This site requires Javascript to be turned on. Please enable Javascript and reload the page.

C2C Digital Magazine (Spring - Summer 2024)

Hollingshead

This page is referenced by:

Exploring Generative AI through Problem-Based Learning and Student-Driven Inquiry
By Rubya Ahmed, Lovette Coston, Linsey A. Hollingshead, Johnny H. Manson Jr., Charles F. Swingle, Johngerlyn Young, and Tiffany R. Snyder, Indiana Wesleyan University
Imagine a classroom—on-site or online—where students learn about cutting-edge technologies and actively shape their practical applications through collaboration. This dynamic environment is especially intriguing in the context of generative artificial intelligence (AI) in higher education. Generative AI technology, which can create new content by learning from existing data, holds the potential to revolutionize teaching and learning, fostering a more interactive and personalized educational experience (Bengesi et al., 2023). These authors observed the benefits of generative AI integration and collaboration firsthand as students and educators.

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:
  * Activity: Present students with multifaceted generative AI problems that require
discerning the root cause and investigating the issues.

  * Example: Create complex scenarios related to current industry challenges, such as
ethical AI deployment, to stimulate critical analysis and discussion. For instance, present
a case where generative AI decisions lead to ethical dilemmas, prompting students to
explore solutions (Hrastinski et al., 2019; Holmes et al., 2021).

2. Facilitate Comparative Studies with Generative AI Tools:
  * Activity: Create assignments where students compare their work with generative AI
outputs, such as a literature review comparative study.

  * Example: Assign tasks to gather, evaluate, and present AI-related content from various
sources, requiring them to try out multiple tools. Encourage them to maintain a blog or
digital portfolio where they regularly post and reflect on AI developments and their
implications (Ruiz-Rojas et al., 2023).

3. Encourage Students to Curate and Explore Generative AI Resources:
  * Activity: Have students try out a diverse set of resources, including academic articles,
news releases, and multimedia content.

  * Example: Assign tasks to gather, evaluate, and present AI-related content from various
sources, requiring them to try out multiple tools. Encourage them to maintain a blog or
digital portfolio where they regularly post and reflect on AI developments and their
implications (Ruiz-Rojas et al., 2023).

4. Develop Tangible Resources with Generative AI Tools:
  * Activity: Guide students to create practical tools and resources, such as the training
resource for faculty developed in the course.

  * Recommendation: Allow creative freedom in developing resources like training
manuals, interactive courses, or instructional videos that can be used by educators to
understand better and teach AI concepts (AACSB, 2024).

5. Reflect and Document Their Feedback:
  * Activity: Maintain regular discussions and reflections on AI topics throughout the
course.

  * Recommendation: Use discussion forums, peer reviews, and synchronous sessions to
allow students to share their insights, challenges, and learning experiences, promoting a
collaborative learning environment. For example, organize weekly reflection sessions
where students discuss the latest AI trends and their classroom experiences with AI tools
(Crittenden et al., 2018).

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

Rubya Ahmed, MA
Doctor of Business Administration Student
DeVoe School of Business, Technology, and Leadership
Rubya.ahmed@myemail.indwes.edu

Rubya Ahmed is a Doctor of Business Administration student at Indiana Wesleyan University’s DeVoe School of Business, Technology, and Leadership. Her area of concentration is Management. Rubya is a passionate professor of Communications at Joliet Junior College (JJC), Joliet, Illinois, the first public community college in the United States, aiming to inspire learning, transform lives, and strengthen communities. Rubya strives to bring out the best potential in her students by maximizing their implementation of effective communication skills with a constructivist learning approach. She holds an MA Degree in Communication Studies from the Governors State University, and BA Degree in English Language & Linguistics with minor in English Language Teaching from American International University.
Lovette Coston

Lovette Coston, MBA, PAL
Doctor of Business Administration Student
DeVoe School of Business, Technology, and Leadership
Lovette.Coston@myemail.indwes.edu

Lovette Coston is a Doctor of Business Administration student at Indiana Wesleyan University with over 25 years of experience in IT leadership, including a certification as a Professional Agile Leader (PAL). Currently, she focuses on college and career work with adult high school students, leveraging her extensive industry background to guide and mentor them toward their academic and professional goals. Additionally, she is passionate about using her skills to help small nonprofit organizations be more sustainable and focuses on that within her doctoral research project.
Linsey A. Hollingshead

Linsey A. Hollingshead, MS, CESP
Doctor of Business Administration Student
DeVoe School of Business, Technology, and Leadership
Linsey.Hollingshead@myemail.indwes.edu

Linsey A. Hollingshead is a Doctor of Business Administration student at Indiana Wesleyan University’s DeVoe School of Business, Technology, and Leadership. Her area of concentration is Management. Linsey is the Director of Operations at PARTNERS in Employment, Inc., an organization that provides vocational rehabilitation services to individuals with disabilities. Linsey has served on the Development Committee for the national Association for People Supporting Employment First, is an active member in the Ohio Providers Resource Association and has earned the Certified Employment Specialist Professional (CESP) credential. Linsey strives to lead change in employment equality for those with disabilities. She received her MS Degree in Statistics from the University of Akron and BS Degree in Mathematics with minors in Business and Communications from the University of Toledo.
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