By Rubya Ahmed,  Lovette Coston, Linsey A. Hollingshead, Johnny H. Manson Jr., Charles F. Swingle, Johngerlyn Young, and Tiffany R. Snyder, Indiana Wesleyan University

This article explores an innovative approach to AI collaboration through problem-based learning (PBL) and student-driven inquiry. We draw on our experiences from Indiana Wesleyan University's (IWU) Doctor of Business Administration (DBA) program (Indiana Wesleyan University, n.d.), where the "Dream Team”—comprising third-year doctoral students—investigated the implications of generative AI for higher education as part of an eight-week online course. Our team’s collaboration yielded valuable resources for IWU educators and administrators, providing a framework for integrating AI into the curriculum while upholding a virtuous business perspective. Our goal in sharing our experiences is to inspire other institutions to leverage student voices and research to address the challenges and opportunities presented by generative AI.

Generative AI in Higher Education: A Brief Overview

Generative AI is becoming increasingly prevalent in higher education, offering customized learning experiences, streamlining administrative tasks, and enhancing research capabilities. It is anticipated to improve the accessibility and efficiency of education delivery, thereby deepening student learning and supporting educators and business operations (Diaz, 2024). For instance, tools like OpenAI’s ChatGPT can generate dynamic content and provide personalized support contributing to improved student engagement and learning outcomes (Henriques & Trajkovik, 2024).

However, the adoption of generative AI is not without its challenges. Some educators, and even students, resist integrating this technology, and existing higher education systems and infrastructures may not readily adapt to these advancements (Zailer & Barak-Medina, 2024). Despite the hurdles, collaboration among educational institutions is paving the way for more effective AI integration.

Some educators are collaborating with other organizations and institutions to share best practices and collectively navigate the challenges of generative AI integration. These collaborative efforts include forming committees and working groups to develop shared strategies, policies, and resources for AI use in education (Diaz, 2024). For instance, EDUCAUSE encourages global institutions to participate in its AI Community Group, where members can exchange insights and experiences related to AI adoption (EDUCAUSE, 2024a). Similarly, regional opportunities such as Colleague2Colleague exist to facilitate collaboration between institutions’ professionals. Such collaborations help pool knowledge and resources, enabling institutions to learn from each other's experiences and adopt more effective AI strategies (EDUCAUSE, 2024b). As generative AI continues to shape the landscape of higher education, academic programs must thoughtfully integrate this technology into their curricula. IWU has embraced this challenge through its forward-thinking DBA program (Indiana Wesleyan University, n.d.).

A core course, Current Issues for a Virtuous Organization, provides a practical example of how
faculty and students can collaboratively explore generative AI and apply their learning within an
academic setting. By leveraging generative AI, IWU's DBA program not only enhances the educational experience but also prepares students to navigate and influence the evolving technological landscape (Snyder & Linerode, 2024).

Course Dynamics: An Overview

The Current Issues for a Virtuous Organizations course is an eight-week online course that
empowers students to assess forward-thinking leadership challenges emerging from contemporary events and critique current business issues through a virtuous business lens. For the past year, the current issue has been generative AI and its evolving impact on the academic community. Students determine the effect of the identified issue on organizations by incorporating synthesized research into their applied practice, allowing for continued, updated research and learning beyond the course curriculum. The Current Issues for a Virtuous Organization course is uniquely set up in a problem-based learning (PBL) format.


PBL is an instructional approach where students learn by actively engaging in real-world, open-ended problems (Sing & Bashir, 2018). This method encourages students to develop critical thinking, problem-solving, and collaborative skills by working through complex scenarios, often in group settings (Tan et al., 2023). Unlike traditional teaching methods focusing on direct content delivery, PBL positions the instructor as a facilitator, guiding students to explore and solve the presented issues (Duch et al., 2001).




Given AI's rapidly evolving and complex nature, PBL provides an ideal framework for students to investigate and navigate the multifaceted challenges and opportunities that generative AI presents in higher education (Snyder & Linerode, 2024).

Course Introduction: Demystifying Generative AI

For the Dream Team, and shared among other student teams, there were mixed opinions about generative AI and knowledge of tools like Open AI’s Chat GPT at the start of the course. Some team members had previous experience using ChatGPT with a preliminary knowledge of generative AI. Other team members had reservations about the innovative advancements, highlighting personal challenges with academic integrity and fears of compromising the quality of scholarly outcomes. Different levels of generative AI knowledge can positively affect cross-team learning; however, they can create dissonance regarding the balance of individual contributions and the potential for over-reliance on generative AI (Frontiers, 2023; Illinois CITL, 2023). In the first week of the course, the Dream Team established and submitted a written plan to chart their course, establish expectations, and assign roles and responsibilities, mitigating dissonance.

