AI Upskilling for Business School Faculty

• Faculty may be hesitant to adopt new technology because it can require significant changes to established teaching methods, raise ethical considerations that need to be carefully navigated, and involve an initial learning curve to master.
• Faculty often are reluctant to embrace new technology because they find it intimidating, disruptive to traditional teaching methods, and ethically questionable.
• Administrators can aid faculty on their AI learning journeys by providing learning communities, mentorship, experiential learning opportunities, and institutional support.

 
Although I’m a tenured full professor and dean of the College of University Libraries & Learning Sciences at the University of New Mexico in Albuquerque, I recently returned to the classroom. After learning about a program at the University of Oxford in the U.K., I sat among students at the Saïd Business School not to teach, but to learn about AI and its transformative potential.

Choosing to learn about AI in a business school setting was a strategic decision on my part. I recognize that business schools are early adopters in the AI technology cycle, and they’re also leaders in the practical and applied nature of education. My experience at Saïd not only deepened my understanding of AI, but also illuminated the critical role that business schools play in shaping how future leaders approach this burgeoning technology.

I believe that, as higher education leaders, we must accept a dual mandate: to lead our institutions in adopting AI and to serve as catalysts for our faculty’s continual professional development in this area. By doing so, we can turn AI from a source of apprehension into a source of empowerment for faculty—and a source of innovation and competitive advantage for our institutions.

Barriers to Adoption

Despite the clear advantages of integrating AI into business education, several barriers can inhibit faculty from fully engaging with this technology.

First and foremost is the anxiety surrounding the perceived complexity of AI. Many educators feel that AI is a field reserved for those with deep technical expertise, which can deter them from even beginning to understand how it might be applicable in their teaching or research. This intimidation factor often is compounded by a lack of structured opportunities for faculty to pursue professional development in the field of AI. Thus, many professors are unsure of where to start or how to proceed.

Moreover, there is a pervasive concern about the relevance of AI to current teaching methodologies and subject matter. Some faculty question how AI can be integrated into established disciplines without disrupting traditional learning outcomes or diminishing the human element that is so vital to education. Others fear that AI could replace them in certain capacities, which creates a significant emotional and professional hurdle.

Many educators feel that AI is a field reserved for those with deep technical expertise, which can deter them from understanding how it might be applicable in their teaching or research.

Ethical dilemmas also play a role in this hesitancy. As they shepherd students through the learning process, professors must be aware of the potential biases inherent in AI algorithms. As they act as custodians of students’ personal data, faculty must navigate the moral implications of privacy issues. These ethical considerations sometimes add another layer of complexity to the adoption of AI technologies in educational settings.

Finally, some faculty are reluctant to integrate AI into their teaching processes because they receive limited institutional support. If their administrations do not provide resources, training, and encouragement, faculty may feel isolated in their attempts to incorporate AI into their professional practices. This lack of support can stifle innovation and slow the adoption of AI technologies that could otherwise enhance educational quality and efficiency.

Principles of Learning

To address these barriers and facilitate the adoption of AI among business school faculty, administrators can turn to Malcolm Knowles’ Adult Learning Theory. Knowles posits that adult learners have distinct characteristics and learning preferences that organizations must consider when designing educational experiences for them. By applying the following four principles to AI education for faculty, we can create an environment that encourages exploration, experimentation, and, ultimately, confident adoption of this technology.

Self-Concept and Self-Directed Learning. To capitalize on this preference, business schools should provide faculty with a range of self-paced, self-directed AI learning opportunities. These could include curated collections of AI resources, online courses, and individualized learning plans that allow faculty to explore AI at their own pace and in alignment with their specific interests and needs.

Experience- and Problem-Oriented Learning. Adult learners bring a wealth of life and professional experience to their learning endeavors. They seek to connect new knowledge to this existing reservoir of experience and are most engaged when learning is framed as a means of solving real-world problems.

Therefore, AI education for faculty should be grounded in the practical challenges and opportunities educators face in their teaching and research. Case studies, workshops, and hands-on projects that allow faculty to apply AI to their specific areas of expertise can help bridge the gap between theory and practice, making the relevance of AI immediately apparent.

By showcasing how AI can amplify rather than replace human capabilities, administrators can demonstrate how mastering AI will allow faculty to augment their impact and fulfill their missions.

Readiness to Learn and Orientation to Learning. According to Knowles, adult learners become ready to learn when they need to know or do something in order to perform more effectively in some aspect of their lives. They’re also more likely to orient their learning toward tasks and problems rather than toward subjects.

To tap into these tendencies, business schools should emphasize that AI education is a way for professors to improve student outcomes while enhancing their own teaching effectiveness, research impact, and professional growth. By highlighting the tangible benefits of AI mastery, administrators can foster a sense of urgency among faculty.

Motivation to Learn. Some adults engage in learning to secure promotions and find better jobs. But others are spurred on by powerful internal motivators such as the desire for increased job satisfaction, self-esteem, and quality of life.

To harness this internal motivation, business school leaders should emphasize how AI competency aligns with each faculty member’s professional identity and values. By showcasing how AI can amplify rather than replace human capabilities, administrators can find ways to demonstrate how mastering AI will allow faculty to augment their impact and fulfill their missions as educators and researchers.

Strategies for AI Upskilling

To support faculty in their AI learning journeys, school leaders can make the following five resources available at their institutions:

AI learning communities. In such communities, faculty can share experiences, exchange ideas, and solve problems collaboratively.

Mentorship and coaching. Faculty who need personalized guidance and support will benefit from access to AI experts who can offer tailored advice and encouragement.

School leaders can demonstrate their commitment to AI adoption by providing robust resources, incentives, and rewards for faculty who engage in AI learning and integration efforts.

Experiential learning opportunities. Pilot projects, classroom experiments, and research collaborations give faculty hands-on, low-risk opportunities to practice using AI tools and techniques in their teaching and research.

Celebrations and showcases. By regularly sharing examples of how faculty have successfully adopted AI, administrators can highlight the positive impact that its use has had on student learning, research outcomes, and institutional innovation.

Institutional support and resources. School leaders can demonstrate their commitment to AI adoption by providing robust resources, incentives, and rewards for faculty who engage in AI learning and integration efforts.

A Working Example

Last summer at my college, we piloted a 12-week cohort-based program for our staff and faculty to experiment with GPT-4, the premium version of ChatGPT. We based the program on the principles of adult learning.

During two weeks of training, participants developed individual projects that they could complete with the aid of GPT-4. Then they spent 10 weeks experimenting with the tool and sharing information about the lessons they’d learned and the challenges they’d mastered. At the end of the program, they all reported a higher level of understanding of AI and more confidence in using GPT-4. The results were so promising that we are now expanding the program to faculty and instructors outside of our college.

One of my personal goals is to provide resources to any faculty members interested in improving their proficiency with AI. As president-elect of the Association of College & Research Libraries (ACRL), I am committed to advancing AI literacy and integrating AI into library services. I also have developed a new course, “The Art of ChatGPT,” which is freely accessible through Sage (after individuals enter registration information). It is intended for faculty who want to use ChatGPT but need expert help to get started.

I believe it is the charge of institutional leaders to enable faculty to take ownership of their AI learning journeys. When administrators create the conditions for professors to experiment and innovate with new technology, those educators will model the kind of adaptive, lifelong learning that all of us seek to cultivate in our students.