPT-3. (YYYY, Month DD of query). "Text of your query." Generated using OpenAI. https://chat.openai.com/" Material generated using other tools should follow a similar citation convention.
Use is freely permitted with no acknowledgement
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course; no special documentation or citation is required.

About AI detection software

We don't recommend AI detection software for three main reasons.

1. The products are unreliable. The latest research on AI detection software from MIT highlights the false positive and negative rates. OpenAI (the company behind ChatGPT) recently withdrew its own detection software due to the software's unreliability.

2. Detection software is biased against non-native speakers, as research from Stanford shows.

3. As AI changes, detection software cannot keep up.

If you decide to use detection software, share that information with your students ahead of time. Results from the software should not be the only measure of whether students have cheated. Students can be encouraged to use detection software prior to handing in work to check for originality (although they should be warned of the limitations as well).

Another option is to craft a policy that warns students they will need to speak with you and defend their work if plagiarism is suspected. As Liza Long (College of Western Idaho) explains "[i]f I suspect that you have used ChatGPT, and you have not included the required citation and reflection, then you will need to meet with me either in person or through Zoom to talk about the assignment. This conversation will include knowledge checks for course content."

If you'd like to discuss your AI policy in more depth, please reach out to learninginnovation@duke.edu. You can also explore our central resource on AI in education.
Looking for other university syllabus policies? Please refer to the Office of Undergraduate Education menu of sample language for many other policies. You can also refer to Learning Innovation's template for help crafting the rest of your syllabus.

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Artificial Intelligence Policies: Guidelines and Considerations - Duke Learning Innovation

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The Duke Community Standard is still the university's strongest measure against plagiarism and cheating, but the issue around whether and how AI-use falls into these categories is a complex one. As ...

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Conor Grennan's AI Mindset Quick Start Guide

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Conor Grennan's AI Mindset Quick Start Guide

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Welcome to the AI Mindset community - check your inbox to confirm your sign up!

To get you started, here's my FREE Quick Start Guide to Generative AI. You're going to dig it.

We're going to learn together, friends.

Stay curious.

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Welcome to the AI Mindset community - check your inbox to confirm your sign up! To get you started, here's my FREE Quick Start Guide to Generative AI. You're going to dig it. We're going to learn together, friends. Stay curious.

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"The AI genie is out of the bottle," said Cynthia Breazeal, a professor of media arts and sciences at the Massachusetts Institute of Technology. "It's not just in the realm of computer science and coding. It is affecting all aspects of society. It's the machine under everything. It's critical for all students to have AI literacy if they are going to be using computers, or really, almost any type of technology" in their daily lives.

The question of what makes a person AI literate is evolving. But it involves delving into technical questions like: "What's happening under the hood? How does it work? How does it impact me and the world around us?" said Bryan Cox, the lead computer science specialist at the Georgia education department.

AI literacy is something that every student needs exposure to-not just those who are planning on a career in computer science, experts argue.

"When we all went to school, we learned how the light bulbs work or how the digestive system works or how photosynthesis works," said Hadi Partovi, the CEO of Code.org, which works to expand computer science offerings in K-12 schools. "And you teach those things to everybody, not just the botanists or the electricians or the surgeons. You learn [these things] to have a better understanding of your world. But most people don't know how the internet works, how a smartphone works, how an algorithm works, and they definitely don't know how AI works."

Here's how to begin developing AI literacy, according to experts and educators:

1. Why it's vital to have a basic understanding of how AI works

Grasping the technical aspects of AI-how the technology perceives the world, how it collects and processes data, and how those data can inform decisions and recommendations-can help temper the oftentimes inaccurate perception that AI is an all-knowing, infallible force, experts say.

"We need to demystify how these systems work, how you build these things, at a grade-appropriate kind of level, because there's so much hype and confusion," Breazeal said. "People talk about these things like a conscious ether that surrounds us. [Students] need to understand that we're really talking about [something] people actually make and control and engineer."

To be sure, some students can delve deeper into the inner workings of AI than others, she added. "If you're an AP Computer Science kid, you're going to get a little more in the technical weeds about it," Breazeal said.

But all students must grasp that the decisions that AI makes-whether it's to recommend a particular pair of boots to an Amazon customer or to flag a job applicant as a promising prospect for an employer-aren't driven by the same kind of nuanced and creative reasoning a human can perform.

Instead, it's "really advanced guesswork," Partovi said. "AI right now is based on probability and statistics. It makes errors. It can spread misinformation. It can have bias. Understanding how it actually works underneath is important so that people recognize the weaknesses it has."

And Partovi noted that students still need to learn how to write, even though the writing skills of technologies like ChatGPT are more sophisticated than most people might have imagined. The same idea applies to learning computer coding, he said. "The superpowers of AI are only available to people who know how to write or how to code," Partovi said.

Children in early-elementary school, for instance, could start with a simple lesson in which they identify the human organs-ears, eyes-involved in hearing and seeing and then find their technological counterparts -microphone, camera-on a digital device.

Students also need to understand how biases in the data that's used to train AI can allow the technology to continue to perpetuate discriminatory policies unless humans recognize the problem and do something about it.

2. Give students hands-on opportunities to understand how the technology works

One hands-on lesson for more advanced students: Give them a flawed historical dataset on which to train an AI system, Partovi said. For instance, students could create a program that gives suggested salary ranges for a company's employees.

If that program is informed using data in which women are paid less than men for doing the same job, the technology will probably propose lower salaries for female employees than for male workers. But if women are at salary parity with men in the dataset, the results will be more equitable.

There are other ways of illustrating how human subjectivity can penetrate AI's decisionmaking. For instance, the technology organizes information to draw conclusions, a less clear-cut process than students may initially realize.

To illustrate this, students in a middle school AI course that Georgia is piloting play a game in a physical classroom that initially asks students to decide whether something is edible or not, Cox said. Depending on their decisions, students choose where to stand in the room.

Some of the answers are obvious: A metal stop sign is not edible, for instance.

But there can be disagreement on a word like "chicken," which vegetarians in the class may claim is not edible.

From there, the game begins adding more and more categories and subcategories to the list of options, mimicking how AI algorithms can work.

During the activity, "great conversations erupt," said Cox from the Georgia education department. "And we'll talk to the students about how the computer is making those same determinations" based on the viewpoints of the majority of people it interacts with. And those determinations are often faulty ones.

3. Discuss and analyze ethical questions about the technology

Once students are aware that humans-and not some sentient robot-are behind how these tools analyze and communicate, they can think about them in a broader context.

And that raises all kinds of important and interesting ethical issues. In Georgia's middle school AI course, for instance, students might consider a case of passengers going to sleep in the backseat of a self-driving car while it continues along the road.

Then they'll unpack questions without easy answers: "What are the legal implications of that? Do we need to stop them from doing that? Or is AI to the point where you can actually sleep in the backseat of a car and let it drive itself?" Cox said.

Students consider questions like: Should all states ban facial-recognition software until it becomes more accurate? Or could it be used for some purposes but not others?

They can also discuss data-privacy concerns, including the implications of having devices such as smart speakers-Amazon's Alexa, Google Assistant, and Apple's Siri, among others-in homes. Teachers will pose questions: "How is that impacting you? Is it listening to you all the time? Is it sending information back to Google or Amazon all the time?" Cox said. "That's really one of the biggest things we wanted to be able to do is to help them guard against the unintended consequences of AI."

4. How to interact effectively with AI

Students will need to practice using AI tools to get information, the same way previous generations learned the card-catalog system to navigate the library.

ChatGPT, for instance, is deeply influenced by the prompt a user gives it, said Torrey Trust, an associate professor of learning technology in the College of Education at the University of Massachusetts Amherst.

For instance, a student could tell the tool to "'write about the American Revolution.' It's gonna give a very textbook response. And then you could say, 'well, write about 15 women who shaped the American Revolution or draw connections between 15 women today and the American Revolution,'" she said. "The way you prompt the tool completely changes the output and your thinking and learning."

The stakes in the real world, of course, can be a lot higher than a classroom assignment. Getting the prompt right can turn into a "matter of life and death" for a doctor using AI to pinpoint a diagnosis, Trust said.

That's why it's not a good idea to ban ChatGPT or other AI tools, as New York City and some other school districts have done, Partovi said. He noted that while the nation's largest public school district essentially banned the technology except for use in limited circumstances, at least one prominent Manhattan private school is offering instruction in "prompt engineering," an AI skill that could lead to a lucrative job. And that makes AI skill development an equity issue, some experts argue.

"I think a lot of the pushback was, 'well, why do people need to know computational thinking in English/language arts?'" Trust said. "And I think AI has made that more clear."

5. Let students know that AI skills are not just for computer science experts

Students need to be exposed to how AI is being used in the workforce today and how they might use the technology in their future careers, even if they don't go into a computer science field.

"There are all kinds of people involved in the design and deployment of AI-based solutions," Breazeal said. "One of the things we try to do is show [how AI impacts] very diverse roles, diverse industries so that kids can appreciate that 'chances are when I enter the workforce, if I'm not making these AI kinds of things, I'm probably at least using them in a way that helps me get my job done.'"

In fact, if students are going to enter a policy field-or simply vote in elections-they'll also need a grounding in how the technology works, said Leigh Ann DeLyser, the executive director and co-founder of CSforALL, a nonprofit organization, which seeks to help expand computer science education.

What's more, MIT's Breazeal and other experts say that AI will work best when it is designed by people who are part of the community that the tool is aimed at serving. Having people from a variety of backgrounds-racial, socioeconomic, age, gender, profession-can help root out some of the biases embedded in society and, therefore, AI.

"You have to take kids seriously," Breazeal said. "We want to empower them to say, 'I can make a difference right now.' So let's get you the education, the curriculum, the tools, the community so that you can make a difference. And that's super empowering for kids."

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Now that AI is expected to influence fields from medicine to policing, what do students need to understand about the technology?

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NISOD How Community Colleges are Adapting to GenAI

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NISOD How Community Colleges are Adapting to GenAI

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McKinsey: The CIO's four-point guide to navigating technology trends

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McKinsey: The CIO's four-point guide to navigating technology trends

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"We should give all our IT to an offshore system integrator-that will save a lot of money. Our competitors are doing the same thing.""We need to do something about generative AI right away!"

Many who work in IT have heard variations of these statements from well-meaning senior colleagues. And given how much technology underpins so many trends, as well as the increasing pace of innovation, IT leaders can expect to hear more of them and feel pressure to act on them.

Sometimes it makes a lot of sense to act quickly on a trend. Innovation, after all, is a hallmark of successful companies. But not all trends are created equal. The annals of many IT organizations are littered with bold initiatives in trends that created a lot of excitement but not much value. Moving quickly to chase a trend often bogs an organization down, leads to wasted spend, and takes attention away from important priorities.

For this reason, the ability to evaluate trends quickly and communicate their relevance to the business is becoming a critical capability for the modern CIO. In practice, it is rarely as simple as saying "yes" or "no" to investment in a particular trend. Trends are unpredictable, change with time, and their relevance to a given business often waxes or wanes. Instead, CIOs need a clear set of parameters to rely on both for evaluating trends and for determining which posture to take for engagement with them: first mover, fast follower, slow adopter, or sometimes non-partaker.

The four guides for determining a trend's relevance

We researched trends of the past to find patterns that can help us better understand how to evaluate them in terms of their relevance to a business. There are many reasons why attempts to turn a promising trend into a profitable one for any given business may fail-insufficient talent or leadership nonalignment come to mind. But we have also found that trends that add value have inherent attributes that make them particularly valuable to any business. We've boiled them down to the following four:

Disruptive business value: The trend can result in measurable value to the business.

Independence: The trend allows the organization to work in smaller, more independent units.

Connectivity: The trend reduces friction in the organization's connectivity.

Extensibility: The trend can broadly shape and improve the organization's technology and management practices.

You don't necessarily need a green light across each of these four parameters to make investment in a trend worthwhile. But if one of them comes up red or yellow, it's worth taking a closer look before making a significant commitment. And while trends often initially aim to address just one of these areas, their relevance and impact rise dramatically when they can be applied coherently to all four.

It's worth emphasizing that this evaluation isn't a simple checklist. It requires rigor in the analysis, a willingness to review the analysis on its merits (without being influenced by how it might serve a narrow but favored "pet" project"), and creativity-some trends aren't particularly meaningful on their own, but in combination (virtualization and cloud, for example) can have greater potential.

1. Disruptive business value

Almost any trend or development has the potential to improve something in an organization. The question is whether that improvement is worth what it costs. It's important to understand the trade-offs. You may save money or generate value in one area by adopting a certain trend, but will it cost you in another?

The most important points to determine are whether the value is to IT alone or to the business overall, whether that value is merely incremental or significant, and whether success is clearly measurable in KPIs. If a trend improves an IT process but can't be directly linked to a business advantage, then it's probably not worth a significant investment. For example, much of the benefit of cloud computing comes not from improvements to IT productivity but from how it accelerates and enables business processes and innovation.

The value of some trends will depend on the sector where they are applied. Blockchain's value potential, for example, has differed from industry to industry. While in some areas of financial services, such as cryptocurrency and collateral management, blockchain has the potential to create a lot of value, in other industries, such as consumer packaged goods, the value potential is less clear.

Key questions

Does this trend provide disruptive business value? If the value is incremental or isolated to some operational improvement in IT, then the trend isn't really worth a "big bet."

How will you measure value capture? Every initiative has metrics, but they are often hazy or do not clearly reflect business value. Be disciplined in developing measurable objectives and key results (OKRs), and determine which KPIs contribute to them. This clarity is critical during implementation to see whether what you're working on is delivering value and why or why not.

2. Independence

One of the tech executive's key challenges is that IT often has too many interdependencies, which leads to technical debt and administrative processes such as alignment meetings and process coordination-for example, when changes to the billing system depend on numerous other systems and development teams, meetings and delays are inevitable consequences.

The concept of "modularity" has been in vogue for almost two decades now, but the enthusiasm for it has generally not been matched in its implementation. APIs have certainly helped, but they don't address the important organizational changes that also need to happen to reduce dependencies. That's where moving toward a product and platform operating model -where independent teams work on user-facing products while platform teams build capabilities to support them-can have significant impact. Assessing how a trend can be profitably adopted and supported by either product or platform teams should be a critical criterion for evaluating its potential.

Many of us are familiar with the big trend some 10 to 15 years ago toward large-scale outsourcing of IT to an external system provider. The rationale was that external providers would reduce IT's overall cost. For some industries with low margins, large-scale outsourcing deals were at least partially successful. But the system dependencies didn't change. So, as the need for speed and flexibility in technology grew, the savings from outsourcing were increasingly outweighed by the system dependencies that remained in place and slowed the pace of development. With modern software as a service (SaaS), on the other hand, only a specific, relatively independent, and clearly contained functionality is outsourced to a vendor, making this approach much more scalable.

Key questions

Does this trend allow me to make my teams more independent-for example, by automating communication at interfaces or reducing the need for coordination and up-front planning?

To what degree will this trend enable a more modular IT estate? Look for specific benefits, like a trend's ability to separate different applications from each other or abstract layers of the technology stack.

How many parts of the IT estate are needed to make this trend run well? The greater the number, the greater the complexity and the more likely you'll run into inevitable workarounds that will add to technical debt.

3. Connectivity

While independence is important, a trend that operates like a satellite on its own or divides the organization up into disconnected units isn't going to lead to at-scale impact. There is a difference between dependency, which isn't good, and leverage, which is critical for delivering value.

That difference was clearly illustrated in enterprise service buses (ESBs), which afforded independence but required additional alignment and orchestration, sometimes through a single central team, which created a bottleneck.

Virtualization is another trend that highlights the need to balance independence and connectivity. Initially focused on improving the usage of hardware assets-specifically, the CPU-virtualization also made applications less dependent on each other and on larger systems by not forcing everything to run on a single server. But it provided limited connectivity between individual virtual machines. There was no structured capability in place that made it easy and convenient to move workloads, scale them, and manage the connections. This changed with cloud computing and containerization, which connected individual machines through a central management layer.

In contrast, modern microservices patterns aim to both improve independence and clarify the interfaces and thus improve connectivity by reducing the need for communication or a central coordination mechanism.

"Improved connectivity" should not be misunderstood as more communications-that's a recipe for creating more dependencies. Improved connectivity is often reached through more clarity on the interfaces, reducing the need for communication.

Key questions

Does this trend increase clarity at the interfaces and reduce the need for coordination? You're looking for a trend that can use stable and reusable interfaces between technologies.

Does this trend lead teams to work with each other more effectively due to less "waste" in communications?

In what way can this trend leverage existing technology assets? It will be important to understand what kinds of interfaces will make the necessary connections, and how easily they can be implemented and maintained.

4. Extensibility

The impact of a technology trend increases with its applicability and coherence across the IT estate. A technology trend that touches only one part of the IT estate in isolation or is managed simply as a "tech product" does not often have the widespread impact that comes with true innovation. Impact often requires companies to think through both the technology and operational implications to open up the potential. Broad applicability is where you get to scale.

Virtualization, microservices, or SaaS-based services, for example, were less successful when they were managed as a pure tech product. Only when combined with enabling factors on the operating side, such as agile, DevOps, and the necessary support structures (such as budgeting roles and HR support) did they have much greater impact.

Similarly, agile works best when all relevant parts of the company work in agile ways. (The impact of agile developer teams is improved when operations, for example, also work in agile ways, which is how we came to DevOps.) The key question for the CIO, then, is how to adjust the technology estate to extend the range of application across technology and the business.

Key questions

How can IT management practices adapt or innovate with the trend?

Will this trend require the business operations and decision processes to change in order to develop its full potential?

Are all the support processes (such as budgeting, HR, architecture) in place to support the trend?

Adopt or not: A strategic decision

As they say in comedy, timing is everything, and that's just as true when it comes to trends. Going in on a trend too early or too late can sometimes be worse than doing nothing at all. Just as important as evaluating a trend's "bona fides" according to the four parameters described above is determining how best to engage with it. CIOs should consider four possible engagement approaches:

First mover: This engagement makes the most sense in the case where the trend has significant impact on the company's core business model (which is to say, it's a matter of business survival). This approach generally involves investing significant resources of time, people, and money. The big trap here is convincing yourself that the first-mover approach is the only viable option.

Fast follower: This approach is best applied when the trend could have an important impact on your business model or open up a significant revenue stream. This engagement posture can work when the trend is still too early in its maturity cycle to understand how it can best be harnessed, or when the business doesn't yet have sufficient capabilities in place to act on the trend. A good signal of a trend's importance is when big players lead the innovation and go all in. The danger here is taking a "wait and see" approach too long and finding yourself much further behind your competitors than you expected. Greenlighting pilots can make it seem that you're taking action, but unless those pilots are closely tracked and-most importantly-scaled, then this activity has no real impact.

Slow adopter: This approach is an acceptable option when the trend isn't directly relevant to the core business or is not mature. It might make sense for niche applications of a trend. The danger here is that complacency and an overly narrow view of the trend's relevance, such as focusing only on how existing competitors might use it and failing to consider emerging players, leave your company too far behind to catch up.

Non-partaker: Some trends just don't make sense for a business to adopt. This is often an unpopular approach when the board or senior management is demanding action. A clear view of business goals and the impact of a trend on those goals is critical. But complacency is also a danger here when legacy-driven thinking argues that a given trend "doesn't apply to us."

The call on whether and how to adopt a trend is not a one-time decision. It requires continual review as technology matures and evolves, new implementation models and supporting services scale, or the market situation shifts. But CIOs who use the four parameters as a compass to determine relevance and think through their engagement options can better guide their organizations toward turning trends into value.

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False friends or good ends? The CIO's four-point guide to navigating technology trends

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CIOs need to keep in mind value, independence, connectivity, and extensibility when evaluating tech trends.

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AI-Powered Interview Preparation: Deep Dives, Not Generic Tips

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AI-Powered Interview Preparation: Deep Dives, Not Generic Tips

Sharpening Skills with Mock Interviews Tailored to the Role

Beyond Interviews: Streamlining the Entire Candidate Journey

Empowering Recruiters for Success

I also created detailed GenAI prompts for updating resume and cover letter materials in a blog earlier this spring . Please check that out.

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The Candidate Whisperer- Generative AI in the Executive Search

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Welcome back to our Blog series "GenAI: More Than Just a Fancy Spellchecker" launching on Mondays throughout May & June. In this series, we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai (& others) to supercharge their executive workflow and communications.

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In the latest annual survey by Inside Higher Ed and Hanover Research, the growing importance of AI in higher education is evident.

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The key lies in "authoritative virtualizations." These models don't have to be perfect; they just need to be accurate enough to replace or reduce the need for physical activities. This allows companies to:

The key lies in "authoritative virtualizations." These models don't have to be perfect; they just need to be accurate enough to replace or reduce the need for physical activities. This allows companies to:

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How Digital Twins Are Revolutionizing Businesses

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"It's not one big thing that makes a difference. It's a lot of little things put together that enable big performance improvements."I read a recent a fascinating McKinsey article "The AI revolution will be 'virtualized'" by Kimberly Borden, Mark Huntington, Mithun Kamat, and Will Roper.