The course content progressively introduced and demystified generative AI through informative asynchronous discussions, applicable articles, and insightful videos shared in course announcements and emails. Team members actively engaged in synchronous online learning discussions guided by the professor. Utilizing the PBL method, the team maintained weekly student-led virtual meetings to discuss generative AI, personal experiences gained and questions from their learning. Team discussions continued the advance of new knowledge in understanding generative AI. By week three of the course, students applied practical knowledge of generative AI using ChatGPT, versions 3.5 and 4o. The course offered multiple opportunities for students to use generative AI in low-stakes exercises. One example and critical component of student learning was the comparative analysis assignment. Team members individually wrote a literature review and then prompted ChatGPT 3.5 or 4o to write a literature review with the same instructions. Individually, team members compared the two outcomes. Relying again on the PBL method, the team wrote a cohesive analysis of the overall differences between human and AI authors, the strengths and limitations of the students' work, and the strengths and limitations of the generative AI output.

Through firsthand experience and the low-stakes experiment, the course encouraged students to apply knowledge, challenge preconceived stigmas, and strengthen their understanding of generative AI in academia (Snyder & Linerode, 2024).

Course Progression: Cultivating AI Confidence

In the course, cultivating confidence and critical thinking using generative AI began with scholarly investigation. Student teams, including ours, prepared a problem investigative report and literature review to establish what was known and what gaps exist in the literature surrounding generative AI implications in higher education. We identified several takeaways from the scholarly investigation that improved our understanding and increased our confidence in making decisions about generative AI in higher education.

The Dream Team concluded that educators and administrators must adopt a forward-looking approach regarding technological proficiency and introspective regarding ethical and metacognitive reflexivity. In our review of the literature, we found that merely adding AI content to the curriculum is insufficient. Effective integration requires a "techno-instructional or techno-pedagogical design," highlighting the link between technological and pedagogical dimensions in virtual training (Ruiz-Rojas et al., 2023). The technological dimension involves selecting appropriate tools, such as virtual platforms and AI software applications. In contrast, the pedagogical dimension requires understanding the target audience, setting clear objectives, developing content, planning activities, and evaluating processes and outcomes. Frameworks and methodologies like this form the cornerstone of instructional design in virtual training. The process begins with an instructional design matrix, selecting subjects, developing units, identifying topics, stating objectives, and defining expected learning outcomes (Ruiz-Rojas et al., 2023).

We also learned that AI implications in higher education can be examined through entangled pedagogy. Entangled pedagogy emphasizes the interconnected relationships between learners, educators, and materials in educational settings (Fawns, 2022). This perspective positions AI as an agent that can generate information, assess student work, or guide learning trajectories. Drawing from actor-network theory (ANT), this evolving network is seen as a socio-technical construct where human and non-human entities engage in intricate relationships (Latour, 2005). And rather than focusing on individual self-regulation, co-regulated learning, with its’ collaborative dimension can be further explored as a regulation network that acknowledges the reciprocal influence among all actors, including AI (Bransen et al., 2022).

 
Our initial uncertainties gave way to improved understanding as we examined these and other frameworks for thinking about AI in education, combined with practically and collaboratively engaging with tools like Chat GPT. By the end of the course, student teams, including ours, reported higher confidence levels in using and making decisions with generative AI. We demonstrated our ability to analyze generative AI in various assignments and projects critically, which prepares us for navigating similar challenges and technological disruptions in our professional and academic careers.

Course Outcomes

The journey through our doctoral course, Current Issues in Virtuous Organizations, demonstrated the profound impact of PBL in demystifying generative artificial intelligence and maximizing our learning engagement. The course highlighted the potential of generative AI to challenge and enhance critical thinking and ethical considerations. So, too, the course empowered us and continues to empower students to have a voice in the ongoing dialogue surrounding generative AI in higher education (Snyder & Linerode, 2024).

One of the highlights of the Dream Team was the development of tangible resources with generative AI tools. The team created a practical training resource for IWU faculty aimed at helping educators better understand and implement generative AI. With excerpts illustrated in Figures 1-3, this training resource included an interactive Canva web page with text and visual content highlighting ethical standards, AI literacy, and key considerations about critical thinking in an AI era. The web page also included hyperlinks to curated educator resources, from sample guidelines and policies to free online courses and webinars. Overall, the project allowed students to apply their knowledge creatively and produce lasting value for the academic community.

