The debate about artificial intelligence (AI) in higher education often swings between two extremes: enthusiasm for transformation and fear of disruption. A more productive approach begins not with technology, but with learning. AI should be viewed not just as a driver of educational change but as a partner that supports evidence-based teaching and learning. As I often remind faculty, the guiding principle is simple: pedagogy first, technology second. This perspective was a central theme of my recent discussion on designing AI-enhanced learning activities.

Start with Learning Outcomes

Before selecting any AI platform, faculty should begin with what Grant Wiggins and Jay McTighe, in Understanding by Design, call backward design: starting from the destination rather than the tools available to get there. The question is not, “What can this technology do?” but rather, “What should students know and be able to do by the end of the course or program?” Learning objectives, assessments, and instructional activities should align with desired competencies.

This approach is consistent with constructivist learning theory, which suggests that learners actively construct knowledge through engagement and reflection. AI should therefore support—not replace—meaningful learning experiences. Assessment blueprinting, competency mapping, and intentional course design remain essential faculty competencies regardless of technological advances.

Promote Active Learning with Technological Tools

Technology-enhanced learning extends well beyond generative AI. Interactive simulations, virtual laboratories, audience-response systems, collaborative digital workspaces, and multimedia assignments all have the potential to improve engagement when thoughtfully designed.

In the faculty workshops I lead, the tools that actually change a classroom are the ones that promote active processing, retrieval/activation practice, and social learning. Platforms such as Poll Everywhere or Mentimeter transform learners from passive recipients of information into active collaborative participants. Simulations and virtual reality environments provide opportunities for authentic practice and experiential learning while reducing risk and cost.

Another particularly promising example is NotebookLM, which can transform course materials into interactive podcast experiences. Its value lies not in automation itself but in its ability to support learner engagement and repeated interactions with content.

How AI Can Support Faculty

AI also offers meaningful opportunities to increase efficiency and improve instructional design. Faculty can use AI to draft assessment questions, develop competency-aligned rubrics, create clinical cases, and produce discussion prompts that can then be refined through disciplinary expertise.

I think of these applications through the lens of what John Sweller called cognitive load theory — working memory is limited, so every minute a faculty member spends on routine drafting is capacity unavailable for the more complex tasks that their role actually requires. By reducing the time needed for routine administrative and content-development tasks, AI can free faculty to focus on higher-order activities such as coaching, feedback, mentoring, and designing authentic learning experiences. This allows AI to augment human expertise, not substitute for it.

Similarly, AI can function as a learning scaffold. Students can use AI-generated explanations, practice problems, and guided questioning to support understanding, provided they remain active participants in the learning process.

AI Risks Require Intentional Design

The greatest educational risk is not AI itself but its uncritical use. Excessive reliance on AI can result in cognitive offloading, where students outsource thinking rather than engage in it. To mitigate this risk, faculty should establish clear AI-use expectations and design assessments that require analysis, reflection, and human interaction.

Authentic assessment remains the strongest safeguard. AI cannot interview patients (at least for now), build community partnerships, navigate ethical dilemmas, or demonstrate empathy. Assignments grounded in real-world application and human engagement are inherently more resistant to misuse while simultaneously producing deeper learning.

Equity also deserves attention. Institutions should work to ensure that the students and communities they serve have fair access to AI platforms and are able to develop the AI literacy needed to evaluate outputs critically rather than accept them uncritically.

A Final Thought

My advice to faculty is simple: do not be afraid to experiment, but anchor that experimentation in learning science. Start with outcomes, design authentic assessments, and leverage instructional designers and faculty development resources. The goal is not to adopt the most technology. The goal is to create learning environments that foster competence, critical thinking, professional judgment and agency for life-long learning.

The educators who will succeed in an AI-enabled future will not be those who master every new platform. They will be those who continue asking the most important questions about learning, equity, integrity, and student success.

Disclosure: The technologies and platforms mentioned in this article are discussed as representative examples of tools currently available to educators. Their inclusion should not be interpreted as a recommendation or endorsement. The author has received no financial compensation or incentives from any company or vendor referenced.

Suggested References

Susan A. Ambrose, Michael W. Bridges, et al., “How Learning Works: Seven Research-Based Principles for Smart Teaching,” Jossey-Bass (2010).

Peter C. Brown, Henry L. Roediger, et al., “Make It Stick: The Science of Successful Learning,” Harvard University Press (2014).

Richard M. Ryan and Edward L. Deci, “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being,” American Psychologist Vol. 55, No. 1 (2000).

Richard E. Mayer, “Multimedia Learning,” Cambridge University Press (2021).

John Sweller, “Cognitive Load During Problem Solving: Effects on Learning,” Cognitive Science Vol. 12, No. 2 (1988).

Grant Wiggins and Jay McTighe, “Understanding by Design,” ASCD (2005).

Author:

Jayne S. Reuben, PhD, FAAPE, FASPET

Dr. Reuben is an Instructional Professor in Biomedical Sciences and Medical Education at the Texas A&M College of Dentistry (Texas A&M Dentistry) where she works to develop educational research opportunities for students and faculty. Skilled in pharmacology education, faculty development, the scholarship of teaching and learning, and educational assessment, she directs all of Texas A&M Dentistry’s undergraduate and graduate pharmacology courses and serves on the Faculty Development, Promotion and Tenure, Admissions and Curriculum committees.