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I have previously shared my own experience with AI-generated headshots and I am generally in favor of using them, while exercising caution. Professionalism and authenticity remain crucial in your personal branding. AI-generated headshots can be an affordable way to get a large number of headshot options for job seekers.

I have previously shared my own experience with AI-generated headshots and I am generally in favor of using them, while exercising caution. Professionalism and authenticity remain crucial in your personal branding. AI-generated headshots can be an affordable way to get a large number of headshot options for job seekers.

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From Meh to Masterpiece- How GenAI Can Supercharge Your Job Search

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Welcome back to our Blog series "GenAI: More Than Just a Fancy Spellchecker" launching on Mondays throughout May & June. In this series, we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai (& others) to supercharge their executive workflow and communications.

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How Generative AI is Ushering in a New Era of Employee Feedback

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Welcome to my new Blog series “GenAI: More Than Just a Fancy Spellchecker” where we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai to supercharge their executive workflow and communications. This week we dig into performance evaluations.Performance evaluations – they’re a necessary part of the management cycle, but let’s be honest, they can be time-consuming, challenging to individualize, and stressful for everyone involved. Struggling for the r

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Mindset in Motion "AI in Student Affairs" Podcast now available.

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Recently, I had the honor of joining Dr. Bill Heinrich on the "Mindset in Motion" podcast. Bill and I attended graduate school together at Michigan State University and only recently reconnected through the power of LinkedIn and the shared interest in AI technologies.

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Claire Brady

In April, I had the pleasure of visiting D'Youville University to speak at their annual "Unpacking" event. This year's theme focused on Artificial Intelligence (AI). My presentation explored emerging best practices in AI literacy, the importance of ethical AI use, and the growing impact of AI on the healthcare industry.

The audience included students, faculty, staff, and university leadership. The diverse student audience included a mix of health professions students, student athletes, and student leaders. Their enthusiasm and range of perspectives created a rich and engaging program. I was honored to be invited to this impactful event that fosters dialogue on critical issues.

While on campus, I met Humberto Hernandez and Susan Arnold, who invited me to join their DYU podcast, "AI in Higher Education." We had a stimulating conversation a few weeks ago, exploring the transformative potential of AI in education. Humberto and Susan, thank you for your expertise and dedication to leading AI integration efforts at DYU.

Check out the episode and let me know your thoughts in the comments.

In April, I had the pleasure of visiting D'Youville University to speak at their annual "Unpacking" event. This year's theme focused on Artificial Intelligence (AI). My presentation explored emerging best practices in AI literacy, the importance of ethical AI use, and the growing impact of AI on the healthcare industry.

The audience included students, faculty, staff, and university leadership. The diverse student audience included a mix of health professions students, student athletes, and student leaders. Their enthusiasm and range of perspectives created a rich and engaging program. I was honored to be invited to this impactful event that fosters dialogue on critical issues.

While on campus, I met Humberto Hernandez and Susan Arnold, who invited me to join their DYU podcast, "AI in Higher Education." We had a stimulating conversation a few weeks ago, exploring the transformative potential of AI in education. Humberto and Susan, thank you for your expertise and dedication to leading AI integration efforts at DYU.

Check out the episode and let me know your thoughts in the comments.

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DYU "AI in Higher Education" Podcast now available

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In April, I had the pleasure of visiting D'Youville University to speak at their annual "Unpacking" event. This year's theme focused on Artificial Intelligence (AI). My presentation explored emerging best practices in AI literacy, the importance of ethical AI use, and the growing impact of AI on the healthcare industry.

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The Intersection of Innovation and Responsibility- Healthcare in the Age of AI

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The healthcare space is not just evolving; it's at a critical juncture of profound transformation driven by innovation and weighted with immense responsibility. At this crossroads, Artificial Intelligence (AI) emerges as both a beacon of potential and a test of our collective values.

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ARL releases Generative AI Guiding Principles for Librarians

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The past two years have witnessed a surge in the use of generative artificial intelligence (GenAI), particularly large language models like ChatGPT and Gemini. This rapid advancement has not only opened doors to exciting possibilities but also raised crucial questions about ethics, privacy, and its impact on research and education.

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Claire Brady

Welcome to my new Blog series "GenAI: More Than Just a Fancy Spellchecker" where we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai to supercharge their executive workflow and communications. This series will post on Monday's through May & June.

So let' get started...with curiosity! Modern workplaces are constantly evolving, and executives who embrace innovation often hold the key to organizational success. One of the most exciting advancements in the past 2 years is the rise of Generative AI (GenAI) tools. Author and researcher Julian Stodd wrote about GenAI "It's easy to get caught up in the popular debate: about bias, about validity, about influence or infiltration, about the 'good' and the 'bad'. All of those things are important. But let's not miss the potential, the excitement, the dynamism and change." Generative AI models use neural networks to identify patterns and structures within existing data to generate new content, such as text, images, sounds, animation, 3D models, audio, video, and code. The models "generate" new content by referring to the data they have been trained on, making new predictions.

But why should you, as a busy executive, be curious about GenAI? I began exploring these tools when ChatGPT was first released and have found them to now be integral to my work day. From helping me to ideate, to improving my writing, helping me to analyze and present data, write proposals, elevate client resumes and cover letters, or improve my social media posts- Gen AI tools have become essential to saving time, reserving energy for higher value tasks, and polishing my professional brand.

GenAI tools can also help you to code faster and more accurately, create video and audio content, automate processes, generate images, compose music, and even create beautiful works of art. Check out my blog post detailing the GenAI tools that I recommend for research/writing, creativity, productivity, and career development/branding. And yes, I asked Google Gemini and ChatGPT to look over this blog post and recommend ways that I could strengthen my writing!

Here's why exploring and using GenAI regularly can be a competitive advantage and an essential skillset for executive leaders:

Beyond Efficiency: Human-centered AI

It's important to remember that GenAI tools are not meant to replace your expertise or judgment. They are powerful supplements that can free up your time and enhance your capabilities. The human element of leadership - your vision, experience, and emotional intelligence - will always take priority and should never be underestimated.

Curiosity is the First Step

By embracing curiosity and exploring GenAI tools, you set yourself on the path to becoming a future-proof leader. Think of it as investing in your own professional development. Don't be afraid to experiment, ask questions, and discover how GenAI can transform your workflow and empower you to achieve exceptional results.

Ready to Take Action?

Start by exploring well-established GenAI tools like Google Gemini, ChatGPT, Claude, or You.ai or the 40+ tools that I recommend . Many offer free trials or introductory resources. The key is to unlock your curiosity and see how GenAI can supercharge your executive toolkit and drive your organization to new heights.

What's coming up? Next week, we dig into how GenAI can help managers develop performance reviews with greater impact.

Welcome to my new Blog series "GenAI: More Than Just a Fancy Spellchecker" where we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai to supercharge their executive workflow and communications. This series will post on Monday's through May & June.

So let' get started...with curiosity! Modern workplaces are constantly evolving, and executives who embrace innovation often hold the key to organizational success. One of the most exciting advancements in the past 2 years is the rise of Generative AI (GenAI) tools. Author and researcher Julian Stodd wrote about GenAI "It's easy to get caught up in the popular debate: about bias, about validity, about influence or infiltration, about the 'good' and the 'bad'. All of those things are important. But let's not miss the potential, the excitement, the dynamism and change." Generative AI models use neural networks to identify patterns and structures within existing data to generate new content, such as text, images, sounds, animation, 3D models, audio, video, and code. The models "generate" new content by referring to the data they have been trained on, making new predictions.

But why should you, as a busy executive, be curious about GenAI? I began exploring these tools when ChatGPT was first released and have found them to now be integral to my work day. From helping me to ideate, to improving my writing, helping me to analyze and present data, write proposals, elevate client resumes and cover letters, or improve my social media posts- Gen AI tools have become essential to saving time, reserving energy for higher value tasks, and polishing my professional brand.

GenAI tools can also help you to code faster and more accurately, create video and audio content, automate processes, generate images, compose music, and even create beautiful works of art. Check out my blog post detailing the GenAI tools that I recommend for research/writing, creativity, productivity, and career development/branding. And yes, I asked Google Gemini and ChatGPT to look over this blog post and recommend ways that I could strengthen my writing!

Here's why exploring and using GenAI regularly can be a competitive advantage and an essential skillset for executive leaders:

Beyond Efficiency: Human-centered AI

It's important to remember that GenAI tools are not meant to replace your expertise or judgment. They are powerful supplements that can free up your time and enhance your capabilities. The human element of leadership - your vision, experience, and emotional intelligence - will always take priority and should never be underestimated.

Curiosity is the First Step

By embracing curiosity and exploring GenAI tools, you set yourself on the path to becoming a future-proof leader. Think of it as investing in your own professional development. Don't be afraid to experiment, ask questions, and discover how GenAI can transform your workflow and empower you to achieve exceptional results.

Ready to Take Action?

Start by exploring well-established GenAI tools like Google Gemini, ChatGPT, Claude, or You.ai or the 40+ tools that I recommend . Many offer free trials or introductory resources. The key is to unlock your curiosity and see how GenAI can supercharge your executive toolkit and drive your organization to new heights.

What's coming up? Next week, we dig into how GenAI can help managers develop performance reviews with greater impact.

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The Future of Leadership is Curious... about Generative AI

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Welcome to my new Blog series "GenAI: More Than Just a Fancy Spellchecker" where we will dig into how Executives can use Generative AI tools like Google Gemini, ChatGPT, & You.ai to supercharge their executive workflow and communications. This series will post on Monday's through May & June.So let' get started...with curiosity!

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Diverse Perspectives on AI:

As the opening speaker, I explored several key points:

The critical role of ethical considerations in AI implementation.

Practical use cases for AI in various academic settings.

Empowering Higher Education leaders to initiate AI integration.

Maintaining the human element at the heart of AI-powered education.

Engaging Discussion and Resources:

As the opening speaker, I explored several key points:

The critical role of ethical considerations in AI implementation.

Practical use cases for AI in various academic settings.

Empowering Higher Education leaders to initiate AI integration.

Maintaining the human element at the heart of AI-powered education.

Engaging Discussion and Resources:

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AI's Impact on Student Learning and Engagement MFYC Panel Discussion

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Last week, I had the privilege of joining a captivating panel discussion at the Midwest First Year Experience Conference. The focus?

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Claire Brady

This is part 2 of 2 of a blog focused on how to best use Generative AI tools to update and improve your application materials for an executive search.

Let's recap from last week:

Last week we focused on exploring how Generative AI tools can streamline the process of updating your resume and cover letter, especially when customizing your application for a particular job opening. We discussed how to "decode the job posting" and I shared some Generative AI tools that you can use to upgrade your application materials for your executive search. Check out part 1 if you want to read the full post.

Here are 10 sample Generative AI prompts for executive resume and cover letters:

2) "After reviewing this resume (insert resume content), please provide 4-6 strong bullet points that could be used in a high level executive profile based on my experience. Do not use specific titles or institutions but focus on overarching achievements and key areas of impact." OR "Can you suggest a professional summary for an executive resume targeting VP Student Affairs roles at private colleges?"

3) "Please help me to improve the writing of these resume bullets or this cover letter (insert content) for the role of <insert title> at <institution> OR paste the <position listing> OR "Revise and enhance my resume bullet points to quantify achievements, using action verbs and focusing on outcomes that resonate with senior executive roles."

3) "Please analyze this job posting and draft a summary of key points I should discuss in my application materials and paste <job posting>" OR "What are the 5-7 most important areas to focus on in my resume and cover letter from this posting? <paste job posting>"

7) Ask for help in phrasing achievements. "How can I describe a project where I increased student participation by 40% in a way that stands out on my resume?" OR "Extract key metrics and achievements from my current resume and reframe them to quantify the impact I made in previous roles. Focus on results that align with the goals and challenges mentioned in this posting <insert posting>.

10) "Draft a compelling career narrative that highlights my journey, key milestones, and how each role has prepared me for the next challenge, specifically targeting an executive leadership position." OR "I have a strong track record in <Area of Expertise>. Help me craft a compelling narrative for my cover letter that demonstrates the impact I made in this area and how it translates to the needs of this posting <insert posting>.

General Tips for Generative AI tools:

Provide as much context as possible to ensure the tool understands your needs and goals.

Don't hesitate to refine your prompts based on responses you get, making your queries more precise for better outcomes.

Remember that you can generate different options by clicking the generate button if you want to see other options.

You can also dig deeper into the response- and be directive with the AI tool. ie: Can you get more specific? Can you edit this writing without adding too many more words?

You can ask the tool to format your content a certain way or clean up formatting irregularities.

Don't expect the tool to generate a perfect final product. Use the generated text as a starting point for further editing and refinement.

Be mindful of potential biases in AI models. Always carefully review the generated text and ensure it accurately reflects your skills and experiences.

And yes, I always say please and thank you to the AI tool, it works hard for me! I also share my final version once I have verified for accuracy and edited in my voice, so that I can help the AI tool learn & evolve.

This is part 2 of 2 of a blog focused on how to best use Generative AI tools to update and improve your application materials for an executive search.

Let's recap from last week:

Last week we focused on exploring how Generative AI tools can streamline the process of updating your resume and cover letter, especially when customizing your application for a particular job opening. We discussed how to "decode the job posting" and I shared some Generative AI tools that you can use to upgrade your application materials for your executive search. Check out part 1 if you want to read the full post.

Here are 10 sample Generative AI prompts for executive resume and cover letters:

2) "After reviewing this resume (insert resume content), please provide 4-6 strong bullet points that could be used in a high level executive profile based on my experience. Do not use specific titles or institutions but focus on overarching achievements and key areas of impact." OR "Can you suggest a professional summary for an executive resume targeting VP Student Affairs roles at private colleges?"

3) "Please help me to improve the writing of these resume bullets or this cover letter (insert content) for the role of <insert title> at <institution> OR paste the <position listing> OR "Revise and enhance my resume bullet points to quantify achievements, using action verbs and focusing on outcomes that resonate with senior executive roles."

3) "Please analyze this job posting and draft a summary of key points I should discuss in my application materials and paste <job posting>" OR "What are the 5-7 most important areas to focus on in my resume and cover letter from this posting? <paste job posting>"

7) Ask for help in phrasing achievements. "How can I describe a project where I increased student participation by 40% in a way that stands out on my resume?" OR "Extract key metrics and achievements from my current resume and reframe them to quantify the impact I made in previous roles. Focus on results that align with the goals and challenges mentioned in this posting <insert posting>.

10) "Draft a compelling career narrative that highlights my journey, key milestones, and how each role has prepared me for the next challenge, specifically targeting an executive leadership position." OR "I have a strong track record in <Area of Expertise>. Help me craft a compelling narrative for my cover letter that demonstrates the impact I made in this area and how it translates to the needs of this posting <insert posting>.

General Tips for Generative AI tools:

Provide as much context as possible to ensure the tool understands your needs and goals.

Don't expect the tool to generate a perfect final product. Use the generated text as a starting point for further editing and refinement.

Be mindful of potential biases in AI models. Always carefully review the generated text and ensure it accurately reflects your skills and experiences.

And yes, I always say please and thank you to the AI tool, it works hard for me! I also share my final version once I have verified for accuracy and edited in my voice, so that I can help the AI tool learn & evolve.

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AI Tips & Tricks- Using Generative AI to update Resumes & Cover Letters (Part 2/2)

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This is part 2 of 2 of a blog focused on how to best use Generative AI tools to update and improve your application materials for an executive search. Let's recap from last week:Last week we focused on exploring how Generative AI tools can streamline the process of updating your resume and cover letter, especially when customizing your application for a particular job opening.

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Claire Brady

This is the first part 1 of a 2 part blog focused on using Generative AI to update resumes and cover letters for executive searches.

Generative AI tools can significantly streamline the process of updating your resume and cover letter, especially when customizing your application for a specific position posting. The key to effectively using Generative AI lies in formulating precise and detailed prompts that direct the AI to generate advice that's both relevant and tailored to your needs without losing your voice and diluting your experience. The most impactful strategy is to adapt your prompts to align with the specific requirements and nuances of the job you're targeting. This approach ensures that the assistance you receive is not only accurate but also perfectly suited to enhance your application, making it more compelling to potential employers.

First, decode the job description:

Keyword mining : Identify key terms, skills, and responsibilities mentioned. Don't just passively list them; translate them into specific actions you can demonstrate. AI tools can help you to identify these.

Problem-solution mindset : Think of the job description as a problem the organization or company needs to solve. How can your skills and experience be the solution?

Context matters : Is this a fast-paced startup-type operation or a well-established and legacy organization? Tailor your prompts to fit the organizational culture and needs.

What Generative AI tools do I recommend:

Primarily, I use free versions of ChatGPT and Google Gemini to improve bullet points, write executive profiles for the resume, improve or tailor the cover letter, and run the job posting through to analyze themes, etc... I also use these tools to create a base cover letter from the job posting that I tailor with my experiences.

There are also resume and cover letter specific tools such as Open AI Cover Letter Generator AI (free tool that crafts personalized cover letters using your resume & the job link) and Resume GPT (free expert tips with industry-specific advice for an ATS-optimized resume).

I also recommend Secta Labs a paid (one time) tool that transforms your uploaded photos into 100's of professional headshots for folks needing to update their headshots at a low cost. I use these AI generated headshots on my website and my social media accounts.

I have no business relationship or partnerships with any of the AI tools that I recommend.

Join us next next week for Part 2 focused on sample GenAI prompts and my best tips and tricks for using these AI tools to tailor your executive application materials.

This is the first part 1 of a 2 part blog focused on using Generative AI to update resumes and cover letters for executive searches.

Generative AI tools can significantly streamline the process of updating your resume and cover letter, especially when customizing your application for a specific position posting. The key to effectively using Generative AI lies in formulating precise and detailed prompts that direct the AI to generate advice that's both relevant and tailored to your needs without losing your voice and diluting your experience. The most impactful strategy is to adapt your prompts to align with the specific requirements and nuances of the job you're targeting. This approach ensures that the assistance you receive is not only accurate but also perfectly suited to enhance your application, making it more compelling to potential employers.

First, decode the job description:

Keyword mining : Identify key terms, skills, and responsibilities mentioned. Don't just passively list them; translate them into specific actions you can demonstrate. AI tools can help you to identify these.

Problem-solution mindset : Think of the job description as a problem the organization or company needs to solve. How can your skills and experience be the solution?

Context matters : Is this a fast-paced startup-type operation or a well-established and legacy organization? Tailor your prompts to fit the organizational culture and needs.

What Generative AI tools do I recommend:

Primarily, I use free versions of ChatGPT and Google Gemini to improve bullet points, write executive profiles for the resume, improve or tailor the cover letter, and run the job posting through to analyze themes, etc... I also use these tools to create a base cover letter from the job posting that I tailor with my experiences.

There are also resume and cover letter specific tools such as Open AI Cover Letter Generator AI (free tool that crafts personalized cover letters using your resume & the job link) and Resume GPT (free expert tips with industry-specific advice for an ATS-optimized resume).

I also recommend Secta Labs a paid (one time) tool that transforms your uploaded photos into 100's of professional headshots for folks needing to update their headshots at a low cost. I use these AI generated headshots on my website and my social media accounts.

I have no business relationship or partnerships with any of the AI tools that I recommend.

Join us next next week for Part 2 focused on sample GenAI prompts and my best tips and tricks for using these AI tools to tailor your executive application materials.

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AI Tips & Tricks- Using Generative AI to update Resumes & Cover Letters (part 1/2)

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This is the first part 1 of a 2 part blog focused on using Generative AI to update resumes and cover letters for executive searches.Generative AI tools can significantly streamline the process of updating your resume and cover letter, especially when customizing your application for a specific position posting.

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Think Like a Robot, Act Like a Human- Fostering AI Literacy

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Imagine a world where students understand how algorithms shape their newsfeeds, how facial recognition works, and the biases hiding within AI systems. This isn’t science fiction, it’s the future fueled by AI literacy. Forget jargon-filled coding classes; AI literacy needs to be accessible to all. It’s not about building robots, but empowering students to navigate an AI-driven world. It’s about humanizing AI.A recent blog post focused on AI in the non-profit sector highlighted two important point

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In my previous blog highlighting writing and research AI tools , I put ChatGPT and Google Gemini to the test, feeding them the same writing task - refining a paragraph about using these two powerful AI tools. The results were eye-opening, showcasing the unique strengths and surprising differences in how each AI tool approached the same challenge.

What did you notice? Which one did you prefer? Would you use elements of both responses in your final product?

Differences in Approach: (Gemini's response)

Differences in Approach: (ChatGPT's response)

In my previous blog highlighting writing and research AI tools , I put ChatGPT and Google Gemini to the test, feeding them the same writing task - refining a paragraph about using these two powerful AI tools. The results were eye-opening, showcasing the unique strengths and surprising differences in how each AI tool approached the same challenge.

