Balancing Promise and Pragmatism: A Cautiously Curious Approach to AI

By Gamze Yilmaz

As a pedagogical innovation practitioner and researcher in the field of communication, I've been recently consuming lots of content on the impact of AI in higher education. It appears that most insights that shape the universal narrative around AI and its impact in higher education come from AI education experts, learning specialists, instructional designers, EdTech professionals and pioneering leaders from well-funded research universities - all of them critical stakeholders in the discourse around AI in higher ed. I also see a small group of professors across various fields contributing to these discussions, primarily focusing on the opportunities AI presents, such as personalized learning or streamlining lesson prepping, as well as the challenges it introduces, such as potential over-reliance on technology and the ethical issues around the use of AI. One of the recurring themes in all these conversations is the necessity for transforming conventional teaching and assessment practices in higher ed in the face of AI.

In this discourse, I place myself in a group of professors I call the “cautiously curious” ones. We are excited and curious about implementing AI-enhanced pedagogies but also cautious about its applications in and impact on student learning and the practice of teaching. Having designed and implemented pedagogical innovations in my classes for years, I am both fascinated and apprehensive about integrating AI into the college classroom.

A significant argument about the disruptive impact of AI is that it will necessitate changes in our teaching practices. For instance, thought leaders encourage the faculty to adopt project-based learning, create assignments based on real-life applications, and focus on the process rather than the product in assessment. These are all very admirable goals, and I have long been a strong advocate and practitioner of pedagogical innovation in my own field and institution. However, I am also acutely aware of the challenges they pose, especially considering the educational philosophies students are immersed in from K-12 through college. In a system where students are not incentivized to value the experience of learning, the pedagogical transformation entailed by AI tools will be met with resistance from both professors and students.

Two important applications of AI-based pedagogical transformation are structuring courses around (1) active learning pedagogies and using (2) process oriented assessment to evaluate student learning. Active learning involves engaging students directly in the learning process through high-level cognitive activities and discussions, encouraging them to become active agents and critical thinkers in their learning process. I personally implement these strategies by using flipped classroom techniques and creative learning pedagogies based on design thinking in my courses. Process-oriented assessments focus on evaluating the learning process itself, emphasizing the development of skills and understanding over time rather than solely measuring final outcomes. Together, these approaches foster deeper comprehension of the course material and real life skill development in students.

As a professor driven by a deep passion for pedagogical innovation and creative learning, I continuously experiment with these methods in my classes, and I believe that experiences with these methods should be part of the AI conversations about pedagogical transformation. We should also discuss the difficulties of implementing these methods for professors with limited resources, guidance and large class sizes. For instance, the success of these practices depends heavily on extensive and systematic course preparation and equally laborious implementation by the professor. Imagine teaching three different courses with 30 or more students, now requiring you to mentor and guide 22 student teams on semester-long projects and active learning activities. Consider the time and effort needed on the professor’s side to design and implement (AI-resistant or AI-enhanced) active learning activities and to provide feedback on hands-on projects multiple times throughout the semester. Having implemented such practices in several different communication courses over the past six years, I understand the realities of these teaching methods. I love designing and delivering my courses around active learning pedagogies, but this approach is by no means an easy endeavor, and require so much extra time and effort by the professor.

For me, teaching courses using these methods is far more enjoyable, engaging, and meaningful than using conventional methods of lecturing and testing. Yet, another very critical factor in the success of such pedagogies is the student motivation and readiness. The impact of these innovative practices heavily depends on students’ motivation, effort, and course preparedness. As someone who teaches many non-traditional students with diverse and competing needs—students who work full-time jobs, care for their families, and manage other responsibilities—motivating and engaging individual students with active learning tools can be rather challenging at times, and also detrimental to the the learning experiences in a collaborative project.

Considering these realities, I believe more voices from professors who navigate these complexities daily need to join the discussion on AI in education. While AI holds great promise for enhancing learning experiences, it is crucial to approach its integration thoughtfully, considering both the opportunities and challenges it brings, especially for unique institutions and student populations. The path forward should involve a balanced perspective, drawing on the experiences and insights of professors who are eager to learn and practice AI-enhanced pedagogies, especially those in teaching institutions serving non-traditional students.