2025

Artificial intelligence (AI) is becoming more pervasive by the day. In higher education, administration teams and faculty are avoiding commitment to AI incorporation into professional development programs and classrooms, scrambling to develop AI-related policies to guide employees and students, actively implementing programs that address the needs of everyone involved with AI-enhanced efforts, or some combination of the three.

This issue of Innovatus focuses on efforts to enhance support for students and staff by integrating AI into daily institutional operations, offering ways that community colleges can respond to the rising tide of AI on their campuses and in their communities. Judith Cardenas discusses AI’s potential to reshape how we work and the fact that community colleges are well equipped to take on the benefits, as well as the challenges, of this rapidly changing technology. Anna Haney-Withrow and Rozalind Jester speak to adapting to generative AI using four leadership pillars that guide institutional efforts. Ian Roark emphasizes the need for community colleges to understand the effects of AI in all sectors of industry, and to rapidly adjust our programs accordingly. And Robert Atkins reports on how an advanced program evaluation system enables colleges to make better, faster decisions to stay one step ahead of the competition.

Innovatus was created by the League as a resource to inspire discussions among college leaders, faculty, and staff regarding contemporary issues impacting our field. Since 2018, the magazine has encouraged stakeholders to reflect on the meaning of innovation and to rethink how we serve students. This issue continues that mission, and we hope you find its contents enlightening.

David Ross, Ph.D.

Chair, Board of Directors, League for Innovation in the Community College
President and CEO, Southern Alberta Institute of Technology

Imagine starting your day with every meeting meticulously organized—complete with notes, action items, and materials seamlessly compiled from your emails and files—all at the click of a button. Or envision a schedule brimming with meetings where preparation time is halved, thanks to an artificial intelligence co-companion that efficiently follows up moments after each session.

For many, such scenarios seem like science fiction. Yet, in our era of AI, the improbable has become reality. Mustafa Suleyman,¹ co-founder of DeepMind and a leading figure in AI, encapsulates this transformation:

From Mundane to Revolutionary: The Ascendance of AI

Technologies like drone deliveries, robotic vacuums autonomously cleaning our homes, fraud alerts on credit cards, and eerily precise targeted ads have normalized disruptive innovations. However, in November 2022, OpenAI launched ChatGPT, a groundbreaking application that showcased AI’s potential to the masses, heralding an intelligence revolution and redefining human-technology interaction. As AI has become more versatile, users are realizing its potential to reshape how we work, share ideas, and solve problems. With millions of people actively collaborating with AI in creative and cognitive processes, generative AI tools have reached a cultural turning point, fostering a new era of innovation, productivity, and ethical inquiry.

Rapid Transformation and Its Challenges

Our personal and professional landscapes are evolving at an unprecedented pace. The integration of AI has sparked fear, uncertainty, curiosity, and, most importantly, opportunity. In higher education—particularly within community colleges—the adoption of disruptive technologies has been sluggish. Leaders grapple with balancing board expectations, adapting to workforce shifts, cultivating agile and inclusive cultures, and addressing the diverse and evolving needs of student populations.

However, community colleges are uniquely positioned in this unpredictable world of change and disruption. This moment calls for action: Our leaders are charged with creating cultures of innovation that foster creativity and agility. We are called to reinforce our value proposition while simultaneously building new individual and organizational capabilities to ensure resilience and relevance.

Amid this complexity, AI is fundamentally reshaping how students gather information, make decisions, and engage in learning experiences. A recent study by Tyton Partners² revealed that nearly 50 percent of college students are using AI tools, while only 22 percent of faculty members are doing the same.

Lessons From Industry: A Strategic Shift for Higher Education

What can higher education learn from industries that have successfully embraced AI? How can colleges pivot their strategies to harness AI’s potential? To address these questions, community college leaders must adopt a proactive approach to AI integration. Utilizing S.H.I.F.T. as a framework for change can begin this process:

Strategize With AI. See AI as a strategic approach to change, not just a tool. Balance the needs of students, faculty, staff, business processes, and technology to stay in control of the transformation.

Honor Your Values. Build your AI strategy around your institution’s unique value proposition. Don’t mimic other colleges; focus on serving your distinct community.