What did you notice? Which one did you prefer? Would you use elements of both responses in your final product?

Differences in Approach: (Gemini's response)

Differences in Approach: (ChatGPT's response)

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Ever wondered how some writers manage to churn out consistently engaging content? The secret weapon in their arsenal might be artificial intelligence (AI) writing tools. AI writing tools are gaining popularity, and I'm frequently asked which ones I use in my daily work.

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Claire Brady

Updated: Mar 3

As I continue to consult more and more about artificial intelligence (AI), I'm frequently asked three crucial questions: how will AI reshape higher education? How can institutions prepare for these changes? How can higher education leaders secure resources to support faculty and staff learning about AI?

These questions highlight the urgency of addressing AI's impact in education. How can investing in AI now lead to institutional advantages in the future?

Alignment with strategic goals: Integrating AI effectively can support existing institutional objectives (strategic plans, student outcomes, equity goals, and workforce development needs.

Enhanced student success: Utilizing AI responsibly can personalize learning experiences and improve student outcomes.

Meeting employer needs: Equipping graduates with AI literacy aligns them with evolving industry demands.

Understanding societal impact: Critically examining AI's ethical implications fosters responsible implementation.

Today's blog aims to delve deeper into these key questions and explore strategies for navigating the impact of AI in higher education. We will discuss how preparing institutions to thrive in the age of AI will impact strategic goals, student success outcomes, emerging employer needs, and the need to explore the societal impact of these massive technological shifts. I'll write more in the future about securing resources for AI training and professional development.

Aligning with Institutional Strategic Goals

By encourageing institutions to embrace AI, they can foster an environment of innovation and adaptability . By integrating AI into their strategic planning, universities can stay at the forefront of educational advancements, ensuring their programs remain relevant and cutting-edge. Exploring AI's applications across different fields can promote interdisciplinary collaboration , breaking down silos and enhancing the richness of academic offerings.

Enhancing Student Success

Meeting Emerging Employer Needs

Educators and students will become more aware of and engaged with the broader implications of AI on privacy, security, employment, and ethics, fostering a more informed citizenry.

I'm working with several institutions to create and facilitate trainings for faculty and staff that explore the many facets of AI that are impacting higher education. By integrating theoretical insights with practical, hands-on experiences, the trainings are designed to provide a balanced overview of essential AI knowledge, cutting-edge trends, and tangible applications. Participants are engaging deeply with specific facets of AI, gaining the skills and confidence needed to navigate and contribute to this rapidly advancing field. Faculty and staff emerge from these trainings not just as informed AI users but as forward-thinking educators equipped to integrate AI innovations in ways that enhance learning outcomes and ethical standards.

We need to prepare institutions, educators, and students to effectively leverage AI technologies. By doing so, we can align academic programs with the future of work, promote student success in a technologically advanced society, and ensure that the next generation of leaders is equipped to use AI for the greater good.

Let's connect if you are interested in offering tailored AI training for your faculty/staff.

As I continue to consult more and more about artificial intelligence (AI), I'm frequently asked three crucial questions: how will AI reshape higher education? How can institutions prepare for these changes? How can higher education leaders secure resources to support faculty and staff learning about AI?

These questions highlight the urgency of addressing AI's impact in education. How can investing in AI now lead to institutional advantages in the future?

Alignment with strategic goals: Integrating AI effectively can support existing institutional objectives (strategic plans, student outcomes, equity goals, and workforce development needs.

Enhanced student success: Utilizing AI responsibly can personalize learning experiences and improve student outcomes.

Meeting employer needs: Equipping graduates with AI literacy aligns them with evolving industry demands.

Understanding societal impact: Critically examining AI's ethical implications fosters responsible implementation.

Today's blog aims to delve deeper into these key questions and explore strategies for navigating the impact of AI in higher education. We will discuss how preparing institutions to thrive in the age of AI will impact strategic goals, student success outcomes, emerging employer needs, and the need to explore the societal impact of these massive technological shifts. I'll write more in the future about securing resources for AI training and professional development.

Aligning with Institutional Strategic Goals

By encourageing institutions to embrace AI, they can foster an environment of innovation and adaptability . By integrating AI into their strategic planning, universities can stay at the forefront of educational advancements, ensuring their programs remain relevant and cutting-edge. Exploring AI's applications across different fields can promote interdisciplinary collaboration , breaking down silos and enhancing the richness of academic offerings.

Enhancing Student Success

Meeting Emerging Employer Needs

Educators and students will become more aware of and engaged with the broader implications of AI on privacy, security, employment, and ethics, fostering a more informed citizenry.

I'm working with several institutions to create and facilitate trainings for faculty and staff that explore the many facets of AI that are impacting higher education. By integrating theoretical insights with practical, hands-on experiences, the trainings are designed to provide a balanced overview of essential AI knowledge, cutting-edge trends, and tangible applications. Participants are engaging deeply with specific facets of AI, gaining the skills and confidence needed to navigate and contribute to this rapidly advancing field. Faculty and staff emerge from these trainings not just as informed AI users but as forward-thinking educators equipped to integrate AI innovations in ways that enhance learning outcomes and ethical standards.

We need to prepare institutions, educators, and students to effectively leverage AI technologies. By doing so, we can align academic programs with the future of work, promote student success in a technologically advanced society, and ensure that the next generation of leaders is equipped to use AI for the greater good.

Let's connect if you are interested in offering tailored AI training for your faculty/staff.

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How Higher Ed Can Lead in the Age of Artificial Intelligence

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As I continue to consult more and more about artificial intelligence (AI), I'm frequently asked three crucial questions: how will AI reshape higher education? How can institutions prepare for these changes? How can higher education leaders secure resources to support faculty and staff learning about AI?These questions highlight the urgency of addressing AI's impact in education.

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72. Generative AI Hub: How Stanford is Empowering K-12 Ed Leaders with AI Tools | Chris Agnew

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Podcast · Daniel Lopez · Artificial Intelligence is here. The AI Education Conversation explores the opportunities, risks, and impacts of AI across education. E: theaiedconvo@gmail.com Linkedin: Daniel Anthony Lopez

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Dan Bowen and Ray Fleming are experienced education renegades who have worked in many various educational institutions and educational companies across the world. They talk about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used. It's not too serious, or too technical, and is intended to be a good conversation.

Please note the views on the podcast are our own or those of our guests, and not of our respective employers (unless we say otherwise at the time!)

Dan Bowen and Ray Fleming are experienced education renegades who have worked in many various educational institutions and educational companies across the world. They talk about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used. It's not too serious, or too technical, and is intended to be a good conversation.

Please note the views on the podcast are our own or those of our guests, and not of our respective employers (unless we say otherwise at the time!)

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Dan Bowen and Ray Fleming are experienced education renegades who have worked in many various educational institutions and educational companies across the world. They talk about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used. It's not too...

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‎The Generative Age: AI in Education on Apple Podcasts

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Generative Age (powered by NYSCATE) explores the rapidly evolving world of generative artificial intelligence and its impact on education. Host Alana Winnick is joined by practitioners and thought leaders who are shaping the discourse around integrating generative AI into the classroom. Whether you...

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Mike Pearson & Pat Burns explore AI's impact on teaching, learning, and EdTech. Discover innovative tools, strategies, & real-world examples of artificial intelligence transforming education in the classroom. Join Mike and Pat and their guests and join the conversation.
Discuss with us:
@TheBotBrothers
Contact Us/Suggestions
Resources
thebotbrothersai@gmail.com
Mike Pearson

Mike Pearson & Pat Burns explore AI's impact on teaching, learning, and EdTech. Discover innovative tools, strategies, & real-world examples of artificial intelligence transforming education in the classroom. Join Mike and Pat and their guests and join the conversation.
Discuss with us:
@TheBotBrothers
Contact Us/Suggestions
Resources
thebotbrothersai@gmail.com
Mike Pearson

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‎The Bot Brothers: AI for Educators on Apple Podcasts

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Mike Pearson & Pat Burns explore AI's impact on teaching, learning, and EdTech. Discover innovative tools, strategies, & real-world examples of artificial intelligence transforming education in the classroom. Join Mike and Pat and their guests and join the conversation. Discuss with us: @TheBotBroth...

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The once-familiar landscape of classrooms, textbooks, and traditional pedagogy is undergoing a unique shift driven by the relentless advance of artificial intelligence (AI). In the very first episode of Learning Through Technology, we speak with Mary Beth Hertz to discuss how AI can improve the classroom. As an Author, Educator, and Founder of Walkabout Philly, Mary Beth has been at the forefront of technology integration in the school through local, national, and global connections with other educators and leaders in the educational technology field for the last decade.

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ai in education: embracing technology and ethical considerations with mary beth hertz

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author of digital and media literacy in the age of the internet

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‎Learning Through Technology: AI in Education: Embracing Technology and Ethical Considerations with Mary Beth Hertz, Author of Digital and Media Literacy in the Age of the Internet on Apple Podcasts

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The once-familiar landscape of classrooms, textbooks, and traditional pedagogy is undergoing a unique shift driven by the relentless advance of artificial intelligence (AI). In the very first episode of Learning Through Technology, we speak with Mary Beth Hertz to discuss how AI can improve the clas...

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Darren Gaddis: From CITI Program, I'm Darren Gaddis, and this is On Campus. Today, how artificial intelligence can impact higher education, what potential influences artificial intelligence could have on grading practices, and is there a need to change grading practices within higher education?

I spoke with Fred Martin, who invents and studies new technologies to enable teaching and learning in computer science, data science, and artificial intelligence. He creates partnerships for bringing these technologies to learners in school and out of school, focusing on K-12 teachers and students.

He collaborates with researchers in other fields, particularly in education and psychology. He supports faculty and students in the Kennedy College of Sciences focusing on experimental learning and student success at the University of Massachusetts Lowell.

As a reminder, this podcast is for educational purposes only. It is not intended to provide legal advice or guidance. You should consult with your organization's attorneys if you have questions or concerns about relevant laws and regulations discussed in this podcast. Additionally, the views expressed in this podcast are solely those of their presenter, and do not represent the views of their employer.

Hi, Fred. Thank you for joining me today.

Fred Martin: Hi, Darren. It's nice to meet you.

Darren Gaddis: Fred, to help us ground our conversation today, what is artificial intelligence, or AI? And how is it broadly used within higher education?

Fred Martin: Sure. So I used to define AI as something more simple, as simply automated decision making. So AI will evaluate a bunch of data, make a decision. Or it'll evaluate a situation and make a choice.

And so for a long time, that's a pretty good definition of AI. I think in the last couple of years, and particularly in the last six months, we've come across this new category of AI. Which existed before, but now it's affecting us and we're seeing it. So that's generative AI. So AI that is a creative agent, and makes things that are really compelling.

There's three sort of major ways that AI is used in higher ed. One of those is for data analytics, and often for student success types of applications. So looking at student grades and attendance in a large-scale, systemic way. And really to help faculty and staff support students.

So it's data analytics and predictive analytics to sort of identify which students are doing great and which students can use more attention. So that's one big area of AI.

Another way it's used is in grading systems. Faculty will use AI technology to sort of pre-process student exams, and categorize things, and it becomes a grading assistant to faculty. So I think the most prevalent use is where the AI will categorize student answers, and then faculty can apply rubrics to these groupings of answers. And it really becomes a way to support faculty in doing grading of exams.

And then the third way is basically computer-aided instruction. Where there are systems that students directly do homework, and then the underlying AI system is building a model of what the student knows and doesn't know, and can then give them the next steps of instructional material based on that.

There's a math education system called Newton that is pretty successful in supporting students in that way. To me, I really most am partial to the systems that are supports for faculty. And in the case of the advising, also support for staff in assisting the student. Even the Newton math system, there's a faculty who's part of reviewing the student work.

Because I think it's really important in higher ed, and in all sorts of teaching and learning, that students know there are people who care about them and are paying attention to them.

Darren Gaddis: And with that information, in the past year, how has AI changed the landscape of higher education?

Fred Martin: So ChatGPT burst onto the scene, I think it was last summer and certainly into the fall, as this technology that's really good at writing essays. Basically, you ask questions and it's a conglomerated, all of the knowledge-slash-incorrect or biased information on the web. And then it generates these pretty well-written essays.

And so I think we're actually yet to see the large scale change that will be happening in the coming months and years. At my school, we organize info discussion session at the very beginning of the semester. So end of January, 2023, one of the faculty co-led the session, she's head of our college writing program. Which of course will be obviously impacted by a system that can write essays.

And she basically said, we just are getting through Covid, two years of disruption and redesign, can we only take a breath before we have to do it again? And kind of the answer is no. We are going to have to do it again because it is such a disruptive technology. We're yet to see the direct impacts, but we're already starting to make accommodations across the higher ed landscape.

Darren Gaddis: How has AI impacted how students learn, but also how faculty members grade?

Fred Martin: Basically, is ChatGPT friend or foe? From one standpoint, we could try to fight it off and basically ban it. And I know that some K12 school districts have done that, and I know that some schools have said that the default policy is students aren't allowed to use it.

I think that's a mistake. I think that ChatGPT and other generative AI, I mean we know already Microsoft and Google are integrating them into the everyday tools that we're going to use. It's going to be integrated into word, it's going to be integrated into email. It's going to be integrated in all the digital tools that we routinely use. And it's a pretty useful thing.

So I think in higher ed, we need to find ways to make peace with it, and make friends with it and teach our students how to use it. When they leave school, they're going to be in the working world, the professional world, and we're going to be using those tools. So it's incumbent upon us in higher-ed to figure out how to support students in appropriate use of tools like ChatGPT.

Darren Gaddis: With this understanding, is there a need to change grading practices given the nuances of AI?

Fred Martin: I was just interacting with a colleague this week who had a student who, English isn't her first language. And in this class, the student is writing lab reports. That's the primary sort of product that's being assessed in the class. And at the beginning of the semester, the student's work was, the writing was not fabulous. There were lots of errors, and this person's learning to use professional english.

And then they turned in a lab report that was grammatically perfect. So the faculty member suspected that ChatGPT was involved. My role in my college, one of the things I do is I support faculty with academic dishonesty cases, and then I have to adjudicate in the cases where students appeal. So I encouraged her to report this and to meet with the student.

So the faculty member met with the student, and the student admitted using it. They were under a lot of stress, and the thing was due, and so they admitted using it. The faculty member is going to give them a chance to rewrite the report on their own.

That one is a clear-cut case that had a happy ending because of the compassion of the faculty member. So faculty are on alert for work that doesn't feel like the student did it themselves.

This makes it a lot easier for students to take these shortcuts in their learning. And so the pressure on faculty is, they care about academic quality, care about student learning. It's another thing to be aware of and worry about.

But I think the next step is for faculty to think, okay, how can I bring ChatGPT, and the things that follow it? And again, it's going to be integrated into our everyday writing tools. So it's something we're going to have to figure out, how to support students in using and learning from.

Darren Gaddis: What are some ethical considerations when grading assignments which might have utilized AI technology?

Fred Martin: I mean, a lot of the work that happens in education is students are making products that are assessed. And there's this dual nature to the work that they do.

So they're making something that's intended to be a learning process, and then the result of that work is assessed. And sometimes the thing that they write up is not the product itself. There could be some other work that then they're documenting. Or maybe the writing product is the main output, and the thinking that went in to make that writing product is where the learning happens.

I mean, it adds work to faculty because you need to evaluate more of the process the student goes through. So there could be more checkpoints where the faculty scaffold students work. So the process is more exposed, and students can more directly reveal the process by which they're learning and producing.

I do think it will imply changes. And broadly speaking, those changes add to the amount of work that faculty have to do to support their students.

There's also an ongoing conversation about authorship. So what does it mean to create work where you've used these tools? I mean, that's at a professional level, that's a conversation. I think it folds back into teaching and learning.

I think the use of tools needs to be disclosed. It's sort of like one of the trends in scientific work is, in journal articles, multi-author articles, to have a narrative of who did what. So it's not just a list of names on the article, but it is that, and it's a conversation about how each person contributed to being co-author of a given paper.

So I think that's a good analogy for what needs to go on with these AI tools that will be supporting our creative work. That when someone creates something they need to describe, how did they make use of slash collaborate with these AI systems?

Darren Gaddis: What else should we know about the usage of AI in grading within higher education?

Fred Martin: When we started out, I was talking about one of the three uses, which is AI supportive grading. I think that's another dimension, is that faculty can use these tools as well.

So it's really, these are professional tools in our world. And I think it's important that we all learn how to use them to become more productive. Right?

Someone will have a conversation about jobs that will be lost because of AI, and then even more jobs will be created. I think the main thing is that so much work will be affected by these AI systems, and the opportunity of accelerating our human creativity and productivity with the use of them.

So I think in higher ed, it's both faculty will be using these tools for themselves. They'll be using them to support student work. That was another dimension of what I shared at the beginning. That a lot of AI, it can surface information in about an individual student or a group of students that then faculty can act on.

So I really believe that we need to make these systems our friends, and be able to be more successful because of that relationship that we have with these digital tools.

Darren Gaddis: Fred, thank you for joining me today. It's been a pleasure.

Fred Martin: Thank you, Darren. Thank you so much.

Darren Gaddis: Be sure to follow, like, and subscribe to On Campus with CITI Program to stay in the know. If you enjoyed this podcast, you may also be interested in other podcasts from CITI Program, including On Research and On Tech Ethics.

You can listen to all of our podcasts on Apple Podcast, Spotify, and other streaming services. I also invite you to review our content offerings regularly, as we are continually adding new courses, subscriptions, and webinars that may be of interest to you, like CITI Program's new Environmental Health and Safety Subscription.

The Environmental Health and Safety Subscription provides organizations with key content areas related to health and safety. All of our content is available to you anytime through organizational and individual subscriptions.

You may also be interested in CITI Program's, AI and Higher Education: An Overview webinar. Please visit CITI Program's website to learn more about all of our offerings.

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On Campus Podcast: AI in Higher Education

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While AI can offer benefits such as pre-written information, human-like language, and sophisticated chatbots, it also presents some drawbacks in the context of higher education.

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Claire Brady

Updated: May 22

I am honored to have been invited to several podcasts over the past few months to discuss my work in championing Artificial Intelligence (AI) in higher education. These conversations have provided a platform to explore the transformative potential of AI in education, share insights, and engage with thought leaders in the field.

Stay tuned for updates as new podcast episodes are added!

Orbis "Mindset in Motion" Podcast, hosted by Dr. Bill Heinrich, May 2024

"Dr. Claire Brady: A.I. in Student Affairs" episode: Today we connect with Dr. Claire Brady, President of Glass Half-Full Consulting! Claire and I were classmates long ago and recently reconnected to talk about Artificial Intelligence in Student Affairs. Claire has served in many roles, including VPSA and now consults with campus organizations about AI, getting started, and what to avoid. Her insights are clear and timely. We also take a listener question from a seasoned Student Success practitioner, Dr. Jeff Burrow from the University of Toronto."

Josie & the Podcast "In my AI Era", hosted by Dr. Josie Ahlquist, March 2024

"Diving into the world of Artificial intelligence (AI) can be intimidating. Or maybe you're unsure about using it at all. In this episode, I'll cover the importance of staying updated with changing AI trends, discuss possible concerns and benefits of integrating AI in higher ed, provide practical and effective steps for experimenting with AI, and recommend several (several) resources for learning more. I am also in the early stages of exploring, so I wanted to seek some advice from my colleagues and hear all about how they're using different tools. Throughout the episode, you'll hear from me; Carrie Phillips, Chief Communications Officer at the University of Arkansas at Little Rock; Claire Brady, President of Glass Half Full Consulting (and a former Vice President for Student Affairs); and Joel Villarani, Director of Social Media at Florida International University."

D'Youville University's AI in Higher Education Podcast, hosted by Humberto Hernandez and Susan Arnold, May 2024

"Join our hosts, Humberto and Susan, in this insightful podcast episode as they delve into the transformative potential of AI in higher education. Featuring guest expert Dr. Claire Brady, the discussion covers a range of pressing topics, including the integration of AI into academic programs, challenges faced by institutions, strategies for maintaining academic integrity, and the role of AI in improving student retention rates. With a focus on innovation, ethics, and practical applications, this episode offers valuable insights into the evolving landscape of AI in higher education.

Want to invite Claire to be a guest on your podcast?

I am honored to have been invited to several podcasts over the past few months to discuss my work in championing Artificial Intelligence (AI) in higher education. These conversations have provided a platform to explore the transformative potential of AI in education, share insights, and engage with thought leaders in the field.

Stay tuned for updates as new podcast episodes are added!

Orbis "Mindset in Motion" Podcast, hosted by Dr. Bill Heinrich, May 2024

"Dr. Claire Brady: A.I. in Student Affairs" episode: Today we connect with Dr. Claire Brady, President of Glass Half-Full Consulting! Claire and I were classmates long ago and recently reconnected to talk about Artificial Intelligence in Student Affairs. Claire has served in many roles, including VPSA and now consults with campus organizations about AI, getting started, and what to avoid. Her insights are clear and timely. We also take a listener question from a seasoned Student Success practitioner, Dr. Jeff Burrow from the University of Toronto."