          Figure 1. Training Resource Excerpt A (links disabled)



          Figure 2. Training Resource Excerpt B (links disabled)



          Figure 3. Training Resource Excerpt C (links disabled)

Recommendations

As we reflect on this journey, we offer practical next steps for educators aiming to replicate and build upon our success. Drawing from our experiences in the Current Issues in Virtuous Organizations course, here are five practical ways can help educators engage their students with generative AI:

     1. Invite Students to Decipher Convoluted Problems:

     2. Facilitate Comparative Studies with Generative AI Tools:

     3. Encourage Students to Curate and Explore Generative AI Resources:

     4. Develop Tangible Resources with Generative AI Tools:

     
5. Reflect and Document Their Feedback:

Conclusion

Our experience integrating generative AI into a problem-based learning (PBL) framework has demonstrated the transformative potential of this technology in higher education. Through hands-on engagement and collaborative inquiry, students transitioned from cautious observers to confident practitioners and innovators, equipped to navigate the complexities of AI in academia. The course not only demystified generative AI but also empowered students to critically analyze and creatively apply generative AI tools, fostering a deeper understanding and practical proficiency (Diaz, 2024; Snyder & Linerode, 2024).

This journey highlighted the importance of an adaptive curriculum that embraces emerging technologies while emphasizing ethical considerations and critical thinking (Henriques & Trajkovik, 2024). By co-creating knowledge, students are not merely passive recipients but active contributors to the evolving landscape of education. This approach prepares them to be leaders and innovators, capable of shaping the future of academia and beyond (Zailer & Barak-Medina, 2024).

As educators, we have a responsibility to harness the potential of generative AI to enhance learning experiences and prepare students for a rapidly changing world (Hrastinski et al., 2019). By fostering an environment of collaboration, inquiry, and ethical reflection, we can ensure that our teaching practices evolve alongside technological advancements (North, 2023). Let us continue to explore and embrace the opportunities presented by generative AI, ensuring that education remains dynamic, inclusive, and forward-thinking.
 

About the Authors

Rubya Ahmed

Lovette Coston

Linsey A. Hollingshead

Johnny H. Manson Jr.

Johnny H. Manson Jr., MSOL 
Doctor of Business Administration Student 
DeVoe School of Business, Technology, and Leadership
johnny.manson@myemail.indwes.edu 
 
Johnny H. Manson Jr. is a Doctor of Business Administration student at DeVoe School of Business, Technology, and Leadership at Indiana Wesleyan University - National & Global. His area of concentration in the IWU-DBA program is Management. Johnny received a Bachelor of Science in Business Administration from Martin University and his Master of Science in Organizational Leadership from Indiana Institute of Technology. Johnny plans to work as a full-time faculty member and business consultant. His professional strengths are organizational leadership, change management, business development, and curriculum and program design. Johnny is a lifelong resident of Indianapolis, IN, where he is an adult secondary education administrator, inspiring others to reach their academic goals and maximize their fullest potential.

Charles F. Swingle

Charles F. Swingle, MBA
Doctor of Business Administration Student
DeVoe School of Business, Technology, and Leadership
Chuck.swingle@myemail.indwes.edu

Charles F. Swingle is a Doctor of Business Administration student at the DeVoe School of Business, Technology, and Leadership at Indiana Wesleyan University - National & Global, with a focus on Management. As the owner and operator of a utility services business, Charles is pursuing his DBA emphasizing consulting to provide expert guidance within his industry. His prior work experiences were in wealth management as a registered independent advisor (RIA) and commercial property management. He holds an MBA Degree in Project Management from Louisiana State University – Shreveport and BBA Degree in Marketing from the University of Cincinnati.

Johngerlyn “Jonse” Young

Johngerlyn "Jonse" Young, MPNL, CAP
Doctor of Business Administration Student
DeVoe School of Business, Technology, and Leadership
Johngerlyn.young@myemail.indwes.edu

Johngerlyn Young is a Doctor of Business Administration student at DeVoe School of Business, Technology, and Leadership at Indiana Wesleyan University, focusing on Management. She is the Director of Philanthropic Services at Grand Rapids Community Foundation with over 30 years of experience in philanthropy and nonprofit administration. Her goal is to help organizations with organizational development and mission fulfillment. She holds an MS degree in Philanthropy and Nonprofit Leadership and a BS degree in Public Administration from Grand Valley State University, and she is a Chartered Advisor of Philanthropy (CAP).

Tiffany R. Snyder, Ph.D.

   
Tiffany R. Snyder, Ph.D.
Director of Faculty Enrichment
Indiana Wesleyan University
Tiffany.Snyder@indwes.edu
Connect on LinkedIn

Tiffany Snyder is the Director of Faculty Enrichment at Indiana Wesleyan University (IWU), overseeing the professional development of both full-time and adjunct faculty for the National & Global campus with a focus on hybrid and online programs. Dr. Snyder leads generative AI training and teaching, from facilitating professional learning communities, delivering conference and university presentations, teaching graduate courses and consulting on graduate research, and hosting on-site and online training for full-time and adjunct faculty. Dr. Snyder has been a co-host of the Digital2Learn podcast since 2018. She earned a Ph.D. in Psychology with an emphasis on the integration of technology and learning from Grand Canyon University, an MA Degree in Student Development Counseling and Administration and BS Degree in Psychology and Leadership from IWU.