Initiate Transformation. Accept that AI adoption is a long-term journey involving large-scale changes and shifts in work processes. Be open to the democratization of AI tools.

Foster a Change Mindset. Adapt to the reality that community colleges may not lead the disruption but can thrive by embracing change and avoiding resistance.

Take the Lead. Be proactive in shaping the future rather than reacting to it. Drive innovation and transformation intentionally.

The Road Ahead: Leading Through Innovation

By embracing AI, community colleges can enhance learning experiences, foster innovation, and equip students with the skills necessary to navigate and contribute to an AI-driven world. This proactive approach positions educational institutions at the forefront of the technological revolution, enabling them to shape the future workforce effectively.

The path forward requires bold leadership, strategic vision, and a commitment to shaping the future of education through AI. By fostering creativity, agility, and innovation, we can transform today’s challenges into tomorrow’s opportunities, ensuring that our institutions remain relevant, impactful, and future-ready.

From Bytes to Belonging: Making AI Work for Everyone outlines how colleges can integrate AI into operations in ways that encourage faculty, staff, and students to embrace change and explore new ideas.

¹ Suleyman, M. (2023). The Coming Wave: Technology, power, and the twenty-first century’s greatest dilemma, p. 175. Crown.
² Shaw, C., Yuan, L., Brennan, D., Martin, S., Janson, N., Fox, K., & Bryant, G. (2023). GenAI in Higher Education: Fall 2023 Update of Time for Class Study, p. 6. Tyton Partners.

College leaders know from experience that success comes from a balance of responding skillfully to what we cannot control and being proactive in shaping the outcomes we can. For our team at Florida SouthWestern State College, the challenge of adapting to generative AI, with this healthy balance at play, has relied on four leadership pillars: inclusive strategy, thoughtful influence, innovation, and inter-institutional collaboration.

Inclusive Strategy

Provide information and opportunities to engage. The more informed people are, the more their perspectives contribute to a productive conversation.

Like many institutions, we convened a collegewide committee to respond to the challenges we encountered after the release of ChatGPT. Largely focused, at the time, on academic integrity, these conversations were spirited and even difficult; our leadership challenge was to include various perspectives while keeping the dialogue productive. We launched a skillful response, leveraging several teams to create robust resources, including a student toolkit on our LMS, faculty development opportunities, and weekly AI Open Forums to allow all to engage with and consider various perspectives on the implications of the technology.

Thoughtful Influence

Proactively shape the way AI influences both culture and workflows at the college. AI is a powerful lever, and our use of it should be thoughtful and strategic.

As we crafted our initial response, we prioritized a trauma-informed approach that recognized how technological change can affect different populations uniquely. We emphasized supportive conversations over punitive measures and developed specific guidance for faculty on having productive dialogues with students about AI use. Mindful of a desire to strengthen collegiality and support in a time of change, we also developed a framework to assist people in understanding others’ perspectives and responses to AI.

As our efforts have evolved, we are also endeavoring to shape the way AI gets used across the college, providing training for all faculty and staff that focuses on impact over productivity and asks participants to consider how using AI can strengthen bonds between people rather than leading to isolation. Importantly, these trainings include participation from every leadership level at the college, ensuring that our use of AI and other emerging technologies supports our institutional values.

Innovation

Create space for innovation and be patient. AI will require authentic transformation in teaching and learning, and your people can lead it with the right support.

Innovation, particularly at the faculty level, will be a crucial part of both ensuring learning and helping students prepare for workforce roles that will require AI literacy, both of which are central to the value of the degrees we grant. After our initial response—providing resources and upskilling—we turned to setting the stage for innovation. We chose to focus on efforts that centered on play, experimentation, and community building rather than looking for immediately employable solutions. Initiatives included an AI Playground weekly challenge, an experiential retreat for instructional designers, and a self-guided tour of AI-assisted pedagogical experiments for faculty. We’re beginning to see some emerging innovations in classes, but we know this approach will require patience. Ultimately, we believe the most transformative innovations in teaching and learning will emerge organically from those closest to students.

Inter-Institutional Collaboration

Leverage inter-institutional connections to enhance impact. The fast-moving pace and complexity of AI warrants cooperation and perspective sharing.