Josie & the Podcast "In my AI Era", hosted by Dr. Josie Ahlquist, March 2024

"Diving into the world of Artificial intelligence (AI) can be intimidating. Or maybe you're unsure about using it at all. In this episode, I'll cover the importance of staying updated with changing AI trends, discuss possible concerns and benefits of integrating AI in higher ed, provide practical and effective steps for experimenting with AI, and recommend several (several) resources for learning more. I am also in the early stages of exploring, so I wanted to seek some advice from my colleagues and hear all about how they're using different tools. Throughout the episode, you'll hear from me; Carrie Phillips, Chief Communications Officer at the University of Arkansas at Little Rock; Claire Brady, President of Glass Half Full Consulting (and a former Vice President for Student Affairs); and Joel Villarani, Director of Social Media at Florida International University."

D'Youville University's AI in Higher Education Podcast, hosted by Humberto Hernandez and Susan Arnold, May 2024

"Join our hosts, Humberto and Susan, in this insightful podcast episode as they delve into the transformative potential of AI in higher education. Featuring guest expert Dr. Claire Brady, the discussion covers a range of pressing topics, including the integration of AI into academic programs, challenges faced by institutions, strategies for maintaining academic integrity, and the role of AI in improving student retention rates. With a focus on innovation, ethics, and practical applications, this episode offers valuable insights into the evolving landscape of AI in higher education.

Want to invite Claire to be a guest on your podcast?

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Dr. Claire Brady AI-related Podcast Interviews

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I am honored to have been invited to several podcasts over the past few months to discuss my work in championing Artificial Intelligence (AI) in higher education.

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Elevate your stories and start podcasting with ease today. You can schedule a complimentary consulting call with them at university.fm. Learn more about them in the show notes.

Hello, and welcome to Josie and the podcast. I'm Josie and I'm so happy to have you here with me today. What does it mean to lead in the digital space with heart and humanity intact? On this podcast, I spend time answering this question with heart, soul, and lots of substance.

My goal is to share conversations that encourage you, empower you, and sometimes we even entertain you to rethink digital strategy for yourself and the organizations you support.

Welcome! Today, we are going to be talking about artificial intelligence, AI. But before we get into that, I want to share some updates. Over these last few episodes, you have been hearing all kinds of goodness about the Digital Community Cohort. And we have officially kicked off, and it is the largest class yet.

I am in love with this group. We've got folks from community colleges, student affairs, academic colleges. I just love it. So, I won't be promoting it for a while, but save the date for February 2025. Isn't that nuts to even say that number? And we will open up for registration in the fall. And I'm putting this out into the world. I do think that we are going to fill up to capacity the earliest that we probably have.

The second update, the Social Media Student Academy, is a resource for you year-round to be able to your students who are helping you with social media, especially this spring, it could be a great tool to integrate into your training, into your conversations, and/or to put on your wish list to have them do over the summer.

They get it for a full year, so you can register them at any point and they're going to have that access. And we are chipping away at a supervisor version of the academy for those pros that are listening, that you want to sharpen your skills about how to better manage and empower students who are helping behind the scenes.

On the personal side, I'm recording this on Valentine's Day. Also, yesterday was Galentine's. And that was just such a fun day. There were so many memes being sent out in all of the places. And I'm hosting my second annual Galentine's party at my house. I think I talked about this last year, not like I'm expecting you to have memorized this. But it was such a cool woo-woo event where we had tarot cards and crystals. And this year, we're going to have astrology. We're going to make friendship bracelets. I'm just so excited. It's really my own strategy for meeting new people and getting to see women that are already in my life that I just want to see more of.

Back to the work stuff, it is spring. And so, that also means it is conference season. And I am headed to NASPA and ACPA, which are both student affairs conferences. NASPA is in Seattle, ACPA is in Chicago. I would love to know if you're going to be heading there, too. I would love to see you. I'm hosting a couple of fun gatherings that I would also love to invite you to. And at NASPA, I'm doing a session on influencer marketing with someone that you should be familiar with if you listen to Building Community versus Facilitating Community episode, Tim from Chapman, we are presenting together.

This is also a season of a lot of behind-the-scenes support and strategy development for our amazing clients. From roadmaps to audits and more, we've got a lot of goodness we are getting out into our clients.

Today's shorty episode is about robots. Well, not fully robots. I'm talking about AI. Or is it IA? No, I'm talking about AI.

[00:05:40] I'm giggling because I am not, and no one, I guess, unless you created these platforms, you're not an expert. We are all explorers. And so, spoiler alert, the biggest takeaway is it is a season of exploring. While the title of this episode is My AI Era, the season and all of our eras, or whatever other non-Swifty terminology you want to use, is about being in a sandbox and trying things out.

The types of tools that I'm experimenting and, goodness, this episode is going to have a lot of show notes. So, know you can clickety clack over there. At first, I experimented with free everything, especially ChatGPT. And then, I upgraded to 4.0. I have the app on my phone. I also got in on an early edition of Bard, which is now Gemini, within the Google Suite. And I am, OMG, in love. Grammarly has been with me. Gosh, I wish Grammarly was around when I went through my doctorate. My mom was my Grammarly, no joke, Mrs. Hutchinson, the teacher.

Grammarly now has artificial intelligence elements to it, where it will do more than just correcting grammar. It's going to suggest prompts, rewrites. It can help you synthesize or even expand content. And heck, right before I press record, I saw a colleague talk about how he uses another tool called Perplexity, which is, again, a very similar artificial intelligence platform that's prompt-based.

Now, I promise this podcast was only slightly aided by AI. We've got some prompts in the show notes that I'll share, too, that are, again, a really great way to experiment. But also, because I realize I am doing my own exploration, I wanted to seek out colleagues who are going to be contributors to this episode in how they're using different tools. You're going to hear from Carrie Phillips, the Chief Communications Officer at the University of Arkansas at Little Rock; Claire Brady, she is a previous Vice President for Student Affairs and now, President of Glass Half Full Consulting; and Joel Villarini, he's the Director of Social Media at Florida International University.

AI is rapidly evolving. I'm even trying to hold off to do this episode as close to the release date as I can, because within every hour, something is changing. And this is just quite the example of needing to come to terms with that we don't have full control, but what we can control is our willingness to explore, to seek guidance and mentorship, which might come in surprising places throughout the organizational chart, and willingness to try.

AI, or any other technology, is a tool in your tool kit. Now, when I was preparing this, I wanted to make sure we gave a definition that was applicable, in case you have not yet gotten even into the explorer stage. And so, I asked ChatGPT to tell me how it would define itself like I was explaining AI to my mom or my dad. I love you, mom and dad, but I don't think you're using any of these tools, yet.

And so, this was the response. Imagine AI or artificial intelligence as a super smart computer buddy. It's like when you tell your phone to set a reminder or ask a virtual assistant a question. AI is a technology that allows computers to do tasks that usually need human-like thinking, such as learning from experiences, understanding language, or recognizing pictures.

I also wanted to try Gemini, Google's AI, with the same prompt. And it said, AI is used in many things that your parents might already use, like their phone's camera for taking better pictures or GPS, like suggesting faster routes. AI doesn't replace humans. It helps them do things better and faster. For example, AI can help doctors analyze medical scans, but it's the doctor that makes the final decision. And finally, there are different types of AI, and some are more advanced than others. The kind of AI you see in the movies, like robots taking over the world, is still science fiction, not reality.

And then I got a little meta, because I asked ChatGPT4 about this very podcast, Josie and the Podcast. This is where we start to get into, even when you talk about exploring, it doesn't have to be all about work, even though the podcast is about work. I asked it something very meta. I said, "What is Josie and the Podcast?" Knowing that, especially the paid versions of this platform are pulling from more recent sources of, basically, the internet. And it said, Josie and the Podcast is a podcast series produced by Josie Ahlquist-check, check-produced on exploring the intersection of leadership, digital communication, and social media within higher education. Josie Ahlquist is a speaker, author, and consultant with a background in student affairs and higher education, interviews leaders, professionals, educators, and students about how they use social media to foster community engagement and identity in the educational landscape. Pretty good!

It goes on to say a few more things, but it does check out. After that, I said, "Thank you. What do listeners think of Josie and the Podcast?" And then, it replied, listeners of Josie and the Podcast rate it highly. Oh, thank you. With a perfect 5.0 out of 5 stars based on 17 ratings on Apple Podcasts. And again, it goes on and on. And then, I asked it even further, "Should I listen to Josie and the Podcast?" You can replace this with a restaurant, another podcast, a hotel, or a country you're looking at visiting, because what it's doing is scouring the internet, like it said in the last prompt, it pulled reviews from Apple Podcasts.

And so, when I said, "should I listen to it," it said, "Well, if you're interested in digital leadership, higher education, and how to effectively use social media for community building and engagement, it could be a valuable resource. It's got lots of positive reviews, insightful content, hosts is able..." Oh, ChatGPT, you make me feel so good. I have also heard such fun prompts, like Moira from Schitt's Creek, when you ask it to write a letter to, I don't know, whoever, in the voice and tone of Moira about anything, there's so many transcripts about Schitt's Creek on the internet. You could do any character out there and just see what it gives you. So, have some fun, be playful. And now, I really want to go do that one.

So, that's a couple examples. But as we go back to this tool versus solution, it is a tool. It's not magic. It's an asset, but it still requires humans and humanity and, one would even say, values. According to HubSpot AI Trends for Marketers, their 2023 report, the four top uses within marketing right now is for creating content, analyzing and reporting on data, learning how to do things, and conducting research. What I would say the theme of all those things are is the very beginnings of what would come and be needed in more advanced skill sets. You're going to hear from folks later the ways they use AI, but think of it as like the outline, the way that, if you ever get stumped, having to start writing on a blank page, AI is going to be your new best friend.

And we're finding AI adoption is quite widespread. In this same HubSpot report, they are finding 62% of business leaders say their company has already invested in AI and automation tools for their employees to leverage. Of those, 71% report a positive ROI and 72% say AI and automation make their employees more productive. Win, win, win.

Let's dig in a little bit more onto that. As we've seen productivity and ROI go up, this also means we're saving some time. So, for marketers who might spend four hours a day on manual administrative and operational tasks, we might be able to save a little bit of time in drafting content. But we still need to put in the time up front to learn some of these AI skills, especially as new platforms and technicalities are coming about.

Okay, so, let's hear about higher ed professionals and how they're using it. I asked my network for a variety of use cases and seeing how they're integrating it in work and daily life. So, we're going to hear from Carrie.

I also love using it for administrative things-so, helping me craft emails, helping me check in on topics, and making my life easier in that sense. And I also really love doing research with it. And one of the great things that I've been able to do is, kind of, get ideas and see how our students are thinking about it. And so, that's been really helpful for me to do that, as we're talking about marketing the institution.

Claire says she uses AI to save time, like that HubSpot research. Administrative efficiency, productivity. It also is helping her improve her writing and research. And even her professional headshots she uses across social and her websites are AI-generated.

Second, and this is both personal and professional, since English is not my first language, I rely on AI tools to tighten up my writing. And when necessary, it helps me with my existential crisis with "in" and "on." I know it sounds simple, but oftentimes, I'll use one, and yeah, it's a whole thing.

When working on presentations I use AI-generated images to help drive or supplement a point. I also use AI to analyze paid media performance spreadsheets to help me find those hidden nuggets or look at the data through different lenses.

So, as we evolve our skill sets, without pressure of feeling like you need to be an expert, there's a lot of free resources and free platforms that are out there. And I've mentioned this idea of being an explorer because it's a framework that I started to use back in 2015 when NASPA and ACPA rolled out student affairs technology competencies. And rumor has it, they are updating their joint competencies for student affairs professionals very soon. Nowhere in the tech competency does it talk about AI. It might refer to social networking. I can't remember. Honestly, if I was to give notes to NASPA and ACPA, it should not be platform-based. They might list some examples, but if we are only updating our competencies every 10 years, if we're lucky, it's not going to adapt if we are actually including platform titles.

But as I took the technology competency, I applied it to a model of being a digital explorer, digital educator, or a digital influencer. And those competencies, it, kind of, pairs nicely as the knowledge, skill, and disposition gradient of the competencies was, kind of, like, introductory, moderate, and advanced.

But within technology, there are some positions and even team sets, it's okay to be at different places. That doesn't actually mean you're behind. It's almost like an adaptation model. So, digital explorers, for example, in AI, you are in a constant state of exploring. This most likely means this might be your first experience with it, and/or you're just always going to be exploring AI because you may not be the one to actually apply it. And so, if we were to apply this to social media, I would say within a department, not everyone needs to be the digital influencer of social, because they don't need to necessarily apply it. They don't need to be advanced, but we do need them to, at least, know some core language, for example, of TikTok and Reddit, because that's where our students are.

What we know with AI is there's adoption across a variety of demographics, including our college students from a variety of ages. Okay, explorer. We all should be explorers.

Digital educators. Now, this is something that I always aim for me, personally, to be at. I would also say educators may also find their own personal interests and/or just naturally they're, kind of, tech coaches. I found this early days within MySpace, like, friends would ask me, like, "Hey, how did you code this thing?" I was like, "I don't know. I just figured it out!" So, again, sometimes disposition has a little bit to do with it and enjoyment.

But educators in AI, you're actively implementing AI in your own life. You're doing that exploring, but you're implementing it over in workflows, tools, and you are seeing the payoff in time-saving techniques. And you are naturally educating, cheerleading, potentially, and sharing what you've learned. So, not just, you have to be on this tool, this is how to do it now, but you are showing the impact.

Now, the final level of this digital influencers, and I definitely sought out people to include in this episode who are very much early influencers, but they're at least willing to, kind of, stick their necks out there to, like, show their own wins and, maybe, stumbles through AI. And you are sharing, for example, in AI writing prompts, you might be using advanced cases, sharing case studies, you're integrating and establishing workflows. You're probably advocating for resources. I think I saw, we'll have to find out and remember what institution or it may have been a whole system invested in an AI tool. So, now, the entire campus has access to the advanced platform that they were using.

I would also say digital influencers and AI, they're not being sponsored by any of these tools in that influence sense, but they're probably blogging about it, maybe talking about it on a podcast. They might be doing workshops or chatting on LinkedIn about it. So, they're just. willing and very much interested in leading in this area.

So, whether you identify as an explorer, an influencer, I want us to start to be more open about AI and how it's going to change and evolve, because it's going to change and evolve a lot. What higher ed does, often, and sometimes in all industries, this can be helpful, because there have been times in the past where we've gone through a technology adoption curve, maybe, too quickly. And then, now, looking back, it was like, how a lot of people smoked in the past, and now we see the negative benefits of that. We're also seeing that with, maybe, children or early teens having too much technology or social media influence.

However, in AI, it was very much doom and gloom immediately. We jumped too far into that side. I love my faculty. I am, or I was, a previous faculty. We got scared really quick. And I don't know if anyone remembers this, but there was actually a time that in middle school, high school, and college classrooms, when grammar check was first applied into Word on desktops, that it was, in some places, not allowed. You could not use a spelling check. Can you imagine that now? Like, I almost feel like that's so mean. Like, why, why wouldn't you want me to try to spell the best that I can?

So, I also want you to think about this as an accessibility and equity discussion, thinking about these evolving tools and the ability for access to information, where, for example, in the past, in order to get your papers edited by someone maybe that has that advanced skill set, you had to pay. And now, you don't. We can have Grammarly.

That said, there is a dark side to AI and anything that's moving very quickly where, you know, we're all trying to honestly get our feet under us. Inside higher ed, just in 2023, they shared a report about the benefits and challenges of AI. And of course, concerns are arising about the reliability and ethical implications of AI. I would not be surprised when you're throwing in some prompts that AI, literally, can have a mind of its own and fills in the gaps, that there have been numerous cases where made-up citations have been added.

One may also worry about taking the human out of higher ed, that, well, we add in more bots and automations is going to depersonalize student interactions. There might be biased decision-making, inhibiting intellectual growth. You know, we want to advance intellectual thinking. And so, it's taking those layers out of it. And so, I, I acknowledge that as well.

And just overall, the ethics of AI-who's creating it, where is it coming from, how do we review privacy, accountability, equity, access, fairness, all those things. Academic freedom, plagiarism, how does that all fold in?

And then, of course, there is equity of just accessing AI. Some of these platforms you already have to pay for to get a higher quality. And so, who's being left out of the most timely and innovative tools? The Chronicle came out with an article about the higher ed arms race around technology. I'd encourage you to check out.

[00:28:46] There is a podcast episode that I would definitely recommend that you check out, called Beyond Bricks and Clicks . It was on Higher Ed Pulse Podcast. They talk about the future of higher ed marketing. And it was my good friends, Mallory, Wilsey, and Seth Odell, A, just subscribe to this podcast. But B, this one is especially timely, as we think about the AI evolution in marketing and enrollment.

And Seth comments that this space and this idea of beyond bricks and clicks, it's beyond growing, it's beyond the clicks of getting people into our website. As we think about technology integration into the learning experience and not just adding more to your tech stack, it's going to be more and more our focus.

And then, the two of them also talk about going beyond the sizzle, and those that are residence life, student affairs, that technology, and even AI can turn into the lazy river of student life. Is a lazy river cool? I love a lazy river. Do we need one to be part of the collegial student development journey? I don't know. But there is going to be some lazy rivers of AI. And we have to stay grounded in outcomes-based, as we adopt technology and AI. We want to focus on leveraging technology for the meaty stuff in what Mallory and Seth talked about. Are you focused more on the sizzle rather than the steak? And now I'm hungry.

[00:30:46] Josie and the Podcast is sponsored by Element 451. And I cannot wait to tell you about a game-changing event called the Engage Summit. It's happening in Raleigh, June 25th and 26th.

Imagine a conference, and I love conferences, where AI isn't just a buzzword but the key to the next generation of tech-driven student engagement. From reshaping student outreach to streamlining administrative processes, the Engage Summit dives deep into AI's role in transforming you and your institution's success.

At the Summit, you'll be joined by industry leaders and innovators for two days of workshops, discussions on AI-driven transformations, and lots of networking. Tickets are $149, but with the code, JOSIE50, you get a discount that brings the price down to only $99. Hurry! This offer ends April 12th. Visit engage.element451.com to learn more and get registered.

[00:32:16] So, how do we move forward with AI with some, and honestly, many things that are unknown, but how we can make sure we have the lens of it being equitable, informed, and centered in humanity? And you could actually replace AI with technology, with social media, with any kind of tech enterprise. Within AI and higher education, how could AI impact the student and campus relationship, moving forward? Let's hear from Carrie, CMO at the University of Arkansas, Little Rock.

And so, I think for us as institutions, it's making sure that we're thinking about how that plays out and how that works. I think it's still a lot in progress, but I can't help but think that editing content on our website, making sure that our website's up to date, I think those are key pieces in terms of how students are searching AI that we can be doing to make that process easier. I also think that this gives us the tool to really bring that hyper-personalization into our website through chat features that are truly AI, through, you know, recall and plugging things in, in a way that we've never been able to do before.

And so, I hope that institutions really find ways to use this to provide good customer service to our students. You know, we know up-and-coming students, especially, are really looking for that experience. And so, I feel like this is, maybe, that missing tool that we've been needing to get our websites really personalized and make it easy for students, much like the corporate world has already figured out and has already been doing.

So, how can you be not just preventative but proactive in how you can add in layers to your materials and assignments that account for that? They're students on their use of AI. We could also help use this as a learning outcome, knowing that by the time they graduate and/or are already getting internships, they're probably going to be tasked with AI tools for integration.

So, how are we integrating it from the student leader experience to the student employee experience and in the classroom? Let's hear from Claire about the benefits of AI for student development.

The possibilities to impact students with disabilities is astounding. Assistive technologies have been dramatically improved through AI in cost, in access, and in quality. I believe that career services, tutoring, learning centers, and writing centers are areas where I see AI revolutionizing how these professionals do their work and how they impact the student experience.

And finally, I'll mention chatbots. Chatbots are improving by leaps and bounds through AI and are impacting student outcomes in really powerful ways. Ocelot reports that over 40% of chatbot interactions happen after traditional business hours. AI can help us to scale up our student services and offer them 24/7 at times when our traditional staff are not available, but students are seeking information and they want to engage with us.

So, there's that. But let me bring it back to this need to experiment, because I am joining you in this AI experiment era. With any new software solution, any new social media platform, it's critical to experiment, question, and be curious. So, how do professionals who are already very busy take time to learn? Carrie puts an LMAC hold on her calendar each week. I know I wondered it, too. What does LMAC stand for? And it, and I love it, and it should be t-shirt, Leave Me Alone committee. This gives her dedicated time each week to dig into new trends. Right now, she's been leaning into understanding AI more during these times.