 

References

Bengesi, S., El-Sayed, Hoda., Houkpati, Y., Irungu, J., Kamruzzaman, M., Oladunni,
          T. (2023). Advancements in Generative AI: A comprehensive review of GANs,
          GPT, autoencoders, diffusion model, and transformers. IEEE Access, Access,
          IEEE, 12, 69812–69837.  https://doi.org/10.48550/arXiv.2311.10242
Bransen, D., Govaerts, M. J. B., Panadero, E., Sluijsmans, D. M. A., Driessen, E.
          (2022). Putting self-regulated learning in context: Integrating self-, co-, and
          socially shared regulation of learning. Medical Education, 56(1), 29-36.
          https://doi.org/10.1111/medu.14566  
Colleague2Colleague. (n.d.). Colleague 2 Colleague (C2C) – Facilitating staff
          development and collaboration between all educational institutions’
          professionals in the Midwest. Retrieved July 10, 2024, from
          https://colleague2colleague.org/
Diaz, V. (Feb. 6, 2024). Exploring the opportunities and challenges with generative
          AI. https://er.educause.edu/articles/2024/2/exploring-the-opportunities-and-
          challenges-with-generative-ai   
Duch, B. J., Groh, S. E., Allen, D. E. (2001). The power of problem-based learning:
          A practical “how to” for teaching undergraduate courses in any discipline.
          Stylus Publishing, LLC.
EDUCAUSE. (2024a). AI Community Group. https://connect.educause.edu/community-
          home?CommunityKey=3e9c1d98-f63e-4ac4-9efd-0187b8b72c8a
EDUCAUSE. (2024b). Recommendations and Resources. https://www.educause.edu/
          recommendations-and-resources
Fawns, T. (2022). An entangled pedagogy: Looking beyond the pedagogy—technology
          dichotomy. Postdigital Science and Education, 4, 711–728.
          https://doi.org/10.1007/s42438-022-00302-7
Frontiers. (2023). Distance learning and face-to-face learning in a pathophysiology
          problem-based learning course during a pandemic. Frontiers in Education. 
          https://www.frontiersin.org/articles/10.3389/feduc.2023.1289526/full
Henriques, R. & Trajkovik, V. (2024). Generative AI for customizable learning
          experiences. Sustainability, 16(7), 3034. https://doi.org/10.3390/su16073034
Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises
          and implications for teaching and learning. Center for Curriculum Redesign.
          https://unesdoc.unesco.org/ark:/48223/pf0000386670
Hrastinski, S., Olofsson, A. D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G.,
          & Utterberg, M. (2019). Critical imaginaries and reflections on artificial intelligence
          and robots in post digital K-12 education. Post Digital Science and Education,
          1(2), 427–445. https://doi.org/10.1007/s42438-019-00046-x
Illinois Center for Innovation in Teaching and Learning (CITL). (2023). Problem-Based
          Learning (PBL). https://citl.illinois.edu/citl-101/teaching-learning/resources/
          teaching-strategies/problem-based-learning-(pbl)
Indiana Wesleyan University. (n.d.). Doctor of Business Administration Completion
          Program. https://www.indwes.edu/program/dba/completion/
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory.
          Oxford University Press: New York.
North, M. (September 27, 2023) Generative AI has disrupted education. Here's how
          it can be used for good – UNESCO. https://www.weforum.org/agenda/2023/
          09/generative-ai-educationunesco/#:~:text=URL%3A%20https%3A%2F%2
          Fwww.weforum.org%2Fagenda%2F2023%2F09%2Fgenerative
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez,
          M. (2023). Empowering education with generative artificial intelligence tools:
          Approach with an instructional design matrix. Sustainability, 15(15), 11524.
Singh, D., & Bashir, H. (2018). Effects of problem based learning and conventional
          learning on critical thinking ability of higher secondary school students in
          economics. International Journal of Education and Management Studies,
          8(1), 81-86.
Snyder, T. & Linerode, S. (2024). Online students reimagine education in an era
          of generative artificial intelligence. Empowered Educator Conference.
          Cengage. https://www.cengage.com/empowered-educator/spring2024/
Tan, S., Shen, Z. & Zhao, L. (2023). Design, implementation, assessment, and
          effectiveness of hybrid problem-based learning. Frontiers in Education.
          https://doi.org/10.3389/educ.2023.1199738
Zailer, G. & Barak-Medina, E. (2024). Strategies for integrating generative
          AI into higher education: Navigating challenges and leveraging
          opportunities. Education Sciences, 14(5), 503.
          https://doi.org/10.3390/educsci14050503