Our ongoing initiatives, both responsive and proactive, have been strengthened by a strong emphasis on inter-institutional collaboration. Early in 2024, we were both leaders in establishing the Florida AI Learning Consortium (FALCON), a grassroots effort of higher education professionals in Florida aiming to foster ethical, responsible, and innovative integration of AI. Participating in and lending our own voices to the consortium has allowed us to maintain a big-picture perspective relative to multiple aspects of AI, including ethics, workforce preparation, faculty development, research, and data privacy and security.

Regularly interacting with colleagues who share our challenges and sense of possibility has been informational and motivating not only for our leadership team but also for the staff and faculty at the college who participate in FALCON. As this collaboration matures, it provides a resource to stay abreast of how emerging issues and developments in AI are aligned with our state’s challenges and unique economic landscape, including relevant industries, workforce needs, and statewide economic priorities.

We know that as technologies evolve and the pace of change accelerates, balanced and thoughtful leadership has the potential to help our colleges leverage change and continue to ensure student success and vibrant futures for our communities. When it comes to generative AI, there are likely to be more surprises, but we can meet them from the foundation of solid leadership principles and the lessons we learn and share with one another along the way.

November 30, 2022, was a harbinger for monumental scientific and social change with the presentation of generative AI, specifically ChatGPT,¹ in a way that both scientists and laypeople could understand. Since then, a nearly ubiquitous discussion of AI, ranging from excitement to fear, has captured imaginations around the world and dominated higher education dialogue.

Analyzing the Hype

The hype surrounding GenAI surpassed the peak of Gartner’s Hype Cycle in 2024. Now, the discussion is increasingly centered on the fear of disruption that this powerful technology may bring as well as the disillusionment of what promises it may not fulfill.² In this era of moving from the hype and the Trough of Disillusionment to the Plateau of Productivity, changes in AI are happening fast, with maximum impact being realized within the next five years.³ Opportunities that benefit college efficiency and the student experience include quick wins with AI-powered chatbots, AI-generated content, and early warning systems to assist at-risk learners as well as deeper integrations such as personalized learning, financial aid optimization, and data-driven decision making at a level we have not yet seen in our sector.⁴ As stated by Jessica Stone:⁵

Applying this perspective to teaching and learning will ultimately transform the learner experience,⁶ as the classroom in all disciplines will necessarily move from a knowledge-based paradigm to an application-based paradigm that may finally eliminate the artificial distinction between academic and career and technical/workforce programs at our colleges. When AI reaches the Plateau of Productivity,⁷ all college programming must demonstrate value to learners and employers in a physical real-world context. Programs that don’t may be rendered irrelevant by learners and employers alike. Determining how AI will impact our workforce development mission will be central to our effectiveness and relevancy moving forward.

Defining Workforce Development in an AI Context

In this context of AI-driven change, workforce development can no longer be relegated to a specific unit at a college that offers unique or innovative, often noncredit, educational/training opportunities. At Pima Community College, workforce development is a paradigm and a strategy rather than a set of programs or course offerings. It defines our collegewide ability to meet employers’ demands today and into the future in an economic development and technology-driven context. Colleges that do not embrace this workforce development paradigm shift may already be behind in understanding how disruptive technologies such as AI can render their current educational and training programs irrelevant in a technological and labor market context. We must empower our learners for equitable outcomes and access to high-paying quality jobs for greater success in this dynamic time.

The University of Florida (UF) has pioneered an AI Across the Curriculum regimen that community colleges may learn from and adopt in a local context.⁸ UF implemented many AI-focused changes in its Quality Enhancement Plan for accreditation under the Southern Association of Colleges and Schools Commission on Colleges, establishing four key areas of focus:

1. Understanding AI literacy, including the basics of AI and how it functions;
2. Using and applying AI to solve problems and accomplish tasks;
3. Building and evaluating AI systems that are reliable, ethical, and fair; and
4. Understanding the moral and ethical implications of AI to make informed decisions using these tools and systems.