Inside Higher Ed had a very interesting article about this. We are seeing dozens of faculty being hired and large-scale AI-focused centers popping up at different campuses. For example, University of Albany has plans for 200 million to go into AI. A 600 million donation to boost artificial intelligence work at Indiana University Bloomington. Miami Dade College spending 5 million each for AI centers on two campuses. And ASU, always an innovator, has a partnership with OpenAI, rolling out personal AI tutors for their students and a ChatGPT partnership.

So, I want to get even more specific and experimenting, not just holding calendar time. And this is where you're going to get lots of resources in the show notes. Practical steps. There are free accessible tools, like ChatGPT and a tool called DALL·E, which can be a good space for learning. We've got online courses and tutorials from our friends at Element451, including a prompt engineering course specifically designed for higher ed pros.

I would also, for those that don't want to experiment right away, you want to consume and read. There are lots of guides, also from our friends at Element451, from AI Readiness Assessment Guide, the Definite Guide to AI and Higher Ed, ChatGPT Cheat Sheet for Higher Ed Marketers. And of course, for the podcasters listening, we'll link to a few, probably more than a few episodes that talk about generative AI, ChatGPT, and more.

I also think it's so exciting to be able to connect as we all explore in a networking sense. So, we obviously have the Engage Summit, but any conference that you're going to in the next year, I would not be surprised if at least one keynote or workshop or maybe even a breakout is about it. And just come to explore. Don't feel like you've got it all figured out.

And then, next is you've got to actually put in some time. I'm not saying 10,000 hours, but you do need some hands-on experimentation. And as I said earlier, you can make it fun. What would Dora the Explorer answer to answer the question of, what is the University of Albany? Or, who is Josie Ahlquist? Wrong answers only. I don't know. Just have fun with it. Professional development opportunities, while there is a lot of free ones, I think the most informative. And honestly, practical and tactical ones might have a little bit of funds that you'll want to prepare for, even though a lot of this stuff is changing often.

I would also say, like Carrie schedules time, could you schedule time into your or your team, department divisions, to come together in a computer lab or all bringing out our phones and we're just, kind of, playing around or we're sharing what we're learning?

And finally would be to collaborate with experts. There are individuals... I use experts with a little bit of a smirk, but there are influencers, both in our industry and others, that have a willingness and excitement about AI, from the beginning or will continue to be jumping in now. I would say Claire and Carrie are just a couple of those examples where they're blogging about it, they're writing about it a lot on LinkedIn. And so, I'm just getting a lot out of following them. And of course, Mallory at Element451.

I've got a few more recommendations for you, as we get even more specific. Now, another person to keep your eye on is Bart Caylor. He's an education marketing strategist, brand communicator of Caylor Solutions, and he's a Forbes contributor. He gave us an epic list of steps to take to optimize AI from, and this is going to be more advanced, you can create personas. You can share previous materials you've already created to get to know your voice and tone. You could even treat AI like your smart intern.

And so, some of these recommendations include, well, one, use paid versions, like ChatGPT4. But keep in mind, I did not upgrade to 4 until I was using the free version for months and months. They also recommended a tool that's nearly as good called Claude, Claude AI. We'll link it. C-L-A-U-D-E.AI. again, number three, always give it a persona. For example, act as a higher ed marketer for (insert your institution). So, my example of, "Write like you're Moira from Schitt's Creek answering this question."

Number four, upload previous materials you want it to mimic. So, are there existing documents, blog posts, emails, et cetera? You could frigging upload your mission statement, so it already knows the salient language.

Five, ask it to create questions to give it the information that it needs for the best response. He says, "This is critical. Otherwise, your responses will be disappointing." Two more, have a conversation like it's a really smart intern. Don't throw it out when it's not the answer you're expecting. So, for example, I, many times, will say, like it's a real intern, "Thank you." And then, almost like giving it soft feedback. And then practice, repetition, over and over.

Next recommendation, give AI a frame of reference for your expertise and tone. This was shared, also a bit by Bart, Claire gives us a little bit more here, that we should always give a context for tone, writing style, and expertise. If you look at your emails or anything you've written or said, you have a personality. You have a voice, and so does your institution, your department or division. AI is not the space for filler words or jargon but clarity in improving that output.

So, she recommends, and what she says, "I tell ChatGPT that I'm an excellent writer with 20-plus years of experience. I don't use filler words or jargon. And what field I work in-higher ed, nonprofit. this small change has dramatically improved my output, like the results that you get."

And so, when. asked how higher ed leaders and marketers could build their AI skills, this is what Claire had to say.

Think about your most mundane administrative tasks in your work week and see if there's an AI tool that could automate that task or make it easier for you or less time-consuming, something like meeting minutes or reading a large volume of industry research or articles, digesting assessment data, or even writing LinkedIn posts. There's an AI tool to help you do all of that.

Now, if you're getting stuck on prompts, Joel says it's all about prompt engineering. So, listen to what he has to say.

I'm going to quote my friend, Ben Olarios. It's not about knowing everything, it's about having the phone number of those who do. While prompt engineering will give you all the phone numbers you need and then some more, don't worry about knowing and trying out every AI tool out there. Focus on the skills needed to work with these tools and how to connect them to other services; thus, expanding their functionality.

If you're in the advancement space, which I think is a space that sometimes gets a little forgotten about as we think about AI, Evertrue is doing some really great work around how our advancement colleagues can be using this to personalize their work and to build out some of the things that they're doing as it relates to proposals and follow-up conversations, thank you notes, things like that. And then, I think Mallory will see in the entire team at Element451 are doing a really good job talking about it in terms of search and chat and all of those spaces. So, lots of really great resources out there.

But I'm always for the fun. Make your work, especially in experimentation phases, fun and entertaining. That's also the third goal of this podcast. So, here's one to throw in there. "I'm a higher ed marketing professional and only have 20 minutes in my day to learn new skills that I need. What resources do you recommend for learning AI and higher ed right now?"

Or, maybe this one, "I am a student affairs professional who's unsure if AI is going to benefit my role. What resources do you recommend that are easy to understand and will only take an hour of my time?" And then you could add on, "Give these in the voice of Moira from Schitt's Creek." It'll be fun.

I would love to hear how you are implementing AI in your work, in your personal life. Maybe, who have been your early educators and influencers of AI? I am learning right along with you. As we continue to build frameworks, and even more importantly, research about how we intentionally and with the goal of making these tools have an impact on student engagement, learning retention, and the list goes on.

And of course, I'm so excited to share about the Engage Summit. Remember until April 14th, you've got a discount, JOSIE50. This whole thing isn't a sponsored episode, I swear, but there are not a ton of conferences out there specifically for higher ed and AI. And this is just going to be a mind meld of, if you're excited about something like this, that is going to be your place. I think that's going to be the biggest bang for your buck.

I mean, it's only $99! That's nuts! I bet your flight will cost more than that. Go, visitengage.element451.com. Learn more. Get registered. Let them know I sent you.

I can't wait in a year when I do an updated AI episode, what is going to change, what lessons we've learned. And no matter what, though, what I know won't change is how important we keep the humanity into our work in higher ed, whether if it's AI or on Instagram or how we write emails. Let me just boil it down to that.

[00:56:06] Thank you for joining me in this shorty episode of Josie and the Podcast. Find me online. I'm @JosieAhlquist, and the podcast is on X, Threads, and Instagram. Remember, those show notes, and there are a lot of them today, can be found at josieahlquist.com/podcast.

I would love it if you were to make sure you're subscribed, give me a little review, and share this episode. If you're interested in learning more about my speaking, consulting work, or my book on digital leadership and higher education, you could find that at josieahlquist.com.

Thank you, again, to our podcast sponsors, University FM and Element451. I'm sending digital hugs, loves, and waves to whatever corner of the world you're listening in from. This has been Josie and the Podcast.

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In this episode, I'll cover the importance of staying updated with changing AI trends, discuss possible concerns and benefits of integrating AI in higher ed, provide practical and effective steps for...

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Episode 18: Exploring AI Transformation, Challenges, and Future Directions with Dr. Claire Brady

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Embark on a journey through the realm of AI in academia with this podcast, brought to you by D’Youville University (https://www.dyu.edu/) and hosted by Humberto Hernandez and Susan Arnold. Engage in unscripted and authentic conversations as we delve into AI’s impact on personalized learning, administrative and academic efficiencies, and cutting-edge educational frameworks. Benefit from the expertise of our guests who are shaping the future of education. Gain valuable insights into how AI tools seamlessly integrate with effective teaching methods and evidence-based health education practices.

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Credit: Qpt / Shutterstock.com © 2023

Generative artificial intelligence (AI) is in a renaissance amid a profusion of new discoveries and a breathless frenzy to keep up with emergent developments. Yet understanding the current state of technology requires understanding its origins. With the state of AI science changing quickly, we should first take a breath and establish proper footings. To help, this article provides a reading list relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Artificial neural networks have been around for more than sixty years. We can start in 1958, when the perceptron was initially implemented on a computer. For the first time, an algorithm used computing power to ingest labeled sets of data and classify them into categories. Nearly thirty years later, in 1986, backpropagation was introduced as an algorithm for training a neural network to learn from its mistakes. This advancement enabled multilayer perceptron networks to learn in nonlinear ways. In 2017, the Transformer was created as a neural architecture that improves on the memory limitations of past recurrent models. This efficiency gain enables a network to better handle the context and relevance of the information it has been provided. All three of these technologies remain relevant and used today.

There is a deep historical record about practical developments in neural networks, and the theoretical work supporting them includes centuries of rigorous discoveries in math and biology. Interestingly, despite this rich context of empirical and conceptual work, neural networks have not received much popular attention. AI developments have broken into the collective consciousness only occasionally-for instance, when the IBM supercomputer Deep Blue beat world champion Garry Kasparov in a chess match in 1997 or, more recently, when Google DeepMind's neural network AlphaGo defeated Go grandmaster Lee Sedol in 2016. Instead of AI developments, computer and data networks have commanded most of the attention of technologists in the past few decades. While AI has been relevant and useful in our lives for at least the past ten years, the barrier to entry has been high, and its applicability to higher education has not been obvious. The time has come, however, for IT professionals to focus on AI in its various forms, not because it will solve all problems but because it will transform how organizations operate.

Neural Network Basics

Since this is a reading list, we'll start off with a video! How could we do such a thing? Because at this stage, only a human author can make such a contradictory statement. If we must condense decades of knowledge about neural networks, then it is necessary to focus on the fundamentals. Video is a helpful tool at the outset because neural networks are fundamentally visual. This is partly because neural networks are based on visual math (e.g., calculus, statistics, and linear algebra) and also because the multiple layers of networks are inherently multidimensional concepts.

Produced in 2018 by 3Blue1Brown, the video "But What Is a Neural Network? | Chapter 1, Deep Learning" occupies an important moment in time: before Transformers began to accelerate NLP models. It presents a classic challenge in AI development: how to handle recognition of handwritten numbers. This video articulates the fundamental components of modern neural networks including the concept of supervised learning. It also provides an example of the conceptual problem presented by hidden layers.

Now that we've seen a general representation of how a neural network functions, it is time to expand our horizon: there are many types of neural networks, each with particular strengths and weaknesses. Fjodor van Veen's list, "The Neural Network Zoo" (The Asimov Institute, September 14, 2016), will at first seem overwhelming, but the goal in reading about these is not to commit this information to memory. Instead, consider each network permutation as being illustrative of the many possible tools that can be used to solve different problems.

The first category of networks that we need to understand in greater detail consists of recurrent neural networks (RNNs), developed in the 1980s. RNNs allow for nodes to interact with each other, enabling the network to handle sequential data (e.g., words in a sentence must be in sequence to be understood). The interaction of nodes within the hidden layers enables the network, with sufficient training and fine tuning, to predict future steps in a data sequence. Eniola Alese's article "Understanding RNNs Using the Game of Chinese Whispers" ( Medium, April 13, 2018) is a helpful introduction to the subject.

In "And of course, LSTM - Part I: The ABC's of the LSTM" ( Medium, June 8, 2018), Alese introduces some of the challenges with RNNs-namely that they have memory problems. In 1997, the long short-term memory (LSTM) neural network was developed, solving this problem. LSTM introduced a memory state to enable the network to retain relevant information in order to understand context, while discarding information that is no longer needed.

While LSTM networks can handle much larger inputs, they are slow to train, and they are less efficient running on modern GPUs that are designed for parallel processing. Despite these limitations, LSTMs continue to be relevant and useful for some tasks (as are RNNs). Two decades later, Transformers were created to address some of these limitations. This technology has largely driven the current revolution in NLP. Transformers have some powerful advantages, including the ability to better understand the context of input data due to a self-attention mechanism that is more precise about word dependency and relevance. Jay Alammar provides a high-level conceptual overview in "The Illustrated Transformer" (June 27, 2018), whereas Peter Bloem gives a nice introduction from a programmer's perspective in "Transformers from Scratch" (August 18, 2019).

Definitions

Note: The definitions listed here are for concepts mentioned in the article. They do not cover all aspects of neural networks.

Fundamental Components

Hidden Layer: A generic term for all layers between the input and output layer. Different configurations of layers and neurons result in different types of neural networks.

Neuron: A multi-layer neural network node that receives multiple inputs with distinct parameters, which are processed through an activation function to produce various types of output.

Parameters: The weights and biases associated with each neuron in a network. These constitute the primary means to tune network functionality and behavior through training.

Perceptron: A single-layer neural network node that receives information (input) and, with the help of an activation function and parameters, produces a binary output.

Neural Network Types

Artificial neural network: A type of software that can independently learn at the expense of reduced transparency. In traditional software, a programmer defines fixed parameters and designs a structure based on logic. Neural networks, like other machine learning algorithms, are not deterministic in this way. Instead, they are probabilistic: they can handle novel tasks without explicitly being designed to do so. Training determines how the software operates, which means that a given neural network can behave very differently based on how it is trained.

Long Short-Term Memory (LSTM): A general improvement to the RNN design. Nodes can retain relevant, specific information from previous nodes' outputs. This is made possible with a dedicated memory mechanism that is discerning (based on training) about what information should be retained, discarded, or output at a given point in time. These networks are able to handle complex sequential data (e.g., several paragraphs of text).

Recurrent Neural Network (RNN): A network design in which nodes within the hidden layer receive partial information about what was processed by previous nodes. This allows for a type of short-term memory wherein a given node may have condensed information from a previous node's outputs. When combined with new input, this enables the network to make predictions about sequential data.

Transformer: A neural network design that ingests all the input it receives simultaneously, analyzes that information to determine which parts are the most relevant (self-attention), and then uses a basic feed-forward network to determine output. The problems of limited memory caused by handling input in a rigid sequential order (e.g., LSTMs and RNNs) are solved by a holistic understanding that enables the transformer to handle highly complex data types.

Neural Network Training

Backpropagation: Developed for supervised learning, an algorithm that computes gradients (essentially efficiencies) in accord with a loss function and propagates these calculations back through all network layers, adjusting parameters in order to improve network accuracy.

Few-shot learning: A variation of supervised learning in which very little labeled data is provided to the neural network. A sufficiently sophisticated neural network can interpret a well-curated limited dataset and accurately extrapolate future matches for data and labels it has not seen in training.

Reinforcement Learning from Human Feedback (RLHF): A training technique that can be used independently or in combination with other training techniques. The user helps train the behavior of the neural network by rewarding some responses over others, thus influencing the way the network modifies its own weights and biases.

Supervised learning: An ideal training technique for neural networks that are designed for well-defined datasets. Engineers will curate a set of data with labels, which in effect is like providing the questions and answers for a test. When the neural network processes this data, it adjusts its parameters based on its own results. If it succeeds in correctly labeling the data itself, certain connections between nodes will strengthen; if it fails, those connections will weaken.

Unsupervised learning: A quick way to train a network on things like grammar and knowledge without explicit labeling. Engineers curate a set of data and ensure that it is relevant and clean, but no labels are provided. The neural network produces value in this scenario when it is able to successfully identify meaningful patterns in the data. A loss function is used but, in this case, for optimization of the network rather than accuracy per se.

The Building Blocks of ChatGPT

With some history established, we can now review important articles published in the past few years. To start, note that OpenAI (the creator of ChatGPT) was a nonprofit organization founded in 2015 by well-established industry leaders with the intent to freely collaborate on AI. In June 2018, OpenAI published "Improving Language Understanding with Unsupervised Learning," which makes the case for unsupervised learning using Transformers. Unsupervised learning is the process of providing data to a neural network without telling the network how to understand the data (e.g., giving the network images of animals without labeling them) and configuring the network so that it can figure out, on its own, what is needed. In the case of GPT-1, 7,000 books were fed into the model to train it to accurately predict future words.

Eight months later, OpenAI announced GPT-2. This version demonstrated capabilities that both exceeded expectations and demonstrated emergent capabilities: ways of operating that the developers had not anticipated. This led OpenAI to do something that didn't garner a lot of attention outside of AI researchers: it restricted the release of GPT-2 due to fears of malicious use (e.g., generating spam, producing fake product reviews). In May 2019, a limited version was released, followed in August by a model at something like half its capability (measured in parameters), and in November 2019 the full GPT-2 model was released. Once again using very accessible language, OpenAI describes GPT-2 in "Better Language Models and Their Implications," which includes a section on policy implications and thus begins to grapple with the societal impact of this technology.

Six months later, in June 2020, OpenAI released GPT-3, without any staged release or restrictions to capabilities. Instead, OpenAI decided to create a for-profit arm and refrained from releasing the source code to GPT-3. The key breakthrough in GPT-3 is that after increasing the size and configuration of the model, OpenAI discovered that the model was now capable of few-shot learning: performing supervised learning with very limited data, enabling rapid adaptation. Thus, while the previously discovered practice of unsupervised learning continued to be followed in GPT-3, the supervised learning portion of training was dramatically improved. Unfortunately, OpenAI did not provide the same high-level summary as it had for GPT-1 and GPT-2, but it did make up for this change of tack with a very thorough technical paper: Brown et al., "Language Models Are Few-Shot Learners" (July 22, 2020). While this paper requires some knowledge in the field, it explains the relevance of this technological advancement.

In March 2022, OpenAI released GPT-3.5, with GPT-4 following just a year later, in March 2023. The chatbot product known simply as ChatGPT-launched on November 30, 2022-for the first time provided an easy, web based interface to the GPT-3.5 model. Something that is notable with these releases (particularly starting with the November 2022 reconfiguration of GPT-3.5) is the dramatic increase in emergent capabilities. This time, OpenAI did not publish a high-level overview for ChatGPT. Instead, the web page "Introducing ChatGPT" simply describes what the developers saw as some neat but essentially "iterative" changes.

In February 2023, Stephen Wolfram, a pioneer in the development and application of computational thinking, wrote a very thorough blog post: "What Is ChatGPT Doing . . . and Why Does It Work?" Wolfram summarizes how ChatGPT works. It is a long read with some math, but it is an outstanding resource.

Meanwhile, AI researchers became increasingly excited and concerned about what GPT was capable of. They started to notice that entirely new capabilities were possible. Wei et al. described this phenomenon in "Emergent Abilities of Large Language Models" ( Transactions on Machine Learning Research, August 2022). The co-authors noted societal risks, including behavioral and sociological concerns.

NLP Model Ethics

NLP models like ChatGPT raise a variety of ethical questions. While OpenAI has operated with intentionality with respect to ethical concerns, monetary and corporate interests likely mean that the company's ethics are not guaranteed. Considering the exploding growth of alternative generative AI models, however, many of these products will face much less scrutiny and may have far more significant ethical lapses. But before we can discuss ethics particular to AI, we should consider an ethical framework. Hegel's theory of responsibility is a helpful mooring: social context is essential for interpreting ethics, and for any given action, it is also important to consider responsibility for the consequence of this action. Further, knowledge of consequences is imperative for interpreting responsibility.

Starting with the ethical question of bias, OpenAI has put in significant work to train the GPT model to be mindful of social context and to manage for bias. This is quite unlike Microsoft's infamous 2016 chatbot Tay, where the problem of AI bias took on an extreme form. The relatively positive results of GPT demonstrate the importance of supervised training. A pressing concern is that competing models have not been trained as intentionally; in addition, "desktop" generative AI models, which are accessible to anyone, could become unethical by design or accident. In "Language (Technology) Is Power: A Critical Survey of 'Bias' in NLP" ( ACL Anthology, July 2020), Blodgett et al. provide an excellent critique of bias research in NLP models and make a compelling case for why much more work is needed to improve bias recognition and training in NLP. In "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" ( FAccT '21 Proceedings, March 2021), Bender et al. address ethical concerns about increasing NLP models to sizes that may amplify bias and create adverse environmental impacts.

Misinformation is another significant concern. Generative AI models, including GPT, "hallucinate" or confabulate by making up information. This is particularly jarring because the confabulations can appear authoritative: made-up sources sometimes reference real authors but fake titles and even specific page numbers. Such faults can be addressed through supervised training: try asking an NLP model about the company that produced it, and the results will be very consistent. We have also seen GPT's dramatic reduction in confabulation errors as new releases come out, thanks in part to reinforcement learning from human feedback (RLHF), in which users rank responses in terms of accuracy. Meanwhile, a product like Bing Chat has attempted to address this issue with a "creativity" setting with the intended goal of confabulation. This is an ethical position that requires nuance, since it posits that when an NLP makes up information, it is being creative rather than misleading. In "Training Language Models to Follow Instructions with Human Feedback" (March 2022), Ouyang et al. make a compelling case for using RLHF in NLP.

