Weathering waves of technology in the classroom

I've been using, developing, implementing, supporting, and critiquing educational technologies for longer than I care to admit. In that time, roughly once every decade, a major new technology emerged that was simultaneously cast as both the future and death of education. Computers in the 1980s. Multimedia and the Internet in the 1990s. "Web 2.0" in the 2000s. MOOCs in the 2010s. And now, generative AI (GenAI).

Each wave brought a paradigm shift in how we view teaching and learning. Each threatened established practices. Each presented serious challenges. And, eventually, each transformed from hyped revolution into diverse teaching practices enabled by thoughtful integration or rejection of these technologies.

These emerging technologies—branded as disruptive by vendors—each provided a fresh opportunity to refocus on intentional course design and constructive alignment as instructors are forced to clarify what their goals are in designing and teaching a course, on what (and how) students will learn, and on how success can be defined.

The current AI surge feels different, though that's likely because we're in the midst of it. 

Companies are developing GenAI products that they promise will modernize teaching and revolutionize learning. This is the same messaging that was used for computers, multimedia, the Internet, Web 2.0, and MOOCs. Disruption. Innovation. Personalization. Each time, these claims were partially true, yet the human core of teaching and learning—connections, collaboration, relationships, storytelling, reflection, practice, and role modelling—remained essential. 

My dissertation explored this relationship between technology and teaching through a novel lens: video games. By examining how the dimensions of player, performance, environment, narrative, and system interact in game design, I developed a framework that helps us understand the dynamic interplay in technology-rich teaching and learning environments. This "Teaching Game" framework reveals how modern teaching incorporates both structure and agency, with tensions between passive and active elements. When we apply this framework to generative AI, we see that these tools aren't simply replacing teaching or becoming tools for academic misconduct—they're creating new dynamics between students, instructors, content, and environments.

GenAI tools, based on Large Language Models (LLMs), are built by extracting content from across the internet—often without clear consent or compensation. This extraction-based origin demands caution. Yet their growing capabilities cannot be ignored, particularly as they become normalized in professional and academic settings.

I believe teaching and learning will remain fundamentally human and relational. 

Teachers won't be replaced by AI, nor will student learning be diminished by it—provided we embrace intentional course design and authentic assessment. This requires investing in faculty support, ensuring equitable access to high-quality tools, and fostering continued and open conversations about academic integrity.

The gameful approach to understanding teaching and technology reminds us that it's not just about the tools themselves, but how they shape the environment, pedagogies, roles, systems, and activities within our shared practices of teaching and learning. By maintaining this holistic perspective, we can better navigate each technological wave—including GenAI—with wisdom and purpose.

And to be prepared for the next major new technology's paradigm shift to come in the coming decade.