By Jim Shimabukuro (assisted by Gemini)
Editor
In their article, “AI in Informal and Formal Education: A Historical Perspective,” published in the inaugural 2025 issue of AI-Enhanced Learning1, Glen Bull, N. Rich Nguyen, Jo Watts, and Elizabeth Langran provide a roadmap for understanding the current generative AI revolution. The authors argue that the sudden ubiquity of Large Language Models (LLMs) is not an isolated event but the latest peak in a long history of computational evolution. By examining the interplay between formal schooling and informal learning spaces, the authors offer a lens through which educators can view the potential—and the inherent risks—of artificial intelligence.
The thesis of the paper is that AI adoption in education is characterized by a persistent lag between informal innovation and formal institutional integration. The authors explain that while formal educational systems are naturally conservative and slow to change due to regulatory and curricular constraints, informal environments act as the primary catalysts for technological shifts. They argue that understanding this historical cycle is essential for navigating the “third wave” of AI, ensuring that pedagogical practices evolve alongside technological capabilities rather than merely reacting to them.
Supporting this thesis, the authors break down the history of AI into three distinct waves, providing a technical and social context for each. The first wave, often referred to as “Good Old-Fashioned AI” (GOFAI), relied on symbolic logic and “if-then” rules. In the formal classroom, this manifested as programmed instruction and early computer-assisted tutoring. However, these systems were rigid and limited.
The second wave, characterized by neural networks and machine learning, allowed computers to recognize patterns and “learn” from data, though this largely occurred behind the scenes in educational software.
The third wave—Generative AI—marks a radical departure because it democratizes the creation of content. The authors emphasize that this third wave has effectively collapsed the barrier between the user and the creator, placing powerful cognitive tools directly into the hands of students before schools have had the opportunity to develop formal guidelines.
Another key supporting point involves the “pioneer effect” in informal education. The paper details how early adopters—hobbyists, gamers, and self-directed learners—typically explore the boundaries of new technology long before it is vetted for the classroom. The authors note that the current tension regarding academic integrity and AI-generated essays is a modern iteration of past anxieties, such as the introduction of the hand-held calculator or the personal computer. They suggest that when formal education attempts to ban these tools, it inadvertently widens the gap between the skills students need for the real world and the tasks they are asked to perform in school.
This paper is important to the field of AI because it shifts the conversation from technological novelty to pedagogical sustainability. An insightful passages in the text highlights the shift in cognitive labor: “The current inflection point in artificial intelligence represents more than a technological shift; it is a cultural reorganization of how knowledge is acquired and validated.” This is a reminder to researchers that AI is not just a new “medium” for delivery, but a fundamental change in the epistemology of the classroom.
Furthermore, the authors provide a warning about the risks of institutional inertia. They state, “History suggests that informal learning environments frequently serve as the laboratories for the technologies that eventually redefine formal education; failure to observe these laboratories leaves formal systems perpetually behind the curve.” This observation is critical for policymakers who often view AI as a peripheral tool rather than a central force that is already reshaping the student experience outside of school hours. By recognizing that students are already living in an AI-integrated world, the paper argues that the “formal” sector must move from a defensive posture to a proactive one.
The paper’s significance is also rooted in its call for “AI literacy” for both teachers and students. The authors underscore the necessity of teacher preparation, noting that “the efficacy of AI in the classroom is ultimately bounded by the educator’s ability to orchestrate the interaction between the human and the machine.” This emphasizes that AI is not a replacement for the teacher but a sophisticated instrument that requires a new kind of professional expertise.
In conclusion, Bull, Nguyen, Watts, and Langran have produced a work that sets the current AI frenzy within a broader, more sober historical narrative. By identifying the historical patterns of technological adoption, the authors provide a framework for integrating Generative AI in a way that honors the strengths of formal education while embracing the agility of informal learning.
The approximate date of publication for this article is late September 20252, and it serves as a cornerstone text for the debut of the AI-Enhanced Learning journal and a vital resource for the coming decade of digital transformation in education. This article represents the culmination of research and historical analysis that the authors, particularly Glen Bull, have been developing within the SITE and AACE communities for several years.
An intriguing aspect of the article is its revisit of Robert Taylor’s classic 1980 framework in The Computer in the School: Tutor, Tool, Tutee3. The authors argue that while the “First Wave” of AI focused on the computer as a Tutor (delivering instructions), the “Third Wave” turns the computer into the ultimate Tutee, where the student must learn to “teach” or prompt the AI to get the desired output.
__________
1 AI-Enhanced Learning is an open-access, peer-reviewed journal on the research and practice of improving learning with AI.
2 Volume 1, Issue 1 of AI Enhanced Learning was effectively launched and made openly available in late September 2025, as evidenced by editor and contributor announcements dated September 22–24, 2025 that introduce and celebrate the “inaugural edition” and “Vol 1, number 1.” A subsequent post from the publisher’s organization then promotes Volume 1, Issue 1 as “just published,” reinforcing that the official publication and public availability of the complete inaugural issue occurred in that same late‑September 2025 window rather than earlier in the year. -Perplexity.ai
3 See the introduction.
Filed under: Uncategorized |
Educational Technology and Change Journal 
Leave a comment