A similar consideration is the impact that this technology has on society. We continue to struggle with the ethical impacts of social media, a technology that is also driven by AI. Since this different form of AI has been used to disseminate fake information, it is apparent that NLP models can be used in a similar manner. Social media AI is, ironically, designed around controlling our attention. One of the consequences of controlling attention is algorithms that promote extreme viewpoints to keep users engaged. This, in turn, leads some people to create socially siloed cultures. When NLP bots are pervasive and anonymized across platforms, how will that change how we interact with each other? What does it mean for our understanding of reality when so much of our society is digital and when the digital output of AI is indistinguishable from the digital output of a human? Will NLP models take a step further than social media and create a distinct AI culture? Hunt Allcott and Matthew Gentzkow provide a thorough account of the role of social media in influencing different kinds of social behaviors in their article "Social Media and Fake News in the 2016 Election" (Journal of Economic Perspectives, Spring 2017).

Privacy and security are complicated ethical concerns for NLP, since we understand very little about the possible consequences of emergent capabilities. Spam and phishing are obvious concerns: grammar mistakes will no longer be a telltale sign of fake solicitations. There are also immediate concerns about the data directly shared with the model (and breaches have already occurred). Yet the ethical questions run far deeper. To what degree can exposure to limited information about a person allow for excellent impersonation of that person, rendering so much of our security infrastructure invalid? To what extent can a model learn about an individual and accurately predict how that person thinks and acts in order to manipulate someone to take an action? How much information will be necessary to determine someone's relationships, habits, and preferences in ways that surveillance states could leverage? If the business model of social media AI is advertising (and thus, AI is influencing people to buy products), what business models will emerge for NLP? How will the mechanism of autonomy on the internet, sometimes used by humans in problematic ways, allow an effective mechanism of responsibility for AI? Indeed, autonomy and privacy are likely on the verge of reformation. Brundage et al., The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (February 2018), provides a comprehensive overview of various security problems posed by AI. While the report is long, it is essential reading and provides a framework that will hopefully be updated in the future.

The crux of AI ethics is accountability and transparency. A key component in neural network functionality is the hidden layers that lie beyond the direct control of programmers. When emergent behavior happens, the practical result is that a black box effect has been extended to the output layer: unanticipated behavior or capability has been realized. There is an important ethical distinction between not knowing how something is done and not knowing what the network is able to do. For every novel permutation of different outputs, we have no mechanism to evaluate the ethical implications of the consequences that may follow. Whether we follow Hegel or other ethical philosophers, understanding how these models produce specific results and understanding the full spectrum of what they are capable of producing is essential.

Technologists looking to use NLP models within their institutions need to establish an ethical framework that considers those concerns within their realm of agency and control. One step toward transparency is attribution: how and when do we attribute work to generative AI? Looking up a topic up on the internet, getting ideas, and writing about it is very different from copying sections from an article without attribution. Similarly, direct copying from content produced by generative AI tools requires disclosure, but using that content for ideation does not. Consider also the need for accountability. Blindly trusting in generative AI output is unwise and often unethical. AI output cannot be cited because it is not referenceable. Validating AI output by consulting reliable sources to arrive at a sound conclusion is surely reasonable. Ethical Guidelines on the Use of Artificial Intelligence (AI) and Data in Teaching and Learning for Educators (2022), published by the European Commission, is an excellent guide for the thoughtful use of AI in education.

Since I started this reading list with a video, how could I pass up the opportunity to end the same way? In the hour-long video "The A.I. Dilemma" (March 9, 2023), Tristan Harris and Aza Raskin, two cofounders of the Center for Humane Technology, excellently summarize the dangers currently posed by the AI field generally. They discuss what the future adoption of these tools may look like based on what we know now about the current capabilities of AI technology.

In Summary

The history of AI is entwined with the history of computing. While the importance of the internet is now widely accepted, the internet is a new technology by comparison with AI. The societal relevance of the internet languished for more than two decades after its creation, yet the incubation period for AI has been even longer.

AI is no longer a specialized or niche field of research with limited applicability. In the past decade, AI has been delivered, in one form or another, to consumers without the AI label (e.g., social media platforms, voice assistants, recommendation systems, photo editing). ChatGPT is an announcement to the world that the field of deep learning-that is, highly capable neural networks-is now immediately accessible to all. Perhaps more importantly, ChatGPT is a demonstration of the effectiveness of one type of AI tool that will encourage all of us to invest in and explore this expansive landscape of possibilities.

As many of us in IT organizations embrace AI tools in the coming years, we need to recognize the consequences of our actions. In time, AI will fundamentally challenge the way we understand reality. AI is a novel collection of technologies without historical parallel. Just as AI will not solve all problems, there is not one simple and ethical way to navigate AI. Instead, a rigorous analysis of ethics and metaphysics and a careful consideration of how IT organizations deploy AI will be required. IT organizations need to build on the ethical framework of equity and inclusion to ensure that this empowering technology experience of generative AI is transparent and accountable.

Notes

1. For the purposes of this article, neural networks throughout will refer to artificial neural networks.

2. The seminal 2017 paper "Attention Is All You Need" by Ashish Vaswani et al. gives an introduction to this subject. Despite the catchy title, this paper is quite technical and requires significant working knowledge of deep learning. Note that Google pioneered this Transformer technology and made it open source, along with other key AI technologies.

3. A. Ivakhnenko and V. G. Lapa's book Cybernetic Predicting Devices (Kiev, 1965) was developed in the Soviet Union and was the genesis of deep learning. For the first time, a mathematical foundation was established for how neural networks could successfully operate with multilayer perceptrons. While this is a technical work, in Chapter 4 the authors recognize the importance of Frank Rosenblatt's 1958 work with implementing perceptrons, and they provide a great contemporary understanding of the model. Following up on this work was Ivakhnenko's article "Heuristic Self-Organization in Problems of Engineering Cybernetics," Automatica 6 (1970), in which he introduced an algorithm to automate neural network construction and utilized the theoretical groundwork he had previously established with multilayer perceptrons.

4. A wonderful resource for more information on other types of networks is the blog series on deep learning written by Tim Dettmers, a staff member at NVIDIA, in 2015-2016. The first blog post focuses on visual AI (e.g., how convolutional artificial neural networks function). The second blog post provides a history of deep learning and discusses some of the math developments. The third blog post covers some of the fundamental building blocks of NLP models. The fourth blog post discusses different network training methods of the time.

5. This high-level summary of their work is quite accessible to nonexperts. For the technical version, see Radford et al., "Improving Language Understanding by Generative Pre-Training" [2018].

6. For the technical version, see Radford et al., "Language Models Are Unsupervised Multitask Learners" [2019]. An interesting way to look at this paper is to search for the term surprising, as noted by Benj Edwards in "Why ChatGPT and Bing Chat Are So Good at Making Things Up," Ars Technica, April 6, 2023.

7. The March 2022 initial release of 3.5 received similarly modest treatment by way of feature announcement.

8. Ethical philosophy is absent in many publications on AI ethics, and this often results in shallow ethical discussions. Furthermore, since 2021 several ethicists with robust philosophy backgrounds have been fired by both Google and Microsoft. See Daisuke Wakabayashi and Cade Metz, "Another Firing among Google's A.I. Brain Trust, and More Discord," New York Times, May 2, 2022; Zoe Schiffer and Casey Newton, "Microsoft Lays Off Team That Taught Employees How to Make AI Tools Responsibly," The Verge, March 13, 2023.

9. Georg Wilhelm Friedrich Hegel, Elements of the Philosophy of Right (1820). Hegel's thoughts on imputability are also relevant. Knowledge of what may occur as a result of one's actions is an important discourse when considering emergent behaviors in AI.

10. For a recent historical perspective, see Sofiya Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (2018), a groundbreaking book based on years of research about Google's search algorithms and the pervasive bias within them.

11. Oddly, for some famous confabulations that have occurred to date, companies disclaimed responsibility. This is a dubious ethical position because the only substantive function of NLPs is their output. This is like saying the company is responsible only when there is no problem! Since the summer of 2023, all major models now feature disclaimers about this issue.

12. Interestingly, ChatGPT works within an explicit knowledge cutoff date of September 2021. This is partly a practical matter, since pretraining has to begin and end at some point in time. It is also, however, intentional: OpenAI researchers understood that these models would soon be used to populate the internet with generated content and that training a neural network on content generated by other neural networks leads to model collapse. Similarly, if we end up "validating" the results of ChatGPT through web searches that are driven by other NLP models and lead to AI-generated content, does that really count as validation?

Brian Basgen is CIO at Emerson College.

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generativ

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Introduction

Generative Artificial Intelligence (hereafter referred to as GenAI) in broad terms refers to a branch of computer science that utilizes machine learning and other advanced algorithms to generate new content de novo. This content includes text, images, code, audio, and video, and is typically created from prompts or other user inputs.

A point of interest is that the output produced by GenAI systems, such as ChatGPT, is often indistinguishable from that of a human. This development has created a need for establishing guidelines in the university setting that are concrete enough to provide immediate direction on best practices - including syllabus development - but can remain malleable as we continue to research this topic and gain a better understanding of its role in university pedagogy.

Based on results from a recent survey by the Applied AI Institute at Concordia, in addition to current literature on the topic, we are aware that there are diverse teaching approaches and learning outcomes across university campuses that necessitate tailored, context-specific guidance for GenAI and not blanket, university-wide policies. For this reason, the Centre for Teaching and Learning (CTL), in consultation with faculty, has generated guidelines while further consideration is being given to university policy.

Given the diversity of pedagogical approaches on our campus and the unreliable nature of GenAI detection tools, Concordia's optimal strategy is to assist instructors in understanding the quickly evolving nature of GenAI in order that they may make informed decisions about its use in their classes. Above all else, it is essential that professors dedicate time to articulating their positions on GenAI clearly within the classroom, on Moodle sites, and in syllabi.

Syllabus statements

These guidelines provide sample syllabus statements on GenAI use for professors to adapt. Customizing the samples can promote responsible use of GenAI by students based on specific course learning outcomes and the demands of specific assignments or activities. Transparency on expectations and limitations is key to leveraging GenAI tools successfully. Clear syllabus statements are crucial for setting consistent standards, mitigating misuse, and fostering the ethical adoption of GenAI.

General Statements

If one is allowing this type of use, here are some ideas for opening general statements about the use of AI tools ( adopted from the University of Toronto):

Students are encouraged to make use of technology, including generative artificial intelligence tools, to contribute to their understanding of course materials, under the circumstances outlined below.

To achieve favorable results with generative AI, it is essential to invest time in building knowledge in the target subject and refining prompts, as this enables students to produce more accurate output while validating its accuracy and relevance to the topic at hand.

Misuse

You may also consider adding statements about misuse, such as the following:

Material drawn from ChatGPT or other AI tools must be acknowledged; representing as one's own an idea, or expression of an idea, that was AI-generated will be considered an academic offense.

Only some uses of ChatGPT or other AI tools are permitted. Prohibited uses and/or not sufficiently acknowledging use will be deemed misconduct under Concordia's Academic Code of Conduct. Students who engage in these behaviours may be charged under Articles 18 (general cheating/plagiarism/dishonest behavior) and 19a (plagiarism) of the Code.

In this class, writing assignments and submitting outputs that contain incorrect information related to class concepts, inappropriate responses to assignment prompts, or details that you are unable to explain or discuss in detail is considered a misuse of GenAI.

Constraints

Clearly identify constraints in statements, such as the approach taken by the University of Toronto syllabus language document (adapted, and expanded):

Students may use artificial intelligence tools for generating ideas, creating an outline for an assignment, or polishing language, but the final submitted assignment must be the student's own work.

Students may not use artificial intelligence tools for completing exams, writing research papers, or completing other course assignments, including creative assignments, posts to discussion forums, or smaller writing assignments. However, these tools may be useful when gathering information from across sources, assimilating it for understanding, improving writing, and even in the early stages of developing what they will produce for the assignment.

Students may not use artificial intelligence tools for major assignments in this course, but students may use generative AI tools for smaller assignments.

Students may use the following, and only these, generative artificial intelligence tools in completing their assignments for this course: .... No other generative AI technologies are allowed to be used for assessments in this course. If you have any questions about the use of AI applications for course work, please speak with the instructor.

Acknowledging GenAI

If GenAI use is allowed, students must be instructed on proper acknowledgement. These statements are helpful additional statements from the University of Toronto:

Students must submit, as an appendix with their assignments, any content produced by an artificial intelligence tool, and the prompt used to generate the content.

Students may choose to use generative AI tools as they work through the assignments in this course; this use must be documented in an appendix for each assignment. The documentation should include what tool(s) were used, how they were used, and how the results from the AI were incorporated into the submitted work.

Any content produced by an artificial intelligence tool must be cited appropriately. The MLA and APA are now providing information on citing generative AI.

Generative AI may be used to draft some of the writing/phrasing, but cannot simply be cut-and-pasted, and can constitute no more than 25% of the text. You must also specify what work is your own and what comes from AI (e.g., by color-coding AI produced text or ideas).

Acknowledge how GenAI inputs were created by documenting their workflow as well as prompts used.

Prohibiting GenAI Use

While some instructors may opt to prohibit GenAI entirely, this approach can be difficult to enforce in practice. As an alternative, we recommend focusing policies on setting clear thresholds and expectations around responsible use. However, for instructors who still find a need to prohibit GenAI use (e.g., for placement exams), here are sample syllabus statements:

The use of generative AI tools is prohibited for all assignments in this course/exam. Their use in this course will constitute a violation of the Academic Code of Conduct.

The use of AI tools like ChatGPT is prohibited for all items on this placement course/exam.

Prohibited uses of ChatGPT or other AI tools will be deemed misconduct under Concordia's Academic Code of Conduct. Students who engage in these behaviours may be charged under Articles 18 (general cheating/plagiarism/dishonest behavior) and 19a (plagiarism) of the Code.

Restrictive policies are very challenging to monitor and enforce effectively. They also limit opportunities to actively engage students in learning about responsible and ethical AI use. We also strongly discourage the use of GenAI detectors, given their unreliability. We instead encourage focusing on transparent syllabi statements that empower students to harness these technologies as learning aids while upholding academic integrity.

Detection

One of the biggest concerns of faculty continues to be the misuse of GenAI, whereby students use the tool to do their work, and consequently do not engage in the required learning. One response may be to inquire about the possibility of AI detectors, but it should be noted that online detectors, like GPTZero are known to be unreliable - commonly producing both false positive and false negative results. There are additional privacy concerns surrounding the use of such detectors, making them an overall unsatisfactory response.

It should be noted that to date, code violations using evidence drawn from detection tools have been challenged. While it is up to each individual professor to determine if they want to use detectors, we do not recommend their use at this time.

Misuse checklist

Identifying and evaluating potential misuse of GenAI requires a balanced approach. To this end, the checklist in table 1 aims to provide instructors with a three-level scoring system for detecting potential misuse. The levels range from potential evidence to strong evidence, with each factor weighted by point values.

While not definitive and not to be used as evidence for misuse, this checklist can aid in identifying assignments that merit further investigation, or discussion with students. It is recommended that instructors review this list with students at the beginning of the term so students can better understand boundaries for GenAI use in their coursework.

Exemplary use checklist

In addition to mitigating misuse, it is essential to define and encourage exemplary use of GenAI. Accordingly, the checklist in table 2 ranges from developing skills to mastery, with each factor weighted by point values. This checklist can aid in identifying student work that demonstrates proficient to advanced integration of AI as a resource.

It is similarly recommended that instructors review this rubric with students at the beginning of the term so that students understand expectations for GenAI use in their coursework. The rubric provides a balanced method for recognizing students who use GenAI to enhance their learning and original thought.

Best practices

While integrating AI tools into the classroom presents opportunities, it requires thoughtful guidance to uphold ethics and academic integrity. These best practices provide concrete recommendations across key areas including faculty support, student training, and promoting ethical AI use. The best practices aim to foster responsible AI adoption that enhances pedagogy without compromising rigor.

Faculty support

Provide comprehensive support for faculty members about the integration of GenAI tools in their courses. Address the benefits, challenges, and ethical considerations associated with AI use. Offer workshops, resources, and articles to help instructors make informed decisions on how to effectively incorporate AI while maintaining academic integrity.

Student support

Faculty need to equip students with the knowledge and skills needed to responsibly engage with GenAI tools. Develop modules that emphasize the ethical use of AI, including proper citation of AI-generated content. Incorporate interactive sessions where students can practice using AI tools effectively, acknowledging their usage, engaging in best practices, and avoiding potential pitfalls related to academic misconduct.

Promote ethical GenAI use

Foster discussions on ethical AI use within the student community. Engage them in conversations about potential biases in AI tools, the importance of acknowledging AI assistance, and the implications of misusing AI-generated content. Encourage students to reflect on the ethical considerations of relying on AI and making responsible choices.

Promote reflective practices

Incorporate a preliminary summary of intended AI tool use before assignments and a reflective piece afterward. Encourage students to assess the role and impact of AI tools on their learning process and assignment outcomes. This time also allows students to have discussions about prompts and other productive uses of GenAI in their work. This reflective practice fosters a deeper understanding of AI's influence on their educational journey.

Next steps

As our understanding of GenAI develops along with new tools and techniques, we recognize the need to continuously reassess best practices for responsible classroom integration. These guidelines will therefore be reviewed at least once per academic term to ensure they remain current amidst this rapidly evolving situation. We aim to provide guidance that balances GenAI's potential and risks in order to enhance pedagogy while promoting ethical use and academic integrity at Concordia.

© Concordia University

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Generative AI teaching guidelines - Concordia University

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Guidelines for teaching with generative artificial intelligence. The Centre for Teaching and Learning (CTL), in consultation with Concordia faculty, has generated guidelines while further consideration is being given to university policy.

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With the emergence of ChatGPT and other AI tools, many members of our community are eager to explore their use in the university context. This advisory provides guidance on how to use these tools safely, without putting institutional, personal, or proprietary information at risk. Additional guidance may be forthcoming as circumstances evolve.

Allowable Use:

Prohibited Use:

At present, any use of ChatGPT should be with the assumption that no personal, confidential, proprietary, or otherwise sensitive information may be used with it. In general, student records subject to FERPA , and any other information classified as Protection Level P2, P3, or P4 should not be used.

Similarly, ChatGPT should not be used to generate output that would be considered non-public. Examples include, but are not limited to, proprietary or unpublished research; legal analysis or advice; recruitment, personnel or disciplinary decision making; completion of academic work in a manner not allowed by the instructor; creation of non-public instructional materials; and grading.

Please also note that OpenAI explicitly forbids the use of ChatGPT and their other products for certain categories of activity, including fraud and illegal activities. This list of items can be found in their usage policy document

Additional Guidance:

For further guidance on the use of ChatGPT for teaching and learning, please see Understanding AI Writing Tools and their Uses for Teaching and Learning at UC Berkeley from Research, Teaching & Learning.

Rationale for the Above Guidance:

UPDATE 7/1/2023: The University of California recently renegotiated the UC systemwide agreement with Microsoft to include Microsoft Azure OpenAI. UC Berkeley is currently working with Microsoft to establish how to use this service under UC's agreement. [Note: For questions regarding the approved use of Microsoft's Azure's Open AI service, please consult with the Privacy Office at privacyoffice@berkeley.edu ]

As of September 2023, the University of California's agreements with the parent companies of ChatGPT and other generative AI services, which include our terms and conditions, Appendix Data Security and other privacy protections, do not cover the use of ChatGPT or other similar generative AI services. The UC Office of the President is working on this issue. We hope to see this addressed in the near future and will update this guidance when additional information is available.

Personal Liability: ChatGPT uses a click-through agreement. Click-through agreements, including OpenAI and ChatGPT terms of use, are contracts. Individuals who accept click-through agreements without delegated signature authority may face personal consequences, including responsibility for compliance with terms and conditions. [1]

Guidance on Appropriate Use

For questions regarding the appropriate use of ChatGPT and other AI tools, please contact privacyoffice@berkeley.edu

References

Additional Readings

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Appropriate Use of ChatGPT and Similar AI Tools

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With the emergence of ChatGPT and other AI tools, many members of our community are eager to explore their use in the university context. This advisory provides guidance on how to use these tools safely, without putting institutional, personal, or proprietary information at risk. Additional guidance may be forthcoming as circumstances evolve.

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Generative AI-also known as GenAI or GAI -is a type of artificial intelligence model or tool which, given a prompt or query, is capable of generating new and creative outputs in a variety of media, including text, images, video, audio, code, or other formats. GenAI tools do this by collecting millions of representative samples from online data and "training" the system by specifying how the tool should respond when prompted.