In addition, UF implemented an AI certificate program that is open to learners across all disciplines, incorporates fundamental AI skills relevant to all sectors, and includes specific and applied AI courses for each college/major. By integrating AI into nearly every facet of the institution and increasing the interdisciplinary approach to AI across industry sectors, UF has increasingly assured its continued relevancy in this age of rapid transformation. This approach is something that all community colleges can adopt to retain relevancy in programming and the empowerment of learners.⁹

The impact and effect of AI in all its forms is real and will soon enter the Plateau of Productivity phase.¹⁰ Community colleges need to plan and engage now, even if the local impacts are not clear. While Pima Community College surveyed local employers about the impact of AI, we need more data to ensure that we are pragmatically contextualizing national development of AI’s impact on local industry sectors.

Community colleges need to understand the effects of AI in all sectors of business and industry in which we offer programs, and rapidly adjust accordingly. This includes adapting our career and technical/workforce programs to new entry-level hiring points and job displacement caused by AI. Similarly, our transfer programs need to quickly adapt, as their relevancy is inextricably tied to that of the universities with whom we partner.

¹ Alssid, J., & LeMoinne, K. (Hosts). (2024, February 27). AI and the Future of Higher Ed: Insights from Paul LeBlanc [Audio podcast episode]. Work Forces.
^2/7/10 Gartner. (2024, August 21). Gartner 2024 Hype Cycle for Emerging Technologies Highlights Developer Productivity, Total Experience, AI and Security [Press release].
³ National Disaster Preparedness Training Center. (2024). Artificial Intelligence: Helping or Harming Emergency Management? [Webinar].
⁴ Lambert, L. D., & Dhawan, R. (2024, September 30). A blueprint for AI integration. Community College Daily.
⁵ Stone, J. (2023, December 21). Mastering Innovation Leadership: para 2. The Vation Ventures blog.
⁶ Kurtz, G., Amzalag, M., Shaked, N., Zaguri, Y., Kohen-Vacs, D., Gal, E., Zailer, G., & Barak-Medina, E. (2024). Strategies for integrating generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities. Education Sciences, 14(503).
^8/9 Southworth, J., Migliaccio, K., Glover, J., Glover. J’N., Reed. D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers in Education: Artificial Intelligence, 4.

An academic program evaluation system (PES) should enable smart, data-informed decisions about new programs to start, existing programs to grow, and a few programs that may need to be phased out. AI has been the foundation of PES for over a decade. More recently, AI is augmenting the traditional PES, making it more accessible and giving it some superpowers.

Changing the Rules

While it may not be obvious, the traditional labor data often used to evaluate programs combines rule-based systems and AI. The rules that align academic programs with career occupations were developed by hand. In most cases, these rules were never validated: Few checked to see if graduates of a given program went to work in the occupations they were trained to enter. Using web scrapers and AI tools, especially natural language processing, analysts now have data on tens of millions of graduates and what jobs they took. Unsurprisingly, the analysts found that the hand-drawn links from programs to jobs had little relationship to real-world results. The old links found five to 10 occupations for most graduates of a given program; the AI-enabled analysis of real-world data shows that graduates of most programs go into hundreds of different occupations.

Presenting the Data

AI has also revolutionized the presentation of program data. Typically, data on program markets was presented in rows and columns of numbers—lots of them. For many, the data was a little overwhelming and difficult to interpret. People wanted a report they could read in straightforward text. Consultants were happy to do the analysis and write the reports, which cost thousands of dollars and took at least six weeks to prepare. Gray Decision Intelligence’s PES can now feed data on over 50 market metrics into a large language model (LLM) and produce a text report in two minutes.

Taming the AI Beast

Reliably converting a numeric report into text takes a few months of work. Much of this work has little to do with AI itself; as with most AI applications, the challenge is assembling the data. When you set out on the AI journey, be sure to bring a few data heavy-lifters, more formally known as data engineers; they will do the hard work of assembling and cleaning up your data. Then, you have to tame the unruly beasts of AI. Even if you give an LLM a set of numbers, it will often make up a few more, do some strange calculations, like averaging the number of jobs with the number of colleges, and miscalculate other numbers. To make the LLM work, you have to develop a prompt that fences in the LLM and makes it use the exact data and calculations you provide. Then, you need to run hundreds of tests to be sure it behaves itself.