GenAI can be used to create outputs that mimic human creation with varying degrees of success. ChatGPT is currently the most well-known GenAI application and is a sophisticated chatbot that has been trained on an enormous collection of text data to develop an understanding of the patterns and structures of human language.

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Generative AI-also known as GenAI or GAI -is a type of artificial intelligence model or tool which, given a prompt or query, is capable of generating new and creative outputs in a variety of media, including text, images, video, audio, code, or other formats.

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effective policies can pay significant dividends.

Credit: vinnstock / Shutterstock.com © 2023

Higher education focuses on building knowledge and investing in the written word, whether it's scholarly research or demonstrations of learning such as essays, research papers, theses, or dissertations. The underlying assumption is that such work could not be significantly faked aside from paying another individual (such as student paper mills) or taking the work of others without attribution. Generative AI upends much of that baseline and in fact will challenge many institutions' academic integrity policies because they often prohibit copying from websites or acquiring papers from other individuals. Generative AI will also introduce other ethical and procedures considerations throughout the institution.

This piece evolved from our institutional roles (instructional design, writing support, and research and library support), our individual work at our respective institutions, and a half-day workshop on generative AI policy development that we facilitated for the NERCOMP 2023 Annual Conference. The workshop included a student panel, followed by focused conversations around several topics related to generative AI: ethics and plagiarism, bias, instructional technology, opportunities, and threats. For the final part of the workshop, we facilitated a mini design sprint for policy development.

The Need for Policy

One of the most pressing examples of areas for guidance is the discourse around plagiarism and how to detect it in an era of AI-generated content. Institutions are under pressure (whether organic or manufactured) to respond in some manner to the rise of generative AI as a widely available tool. The first wave of awareness seemed to be generated around plagiarism concerns and worker replacement theories ("Students will never write original papers again" and "Robots are replacing everyone"). For those in higher education, the drumbeat of fearmongering about plagiarism has succeeded in capturing faculty and administrative attention.

Early on, some assumed that generative AI work submitted by students could be identified with generative AI plagiarism checkers. TurnItIn's documentation states, "Our AI Writing detector's false positive rate is less than 1% for documents with 20% or more AI writing (our tests showed that in cases where we detect less than 20% of AI writing in a document, there is a higher incidence of false positives)." However, after the TurnItIn AI Detector was used in practice, across 38 million papers, false negative and positive detections are now at a higher risk. TurnItIn and other companies claim to offer greater odds of correct detection than reality seems to bear out. Additionally, further research is showing biases in what AI-plagiarism detection tools flag as being AI generated, including an increased likelihood of inaccurately flagging multi-language learners' work as having been generated by AI. Beyond the problem of false accusations, this environment also creates an untenable situation for students who must somehow defend themselves against a machine that cannot show its work but is just a projection. Additionally, students are using tools such as QuillBot, which will paraphrase text and use synonyms, to circumvent AI detection tools.

As original student writing becomes increasingly difficult to verify and confirm, developing a policy for AI detection might result in its being outdated just as the policy is implemented. Having an agile committee representative of diverse campus needs who can both review these issues and considerations to craft a more useful policy is part of what is needed to both protect students and choose the right tools for navigating the technological changes.

This is but one issue that higher education needs to navigate when crafting policy around generative AI. Other issues include but are not limited to the following:

The role of generative AI in visual and textual outputs of the institution, be they for marketing, for social media, or in reports

How and where faculty can use generative AI in the creation of course content, assignments, and feedback or assessments

Addressing the embedded biases of the data and outputs

The impact and challenge of information literacy

The environmental impact of generative AI, including greenhouse gasses and water usage

The impact on workers needed to run generative AI

Starting with the End

In many areas of life, including instructional design, an ideal place to start is with the results-what you want the outcome to be or do. To feel that your-and your collaborators'-time has not been wasted, and to see that you have made progress toward what might still be an abstract version of achievement, codify what success looks like for you, your collaborators, your stakeholders, and the final institutional arbiters. As AI continues to develop at a rapid pace, it is important to pause and do enough research so that you can formulate the questions you ultimately seek to answer. Below are some questions to get you started in thinking about this policy:

Whom is the policy going to focus on? Students, faculty, staff, administration, third-party vendors, contractors, etc.?

Can the same policy apply across the institution, or will different policies be needed for different parts of the organization?

Will the policy stand on its own, or will there be room for adjustments (for example, will students encounter variations depending on whether instructors-under the notion of academic freedom-want to encourage or discourage certain uses of generative AI for the purposes of teaching)?

What can or will be the implications of violating the policy?

What methods of accountability with the policy can be created when it may be hard to actually detect generative AI text?

Will there be differences between institutionally affiliated generative tools and those that are available to anyone?

Perhaps you are still in the fact-finding and idea-generating stage-you might need to specifically dedicate time to consider what will constitute your goal for the mission. You may decide that success for your group means developing and writing down these measurable goals. That is, you might still be in the phase of brainstorming and research, and you might need to take time to decide what your goals are. Ultimately, all involved should agree on specific measurable outcomes that are necessary for your group to have completed your work.

It is natural to feel a sense of urgency to take action, and it might be tempting to rush through this step. Although this process will not necessarily be easy, it will be helpful in carving out exactly what you are going to do in this phase. One of the trickiest parts about generative AI is that it has continued to change and shift in the past 12 months and will likely continue to do so, especially as other forms of generative AI (image, audio, video, slides, etc.) become increasingly available. Therefore, no matter what your goals are, creating a mechanism for revisiting, adjusting, updating will be equally important to name and anticipate early on. In that case, think in terms of not getting it perfect the first time through but having a process to iterate.

Identifying Stakeholders

Generative AI has the possibility of hitting every part of the institution. Because it is difficult to imagine an area that generative AI won't touch, it's useful to think about all the institutional stakeholders who will need guidance around usage of these new tools. Start by considering all the user groups and their iterations at your institution. One approach would be to conduct an audit of your campus; if your institution has an organization chart, that is a great document to use as the foundation. Some people and groups are likely not represented, so detective work will come into play. This is a great opportunity to break down institutional silos and examine your own invisible biases about the roles others play on our campuses.

Identify non-classroom student- and faculty-facing roles, which could include instructional designers, writing specialists, librarians, and academic support services. Then move on to people whose work has an impact on the campus experience as a whole. This could include staff working in IT (e.g., information security, academic computing, or web services), institutional communications, student life, even alumni relations. Be mindful that just because a group is not readily visible to you does not mean that they will not be impacted by or have an impact on your policies. We are a good example of coming together to collaborate on AI policy. The diversity of our professional perspectives has lent itself to not only seeing different viewpoints but also creating the opportunity to truly understand the nuances in just how many ways AI is impacting our institutions.

Consider, too, your contingent communities, such as part-time educators (adjuncts) and part-time staff. How are they included in the community in general? Do they experience regular communication patterns as the full-time employees do, such as being included in campus-wide emails? Will you need to set aside time to engage them in discussion separately, and will they need more training and professional development as you enact the policies you create? Look for allies who can serve as conduits to get people involved. For instance, adjuncts are often on campus for limited amounts of time during the day or might work elsewhere during typical business hours. When you are scheduling conversations, be mindful that you will need to create opportunities for those who work nontraditional schedules to be involved.

Some questions to consider for other entities at your institution may include the following:

Will the institution's upper management consider using generative AI to surveil employees' work to detect efficiency or generate employee evaluations?

Should your policymaking efforts account for this scale of institutional use?

How will AI be addressed in human resources, especially the recruiting and hiring process?

In what ways will the community outside the campus be impacted by your policies?

Do you have community partnerships in which you offer students an opportunity to put their theoretical learning into practice? Do you have an obligation to educate your students on the uses of generative AI as part of that collaboration?

Will your institution be viewed as being a policy and position leader on the subject of generative AI?

What will be reasonable and equitable means of challenging outputs by generative AI?

Finally, one of the most visible and yet frequently overlooked communities is the students. The value of engaging students in determining this policy cannot be overstated. To craft a policy without their input could result in a policy that feels out of touch and irrelevant to them. At the NERCOMP workshop, the highlight was hearing at the student panel how they demonstrated their own deep and sophisticated thoughts about generative AI and its roles in their lives. In particular, the conversation should extend beyond plagiarism and also include student perspectives about how other institutional areas should be using it (e.g., faculty, marketing, communications). Many students are aware of and interested in how generative AI is currently being employed in a wide variety of industries. Students might be reading about the way Paul McCartney used generative AI to restore John Lennon's voice to create a "new" Beatles track, or they may be interested in how generative AI is being used to dramatically improve recycling programs. These interests might be general curiosity (to be encouraged!), but they might also tie into students' future employment goals.

The consequences of not including a diverse array of groups in these conversations far outweigh any inconvenience you may experience when trying to find times and ways to gather input throughout the policy development process. Our workshop included people from many of the groups mentioned, and a key takeaway was just how much we learned from each other. Many realized that while something may seem obviously important to you, it may not even be considered by someone working in a different position.

Models for Developing Policies

Depending how an institution is structured and how much its leaders want to include different groupings within the organization, any of several models can be deployed to develop policies around generative AI. The following options are useful approaches and, to some degree, can be mixed and matched to meet the institution's needs and structures.

Task Force Model

Put out a call to action to form an inclusive team representing all aspects of the institution to create a policy task force. Alternatively, it could be a smaller, nimbler team that works to engage with different stakeholders across the institution, creating a template policy for generative AI and adjusting and tweaking it for each area of the institution, in conversation and collaboration with relevant stakeholders.

For instance, the policy for students and faculty is likely to look different from that of the marketing department, but realistically all of these groups should have AI policies. This might seem self-evident for students, but there are important questions to consider even for marketing, such as the use of generative AI image tools to represent students. Would that adhere to the institution's ethos or marketing ethics? Those are the kind of questions that would be relevant to marketing that students and faculty may not need to be involved in.

Governance Model

Some institutions have governance models in which faculty and staff play pivotal roles in the development and creation of institutional policy, particularly around policies that directly impact the classroom and students. This can be a useful model to gather a range of voices throughout the organization and have a clear pathway and record (e.g., committee notes) of how decisions are made and implemented. Yet these processes may encounter challenges in moving at an effective pace to make decisions that are timely and responsive to a changing environment. Due to the rate of change in generative AI, committees might create policies for assumptions around generative AI that are no longer valid.

Design Sprint Model

1. Understand: The first step is to understand how generative AI is currently being used (or isn't) at your institution, the concerns of the community, and the ideal state for AI use. This can be achieved through a listening tour, lighting talks, surveys, or structured conversations.

2. Define: Next, the policy team should review all the information gathered in the first step to define their main goals and desired outcomes that reflect the needs of the community.

3. Sketch: Each individual on the policy committee should sketch out their own draft policy that meets the definitions outlined previously. The larger committee can then review all policy drafts and then narrow down the ideas of each draft into a finalized Solution Sketch.

4. Decide: The policy committee will then review all Solution Sketches to decide which version they want to use to move forward with the process.

5. Prototype: The prototype can be considered the first draft of the policy to be shared with the community.

6. Validate: Return to the community members from the first phase of the design sprint process, gather feedback based on your policy draft, and make relevant edits. This is the last stage of the design sprint process, which will involve crafting the final version of the policy ready to be shared with your institution.

Consultant Model

Exemplar Model

Another option is to follow the lead of institutions that are publishing their own policies. For instance, Lance Eaton and his students have proposed and posted policies for College Unbound. Starting with one of these policies, the institution can then review, share for input, and adapt as needed. This can save a lot of time in terms of coming up with the initial approach, but it will need to be tailored to the specific needs of the institution and its population. If the policy creation team is reluctant to borrow from another institution, they could use a generative AI tool to draft the initial policy and workshop it across the institution. We used ChatGPT to generate several examples of policies to help get you started crafting your own.

Recommendations for Gathering Voices

To create the clearest and most effective policy, all stakeholders' voices must be heard. Because recruiting volunteers for their insights in drafting a policy can sound tedious, consider using both formal and informal routes to get their assistance. Also important is conveying how each stakeholder may be impacted by such a policy and emphasizing that their experiences and observations are highly valuable to the process. Asking for help drafting a policy document can be overwhelming to many. These approaches might make this more engaging:

Connect with people in mini roundtable discussions or small-group conversations

Find help through social media-use polling features to generate feedback and locate allies

Have one-on-one conversations with the people you see as your greatest supporters and greatest challengers in this process. Diverse perspectives are necessary to have a policy that works for everyone.

Allow for enough time in discussions. It can be easy to rush through things, but these discussions will include deep topics and issues that need time to be fully processed.

Provide collaborative documents for participants to add text, and use the comment feature for questions or additional thoughts.

Institutional knowledge is both an advantage and a disadvantage in this process. Some stakeholders will be obvious, and some people will quickly volunteer. Others may be more challenging to reach because of their disinterest or because they don't see the relevance to their work. These are important people to bring into the conversation, rather than making assumptions about their thoughts or possible contributions.

Framework for Generative AI Policy Creation

Below are the relevant sections that a generative AI policy should address to help institutions in their policy development processes. Each institution should have its own individual policy based on the needs of its community.

Policy Audience: Whom is this policy for? Is it for the entire institution, faculty, students, staff, departments, third-party vendors, or others?

Policy Timeline: What is the timeline for implementation? Should that timeline include a review and update cycle after initial implementation?

Policy Tools: What counts as AI for this policy? Is it focused on all AI or only generative AI? Is it focused on all generative AI or just text-generating AI?

Academic Integrity Guidelines: If the policy is related to student or faculty work, what are the integrity requirements to make sure academic integrity is upheld?

Acceptable Use: If AI usage is acceptable, are there any limitations on the amount of usage (e.g., a certain percentage must be individually generated in certain contexts) or purposes for which generative AI may be used?

Transparency: What practices are in place for communicating its usage throughout the institution?

Security and Legal Considerations: What concerns need to be addressed concerning privacy, intellectual property, and proprietary knowledge around using external or enterprise generative AI tools? Does endorsing the use of generative AI conflict with any laws such as the General Data Protection Regulation?

Ethical Considerations: What concerns or responsibilities does the institution have explicitly or implicitly within its mission that conflict with the environmental, human-exploitation, and bias issues related to generative AI?

Institutional Resources: Which areas of the institution will be committed to supporting, responding to, and implementing uses of generative AI?

Processes for Policy Violation: If the policy is violated, what are the steps for identifying and addressing it?

Conclusion

Institutional policy development can often be a methodical process that both informs and better unifies an institution's approach to a particular challenge. And yes, it can be tedious. Yet many institutions are often unprepared for technological developments, and the delays in catching up have a deep impact on students, faculty, and staff (e.g., the reactive response to the pandemic). Although the generative AI cat is out of the bag, there's still an opportunity to meaningfully guide-through a collaborative effort-how it can be best used by all the stakeholders. Such an approach will result in a better learning and working environment for students, faculty, and staff. Taking an iterative approach to AI policy might feel unnatural for those who operate best with exhaustive policy documents, but it just might be the key to successfully navigating this new technological reality.

Notes

1. Daniel E. O'Leary, "Gartner's Hype Cycle and Information System Research Issues," International Journal of Accounting Information Systems 9, no. 4 (December 1, 2008): 240-52; Jackie Wiles, "What's New in Artificial Intelligence from the 2022 Gartner Hype Cycle," Gartner, September 15, 2022.

2. Sam Levine, "Vanderbilt Apologizes for Using ChatGPT in Email on Michigan Shooting," The Guardian, February 22, 2023; Josh Moody, "The ChatGPT Commencement Address," Inside Higher Ed, June 29, 2023; Lauren Coffey, "Harvard Taps AI to Help Teach Computer Science Course," Inside Higher Ed, June 30, 2023.

Esther Brandon is Manager of Learning Design & Technology Adoption at Harvard Medical School.

Lance Eaton is Director of Faculty Development & Innovation at College Unbound.

Dana Gavin is Director of the Writing Center at Dutchess Community College.

Allison Papini is Assistant Director/Manager of Research & Instruction Services at Bryant University.

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cross-campus approaches to building a generative ai policy

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Cross-Campus Approaches to Building a Generative AI Policy

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effect

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September 26, 2023

Dear Students,

As you settle into the fall semester, we want to share important reminders and guidance about the use of generative Artificial Intelligence (AI) in your coursework and research. The information below was put together by experts across campus to help you as you assess the use of this technology in your academic studies and research.

Generative AI Reminders and Guidance

Ask before you use AI and check your syllabus and assignment guidelines. Many instructors are providing specific guidance about appropriate use of generative AI in their courses. If you do not know whether your use of generative AI is appropriate to the outcomes in your course or research activities, tell your instructor or supervisor what you are considering and ask for guidance. Do this well in advance of your deadlines, not after you have submitted your work.

Remember that academic and research integrity are a core part of your experience as a student. Disclosing your research methods, using proper citations when incorporating other's work, and contributing original intellectual work as an author are central tenets for ethical scholarly work. As an author, you take full responsibility for your writing and the evidence supporting it, including attribution of sources such as generative AI. It should be used as a resource to facilitate your learning, not a replacement for your education.

Just like personal items, keep your data safe - and beware of scams. Do not provide personal information about yourself or others that is not public, and always follow MSU's data guidance. If you use AI, be sure to use reputable, recognized sources, and never share your credentials with a third party, no matter how legitimate they may seem.
Know the potential risks and limitations of AI outputs. Results might include misinformation, inaccuracies, bias, or inappropriate or unintentionally harmful content.

Throughout the year, we will be engaging in thoughtful conversations with the MSU community and experts about the ethical use of these new technologies in ways that align with the university's mission and values. We will continue to share additional guidance and reminders as those conversations progress. We look forward to learning and using these new technologies in scholarly, equitable, and ethical ways.

Sincerely,

Thomas D. Jeitschko, Ph.D. (he/him)
Interim Provost and Executive Vice President for Academic Affairs

Mark A. Largent, Ph.D. (he/him)
Vice Provost and Dean of Undergraduate Education

Pero G. Dagbovie, Ph.D. (he/him)
Vice Provost for Graduate and Postdoctoral Studies
and Dean of the Graduate School

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Generative Artificial Intelligence (AI) Reminders and Guidance for Students | Michigan State University

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Skip navigation links September 26, 2023 Dear Students, As you settle into the fall semester, we want to share important reminders and guidance about the use of generative Artificial Intelligence (AI) in your coursework and research. The information below was put together by experts across campus to help you as you assess the use of this technology in your academic studies and research.

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Because artificial intelligence will likely become the primary way humans access information, professors must prepare students to use the technology effectively in their lives and careers.

Students will especially need to learn skills related to effective prompt engineering, which refers to the ability to craft questions that elicit the most useful answers from AI platforms.

The more comfortable that faculty become with using AI, the better they will be at teaching students how to use this skill ethically and effectively in the years to come.

While ChatGPT has only recently captured public attention, artificial intelligence (AI) is not new. Some form of the technology has been around since the mid-20th century. However, the fact that AI is now being used for more applications and is more accessible to the general public has changed the conversation, as we realize that the technology will have a significant impact on 80 percent of jobs -including teaching.

Indeed, in the very near future, AI models will likely be the primary way we access knowledge-and higher education institutions will be where many people learn to use the technology effectively. This new reality makes it clear that it's time for educators to master a new skill set relevant to AI and to teach those skills to their students.

Model the Technology

Although many learners are experimenting with AI models such as ChatGPT, the first place most learners should learn how to use AI effectively is in the classroom. But this will happen only if schools have introduced relevant learning activities into their curricula and have linked those activities to appropriate learning objectives.

However, because many faculty are not comfortable using AI, they will need to learn how to integrate the technology into their classrooms. Schools can start by showing students and faculty how AI can be viewed as an ally, rather than an enemy. To convey this message, schools can take several steps to expose students and staff to the technology:

Create seminars to train faculty in how to introduce use cases of AI and how to emphasize the benefits and limitations of this technology.

Use learning experience platforms (LXPs) that leverage adaptive learning and AI processes that allow faculty to detect when students are having difficulty with the material and to customize their courses. Faculty can use feedback from an LXP to help students gain a deeper understanding of their weaknesses and enhance academic outcomes.

Use robotic process automation (RPA) systems to automate repetitive tasks, such as the students' submission of assignments to correctors. Such automation frees up faculty's time so they can concentrate on their primary missions.

Delegate to AI other routine administrative tasks, such as accepting admissions forms and generating emails and automatic replies, allowing staff more time to offer individualized support to learners.

Perfect the Art of 'Prompt Engineering'

Once faculty are comfortable with the technology, the most critical competency they will need to teach is the art of prompt engineering -the skill of formulating questions in ways that elicit the most useful answers. Effective prompt engineering encompasses the following areas where both faculty and their students will need to refine their knowledge and abilities:

Large language models (LLMs). Understanding LLMs-the algorithms that lie behind the output of generative AI-is a vital component to harnessing the potential of and tailoring interactions with ChatGPT. Only through meticulous and unambiguous prompt construction can users elicit desired responses from the language model.

That makes it critical that faculty incorporate the use of platforms such as ChatGPT into their classrooms. Then, they can teach students how to steer ChatGPT's output toward their intended objectives. For students, the goal is to interact with ChatGPT as if they were engaging in a conversation with a human. To do this, they must first understand how LLMs work.

Communication. Students might view effective communication as less important when they are inputting a prompt into ChatGPT than when they are interacting with their professors or peers. But providing clear and explicit instructions in an AI prompt is just as essential as providing clear instructions to colleagues-good communication helps users streamline their interactions with AI.

Faculty can help students learn how to refine their questions to achieve increasingly precise responses (known in research as an iterative approach). They will learn to establish context, define the task, and specify the desired outcome for the language model.

For students, the goal is to interact with ChatGPT as if they were engaging in a conversation with a human.

The better students are at communicating with AI, the more likely it is that they will have seamless and rewarding learning experiences as they use AI to deepen their understanding of complex concepts, find solutions to problems, or explore new areas of knowledge.

Contextualization. When it comes to mastering prompt engineering, contextualization can be just as important as an iterative approach. With this skill, students will provide relevant factors and background information within the prompt to help the language model understand the specific context of each query. By setting the stage and giving necessary details, they increase the chance of receiving accurate and meaningful responses.

Role-playing. Here, students learn to assume different personas or perspectives in different prompts. This can help students obtain responses that align with specific viewpoints or generate creative outputs based on the chosen character.

Those who learn the skills listed above will become adept prompt engineers who will make the most of this technology in a variety of job roles and situations. They will be able to use AI effectively to streamline the problem-solving process, achieve a specific result, or improve their overall job performance. But faculty can set them up for success only by embracing ChatGPT and generative AI in the classroom, as pedagogical tools to help students exercise and expand their critical thinking abilities.

Disrupting Traditional Teaching Models

It's an understatement to say that AI technology is having a seismic effect on faculty. It clearly is disrupting traditional teaching methods and practices, so much so that many faculty initially reacted by banning its use in their classrooms. But just as ChatGPT offers students a new way of learning, it compels faculty to adopt a new way of teaching.

In fact, there is immense potential in utilizing ChatGPT as a practical learning tool to foster and enhance students' creativity and design thinking. If faculty incorporate relevant exercises into their courses, students can learn to use AI not only to write better questions and generate more innovative ideas, but also to discover the technology's limitations.

So, how should faculty embrace AI in their classrooms instead of banning its use? They can adopt evolved pedagogical models that use AI to develop skills in several areas:

Assessment. The rise of AI will inspire the revision and rethinking of teaching and assessment methods. Faculty can use AI platforms to diversify student assessments, including formative, normative, and ipsative evaluations. They can even use AI to assess the new set of skills related to AI itself. These skills include the development of analytical mindsets, familiarity with intelligent user interfaces, the ability to look beyond statistics to instead use predictive analytics, and an understanding of artificial neural networks.

However, while looking at students' final deliverables in a particular course still will be an important part of assessment, those deliverables will not be the only measure of student learning. The objective should shift from focusing on deliverables to evaluating the different stages of the learning process itself.

Faculty can use AI to streamline their own workloads, turning to ChatGPT to handle repetitive tasks while they concentrate on the most valuable aspects of teaching and learning

In other words, educators will need to do more than confirm that students solved a problem when they assess student learning. Educators also will need to ask students to explain how and why they solved a problem in a particular way. For example, faculty can ask learners to detail how they have used AI tools to create their deliverables-from case studies and analyses to full-length dissertations. Learners also could share whether they found the technology useful and how they adjusted to make AI work for them.

Critical thinking. Using AI as a complementary tool may improve students' learning process while challenging their critical thinking abilities. Encouraging human-machine collaboration reinforces a student's ability to adapt the technology to any environment or job role.

Adaptability. There is an urgent need to prepare the future users, designers, and shapers of these technologies to keep up with the pace of change. An essential part of teaching students to use AI is to introduce them to new platforms and functionalities as these innovations arise.

Better yet, faculty also can use AI to streamline their own workloads. They can turn to ChatGPT to handle the repetitive tasks. They can use the platform for help writing syllabi, for example, while they concentrate on the most valuable aspects of teaching and learning: adopting innovative pedagogy, nurturing creative thinking and problem-solving abilities, and ensuring that students develop essential future skills.

AI Is Here to Stay

It is inevitable that faculty increasingly will be working hand-in-hand with AI models to support their teaching. For that reason, higher education institutions must support faculty as they face this new challenge and do as much as possible to make that collaboration a success.

In essence, university faculty are, in a sense, gardeners who are nurturing two different seeds in fertile ground. On the one hand, they are developing AI-ready students; on the other, they are shaping the future use of AI technologies. Faculty might not be able to stop the AI trend from growing, but in this role, they can direct its growth by preparing students to use it as effectively and ethically as possible.

In the coming years, it will be interesting and enriching to observe how faculty teach AI models, as well as how students decide to use AI models to solve problems in the classroom. The technology might also help us address one of the more challenging aspects of teaching: how to engage students in their learning process.

Students will need to master the competencies we outline above if they are going to be able to work effectively with AI. In fact, we believe that as faculty and students experiment more with AI models, they will come to view the technology not as a threat, but as a teammate in their work.

The more schools enable faculty and students to strengthen their technological skills, the more they will support successful AI-human interactions. And the more they support these interactions, the faster we all will accelerate a shift in our mindset, from merely assessing AI's capabilities and implications to improving and refining the way we use it.

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How AI Is Reshaping Higher Education | AACSB

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With the rise of generative artificial intelligence, students need to develop new skills-and educators need to embrace new ways of teaching.

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Inside Higher Ed | Higher Education News, Events and Jobs

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OpenAI unveiled a new version of ChatGPT focused on universities on Thursday, building on work with a handful of higher education institutions that partnered with the tech giant.

The ChatGPT Edu product, expected to start rolling out this summer, is a platform for institutions intended to give students free access. OpenAI said the artificial intelligence (AI) toolset could be used for an array of education applications, including tutoring, writing grant applications and reviewing résumés.

Addressing concerns about privacy and user data, OpenAI said the Edu platform allows for the creation of personalized large language models to power the AI tools, instead of using the publicly available ChatGPT. An OpenAI spokesperson also told Inside Higher Ed that ChatGPT Edu conversations and data are not used to train OpenAI models.

Inara Scott, senior associate dean for Oregon State University's College of Business, noted the concerns with using custom AI tools while keeping data private, saying "I don't know enough about [ChatGPT Edu] yet to say whether it will be [safer]. It sounds like that's the intention of it, which is great and something we would look into further."

The announcement of ChatGPT Edu comes only a couple of weeks after the launch of GPT-4o, or Omni. That new ChatGPT version, with a more realistic voice and quicker verbal response time, brought mixed feelings from educators. Many in academia have struggled to keep up with the rapid advances in artificial intelligence tools.

"This has been like a firehose and it's not settling down," said Marc Watkins, director of the AI Summer Institute for Teachers of Writing at the University of Mississippi. "There's a level of both fatigue and wondering when things are going to stabilize, because ... we like to take our time before we adopt things. It's another thing to worry about."

The fatigue comes as there is more focus than ever on student and faculty data privacy.

"I do think everything is changing, and it opens up conversations about data privacy issues, student data issues," said Rob Moore, assistant professor of educational technology at the University of Florida. He was among the 100 AI-focused hires made by the university in 2021. "That will open up another set of conversations that higher ed has not always been the best about having."

OpenAI said it built ChatGPT Edu after success at universities that used the enterprise version of the AI tool. Those partners included Arizona State University, the University of Pennsylvania, the University of Oxford and the University of Texas at Austin.

ASU kicked off the partnerships in January, and the other institutions were added throughout the spring semester.

On how much input those universities had on ChatGPT Edu, ASU deputy chief information officer Kyle Bowen said "it's hard to draw a line around what is direct feedback."

"It's understanding where the technology is evolving, and what the real world experience is on the ground with the faculty and staff," he said. "It's very much the give and take of the relationship, saying, 'Here's how we can help evolve; here's what we're running into.'"

Bowen said the university's work with the enterprise platform allowed them to create workrooms where documents could be shared in private workspaces. That can be expected for others using the new platform.

"As we engaged with faculty and staff, there needed to be a point where privacy was a part of it, where the information being shared was kept inside of ASU," he said.

One of the biggest benefits of the platform, Bowen said, was using it across departments, faculty and staff. The university sought out proposals from faculty and, later students, on ways to use the technology.

That work can be replicated across other institutions, whether or not they have an aptitude for innovation, Bowen said.

"We've definitely had conversations with a range of institutions, asking 'How do you introduce this technology to your community,'" he said. "And how to approach it to pursue ideas people have and evaluate how to support good outcomes in those scenarios."

Other tech giants are pushing hard into AI. Days after OpenAI launched GPT4-o, Google launched new versions of its AI model, Gemini. Microsoft is also a large AI player and has long-standing relationships with universities.

But this AI frenzy must come with caution, Moore said.

"Everyone wants to get in where they can and unfortunately technology is big business; I'm always seeing companies that want to make money off this newest and greatest technology," he said. "I think it's going to be an arms race. So we really need to teach students and teachers to go back to digital literacy skills to understand how to pick tools and understand that all things aren't the same."

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ChatGPT Edu, emerging after initial partnerships with several universities, is prompting both cautious optimism and worries. OpenAI unveiled a new version of ChatGPT focused on universities on Thursday, building on work with a handful of higher education institutions that partnered with the tech giant.

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Creating an AI strategy has become a distraction for many organizations. It can seem impossible to focus on an overarching strategy when the priority is meeting day-to-day expectations.

Many companies are focused on a technology-first approach in the effort to solve problems as fast as possible. But as UiPath knows, identifying business outcomes first is imperative to successful automation outcomes.

Luckily, Gartner has identified five practical steps to creating a successful strategy, that don't take superpowers to achieve.

Data and analytics leaders can follow the five steps in this research to pursue an AI strategy in a pragmatic fashion.

Read the Gartner® report and learn how to implement an AI strategy that unlocks major value for your business and makes the most of your automation initiatives.

Gartner, 5 Practical Steps to Implement AI Techniques, By Erick Brethenoux, Frances Karamouzis, 13 February 2023

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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5 Practical Steps to Implement AI Techniques: Gartner® Report | UiPath

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Read this Gartner® report to learn how to implement an AI strategy that unlocks major value for your business and makes the most of your automation initiatives.

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Revolutionizing Campus Life with AI, Confessions of a Higher Ed CMO podcast

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Revolutionizing Campus Life with AI, Confessions of a Higher Ed CMO podcast

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AI is not the future of technology. It's the technology of today that is shaping the future. With that mantra in mind, Jaime sits down with Claire Brady, president of Glass Half Full Consulting and a former vice president for student affairs, to discuss how AI is shaping the student experience.

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Takeaways from this episode include:

A look into some of the most innovative ways colleges and universities are using AI.

Insight into what barriers keep people - and universities - from exploring AI and ideas for overcoming them.

Guidance on how to upskill yourself and your team to best leverage AI.

Tips for how campuses can create more cohesive strategies for deploying AI solutions.

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To hear this interview and many more like it, subscribe on Apple Podcasts, , or our website, or search for Confessions of a Higher Ed CMO in your favorite podcast player.
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Connect With Our Host:
Jaime Hunt
https://www.linkedin.com/in/jaimehunt/
https://twitter.com/JaimeHuntIMC

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About The Enrollify Podcast Network:
Confessions of a Higher Ed CMO is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!

Some of our favorites include Talking Tactics and Higher Ed Pulse.

Enrollify is made possible by Element451 - the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.

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Ep. 58: Revolutionizing Campus Life with AI

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Jaime sits down with Claire Brady, president of Glass Half Full Consulting and a former vice president for student affairs, to discuss how AI is shaping the student experience.

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Claire's "Best of" List- AI tools for Higher Ed Pros Wrap Up

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I've thoroughly enjoyed delving into this blog series with you, where I've showcased my top AI tool recommendations for Higher Education professionals.

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Faculty Focus | Higher Ed Teaching & Learning

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Raffi DerSimonian and Christine Montagnino

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Crafting Thoughtful AI Policy in Higher Education: A Guide for Institutional Leaders

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Learn how institutional leaders can develop mission-driven AI policies that balance innovation, ethics, and stakeholder needs in higher education.

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Stanford HAI Artificial Intelligence Bill of Rights | Stanford HAI

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Stanford HAI submitted a response to support the work of the White House Office of Science and Technology to develop an AI Bill of Rights, recommending six principles to guide the public and private uses of biometric and broader AI technologies.

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Generative AI tools are changing the way we teach, learn, research, and work. Explore Harvard's work on the frontier of GenAI.

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This page provides an overview of policy, governance, and other guidance regarding the use of artificial intelligence (AI) at UC Berkeley. The information presented here is subject to change. Please consult your departmental resources for local requirements regarding the use of AI.

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Using generative AI at U of T - Generative AI at U of T

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Using generative AI at U of T Generative AI (GenAI) is a groundbreaking technology that uses Machine Learning techniques to produce content [...]

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Generative Artificial Intelligence Committee Report | U-M Generative AI

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We are pleased to share with you the initial report from the U-M Generative Artificial Intelligence Advisory (GAIA) Committee.

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Generative Artificial Intelligence (AI) tools enable the quick creation of new content based on the analysis of data and inputs. This technology unlocks possibilities to enhance our written and visual content creation approach.

Consider These Factors

As these tools evolve and we experiment with them, it's important to be aware of the associated data privacy, security, ethical, and legal concerns to use them responsibly.

Data Privacy

Generally, only enter information into Generative AI tools that you would share publicly online. Data classified as private, restricted, or restricted-private should not be entered into open or commercial generative AI tools. Become familiar with the Guidelines for Data Classification to ensure the security of university data and the privacy of our students and colleagues.

Security

Malicious actors are also taking advantage of Generative AI to create more convincing phishing emails and fake video, audio, and graphical representations. Follow the guidance provided by the Information Security Office to identify and report suspicious activity.

Copyright Considerations

Copyright guidance regarding authorship is still unclear. Adding personal modifications to Generative AI output may increase the likelihood of copyright protection but exercise caution.

Alter the output to make it your own

Use plagiarism detection services

For visual content, verify that AI vendors have the necessary licenses to allow use on your website, social media, etc.

Accuracy

Remember, Generative AI tools create content based on your prompts and what they learn from other inputs.

Lack of data, poor training, or unclear prompts may result in inaccurate content generation. Always review and fact-check the output of AI tools.

With the rapid adoption of Generative AI tools across varying industries, guidance for use is evolving just as quickly. Following is the current guidance for using these tools at Carnegie Mellon to support teaching, learning, research, and the operation of university business.

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AI Guidelines - Computing Services - Office of the CIO - Carnegie Mellon University

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As these tools evolve and we experiment with them, it’s important to be aware of the associated data privacy, security, ethical, and legal concerns to use them responsibly.

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Generative AI at the University of Edinburgh

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Generative AI at the University of Edinburgh

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Guidance for students and staff on AI use

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Artificial Intelligence offers many opportunities and risks for students and staff. Below are some resources to support the University community as it adopts AI into its studies and work.

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Six Ways to Maximize Authentic Learning in the AI Era

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The release of ChatGPT in November 2022 marked one of the most profound technological advances I’ve seen in my 25 years in higher education. | Today’s educators are pioneers in classrooms where artificial intelligence (AI) is part of daily conversations and students are exploring new ways to write, research, brainstorm, analyze, and complete assignments.

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AI isn't replacing Student Writing- but it is reshaping it article

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AI isn't replacing Student Writing- but it is reshaping it article

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Studies have shown that many students are using AI to brainstorm, learn new information and revise their work. krisanapong detraphiphat/Moment via Getty Images

I'm a writing professor who sees artificial intelligence as more of an opportunity for students, rather than a threat.

That sets me apart from some of my colleagues, who fear that AI is accelerating a glut of superficial content, impeding critical thinking and hindering creative expression. They worry that students are simply using it out of sheer laziness or, worse, to cheat.

Perhaps that's why so many students are afraid to admit that they use ChatGPT.

In The New Yorker magazine, historian D. Graham Burnett recounts asking his undergraduate and graduate students at Princeton whether they'd ever used ChatGPT. No one raised their hand.

"It's not that they're dishonest," he writes. "It's that they're paralyzed."

Students seem to have internalized the belief that using AI for their coursework is somehow wrong. Yet, whether my colleagues like it or not, most college students are using it.

A February 2025 report from the Higher Education Policy Institute in the U.K. found that 92% of university students are using AI in some form. As early as August 2023 - a mere nine months after ChatGPT's public release - more than half of first-year students at Kennesaw State University, the public research institution where I teach, reported that they believed that AI is the future of writing.

It's clear that students aren't going to magically stop using AI. So I think it's important to point out some ways in which AI can actually be a useful tool that enhances, rather than hampers, the writing process.

Helping with the busywork

A February 2025 OpenAI report on ChatGPT use among college-aged users found that more than one-quarter of their ChatGPT conversations were education-related.

The report also revealed that the top five uses for students were writing-centered: starting papers and projects (49%); summarizing long texts (48%); brainstorming creative projects (45%); exploring new topics (44%); and revising writing (44%).

These figures challenge the assumption that students use AI merely to cheat or write entire papers.

Instead, it suggests they are leveraging AI to free up more time to engage in deeper processes and metacognitive behaviors - deliberately organizing ideas, honing arguments and refining style.

If AI allows students to automate routine cognitive tasks - like information retrieval or ensuring that verb tenses are consistent - it doesn't mean they're thinking less. It means their thinking is changing.

Of course, students can misuse AI if they use the technology passively, reflexively accepting its outputs and ideas. And overreliance on ChatGPT can erode a student's unique voice or style.

However, as long as students learn how to use AI intentionally, this shift can be seen as an opportunity, rather than a loss,

Clarifying the creative vision

It has also become clear that AI, when used responsibly, can augment human creativity.

For example, science comedy writer Sarah Rose Siskind recently gave a talk to Harvard students about her creative process. She spoke about how she uses ChatGPT to brainstorm joke setups and explore various comedic scenarios, which allows her to focus on crafting punchlines and refining her comedic timing.

Note how Siskin used AI in ways that didn't supplant the human touch. Instead of replacing her creativity, AI amplified it by providing structured and consistent feedback, giving her more time to polish her jokes.

Another example is the Rhetorical Prompting Method, which I developed alongside fellow Kennesaw State University researchers. Designed for university students and adult learners, it's a framework for conversing with an AI chatbot, one that emphasizes the importance of agency in guiding AI outputs.

When writers use precise language to prompt, critical thinking to reflect, and intentional revision to sculpt inputs and outputs, they direct AI to help them generate content that aligns with their vision.

There's still a process

The Rhetorical Prompting Method mirrors best practices in process writing, which encourages writers to revisit, refine and revise their drafts.

When using ChatGPT, though, it's all about thoughtfully revisiting and revising prompts and outputs.

For instance, say a student wants to create a compelling PSA for social media to encourage campus composting. She considers her audience. She prompts ChatGPT to draft a short, upbeat message in under 50 words that's geared to college students.

Reading the first output, she notices it lacks urgency. So she revises the prompt to emphasize immediate impact. She also adds some additional specifics that are important to her message, such as the location of an information session. The final PSA reads:

"Every scrap counts! Join campus composting today at the Commons. Your leftovers aren't trash - they're tomorrow's gardens. Help our university bloom brighter, one compost bin at a time."

The Rhetorical Prompting Method isn't groundbreaking; it's riffing on a process that's been tested in the writing studies discipline for decades. But I've found that it works by directing writers how to intentionally prompt.

I know this because we asked users about their experiences. In an ongoing study, my colleagues and I polled 133 people who used the Rhetorical Prompting Method for their academic and professional writing:

92% reported that it helped them evaluate writing choices before and during their process.

75% said that they were able to maintain their authentic voice while using AI assistance.

89% responded that it helped them think critically about their writing.

The data suggests that learners take their writing seriously. Their responses reveal that they are thinking carefully about their writing styles and strategies. While this data is preliminary, we continue to gather responses in different courses, disciplines and learning environments.

All of this is to say that, while there are divergent points of view over when and where it's appropriate to use AI, students are certainly using it. And being provided with a framework can help them think more deeply about their writing.

AI, then, is not just a tool that's useful for trivial tasks. It can be an asset for creativity. If today's students - who are actively using AI to write, revise and explore ideas - see AI as a writing partner, I think it's a good idea for professors to start thinking about helping them learn the best ways to work with it.

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AI isn't replacing student writing - but it is reshaping it

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If AI allows students to automate routine cognitive tasks, it doesn't mean they're thinking less. It means their thinking is changing.

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Glass Half Full Consulting | Explore AI Opportunities Today

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Transparency Over Detection- The Design Statement Approach to Student AI Use — Glass Half Full Consulting | Explore AI Opportunities Today

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The Friendship Bench: A Simple Yet Powerful Approach to Campus Mental Health.