Calling on the Superpowers

Now for the superpowers: If you apply machine learning to about five million data points on higher education institutions and program size, you can accurately estimate the size of potential new programs at an institution. Gray DI’s Predict Program Size tool can perform this minor miracle for any IPEDS program at any institution. Interestingly, AI can even figure out an institution’s strengths (e.g., healthcare or engineering) and adjust its predictions accordingly. For example, Predict’s estimated enrollment for a cybersecurity program at a liberal arts college would be lower than its estimate for cybersecurity at a more technical college. As a side benefit, Gray DI’s Predict Program Size includes pro forma financials so that you can estimate seven-year budgets, cash flow, and breakeven for new programs, all in under an hour.

This combination of a text interface with advanced data, predictions, and cash flow models enables your faculty and administrators to use data to inform their decisions when, before, they might have shied away from all the time and analysis required. They help departments develop sound estimates of program financials so there are no unpleasant surprises when programs roll out. Most importantly, these more advanced program evaluation systems enable you to make better, faster decisions to stay one step ahead of the competition—and well away from the demographic cliff.

As artificial intelligence has gained prominence in society over the past few years, a divide has emerged among its users. Early adopters are quickly integrating these tools into various aspects of teaching and learning, while others remain hesitant to use or even consider them. This divide presents challenges in bridging gaps, as individuals interpret the term “artificial intelligence” in various ways. This has led to confusion among students, who are uncertain about whether they are complying with faculty members who have implemented “no AI rules” in their courses, as faculty interpretation and application of these rules can differ significantly.

Establishing a Common Foundation

The Maricopa Center for Learning and Innovation (MCLI), a department within Maricopa Community Colleges’ District Office, in collaboration with the district’s AI Task Force, recognized that a technology literacy workshop was needed to help establish a common vocabulary and to introduce how AI may be used in various teaching and learning scenarios by both faculty and students.

As the AI Task Force reviewed goals for an AI literacy workshop, we found an article by EDUCAUSE on this topic.¹ Faculty developers from Barnard College developed a structure by adapting a framework for AI literacy that utilizes Bloom’s Taxonomy. We found that the framework of (1) Understand AI, (2) Use and Apply AI, (3) Analyze and Evaluate AI, and (4) Create AI was not only practical but also ideally suited to our needs. We also identified that we need to focus on the first two levels of the framework, as our goal was to build a common language and understanding of the use of AI tools.

As MCLI began developing the first workshop, our primary goal was to provide participants with a broad understanding of AI. While recent advances in generative AI are fascinating, we wanted to ensure that participants recognize AI’s presence in our daily lives for many years. This is illustrated by defining AI and highlighting that spell and grammar-checking tools in document applications have been considered AI tools for over 20 years. We guide participants through recent developments in large language models, their evolution, and best practices for using these tools. This self-paced workshop, focused on the understanding level of Bloom’s Taxonomy, includes auto-graded knowledge checks and an activity in which participants can create course-related content using various generative AI tools. The self-paced nature of the workshop helps participants feel comfortable and in control of their learning journey.

MCLI is also developing a second workshop in the series focused on using and applying AI. This workshop will emphasize the use of generative AI tools and explore how to critically evaluate their responses.

Initial Response

We have received positive feedback from the first offerings of the Understanding AI workshop. One participant commented in our evaluation, “[I] appreciated the course for its coverage of key AI concepts, making it easier for beginners like me. Very engaging materials and resources and real-life case studies kept the content interesting.” Another participant stated, “I liked the approach to showing different drawbacks to AI and the way the course talked about using education to combat against those drawbacks.”

Next Steps

By emphasizing AI literacy, we can shift the conversation from whether AI should be used to a more nuanced discussion about when and how it is appropriate to use AI. We are developing a facilitator’s guide for our workshop to support this shift, enabling instructional technologists and faculty developers throughout the district to conduct the workshop as either an asynchronous online course or an in-person session. Additionally, we will complete the development of our Use and Apply AI workshop, which will focus on utilizing various AI tools and evaluating their responses.

¹ EDUCAUSE. (2024, June 3). A Framework for AI Literacy. EDUCAUSE Review.