Learning and Teaching with AI
Forsyth, Rachel LU
(2025)
In Change: The Magazine of Higher Learning
2025(3).
p.40-47
- Abstract
- According to Ethan Mollick, artificial intelligence (AI) is the most recent general purpose technology (GPT), the “once-in-a-generation technologies, like steam power or the internet, that touch every industry and every aspect of life” (2024, p. xv). Unlike the slowly unfolding transformations caused by these earlier GPTs, the changes sparked by AI seem to evolve daily. This rapid pace creates a sense of urgency that is reinforced by corporate and media promoters of AI—the revolution is now, don’t be left behind! However, Mollick cautions, “No one really knows where this is all heading, including me” (p. xviii).
That truth has not prevented a mushrooming literature including dozens of books, piles of articles, and scores of... (More) - According to Ethan Mollick, artificial intelligence (AI) is the most recent general purpose technology (GPT), the “once-in-a-generation technologies, like steam power or the internet, that touch every industry and every aspect of life” (2024, p. xv). Unlike the slowly unfolding transformations caused by these earlier GPTs, the changes sparked by AI seem to evolve daily. This rapid pace creates a sense of urgency that is reinforced by corporate and media promoters of AI—the revolution is now, don’t be left behind! However, Mollick cautions, “No one really knows where this is all heading, including me” (p. xviii).
That truth has not prevented a mushrooming literature including dozens of books, piles of articles, and scores of Substacks and podcasts. This article examines a small slice of that, asking, what does the rise of AI mean for teaching and learning in higher education?
One helpful framework, not in the AI literature reviewed here, is from Manu Kapur, a professor of learning sciences and higher education. Kapur (Citation2016) has a four-quadrant conception of learning experiences that, for our purposes, distinguishes between “productive failure” and “unproductive success” in teaching and learning. In the first, even though students “generate suboptimal or even incorrect solutions” to a problem, their failure prepares them “to learn better” in the short and long term (pp. 289–290). On the other hand, “unproductive success” occurs in an educational experience when students “maximize performance in the shorter term without maximizing learning in the longer term,” creating “an illusion of learning” (p. 290).
Although Kapur wrote nearly a decade ago, this distinction frames a—perhaps the—central challenge for educators and educational institutions posed by the recent rise of AI. How can we help students do the hard, and often frustrating, work of learning when, all around them, AI’s champions are promising quick and easy answers to every question? The illusion of learning is only one click away. Why take the time and make the effort—and pay the tuition—necessary to develop deep knowledge and expertise? (Less)
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- author
- Forsyth, Rachel
LU
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Change: The Magazine of Higher Learning
- volume
- 2025
- issue
- 3
- pages
- 40 - 47
- publisher
- Taylor & Francis
- ISSN
- 0009-1383
- DOI
- 10.1080/00091383.2025.2496117
- language
- English
- LU publication?
- yes
- id
- f288acb6-2868-4e13-8311-3a547547db4c
- date added to LUP
- 2026-02-12 10:21:39
- date last changed
- 2026-02-24 03:54:36
@article{f288acb6-2868-4e13-8311-3a547547db4c,
abstract = {{According to Ethan Mollick, artificial intelligence (AI) is the most recent general purpose technology (GPT), the “once-in-a-generation technologies, like steam power or the internet, that touch every industry and every aspect of life” (2024, p. xv). Unlike the slowly unfolding transformations caused by these earlier GPTs, the changes sparked by AI seem to evolve daily. This rapid pace creates a sense of urgency that is reinforced by corporate and media promoters of AI—the revolution is now, don’t be left behind! However, Mollick cautions, “No one really knows where this is all heading, including me” (p. xviii).<br/><br/>That truth has not prevented a mushrooming literature including dozens of books, piles of articles, and scores of Substacks and podcasts. This article examines a small slice of that, asking, what does the rise of AI mean for teaching and learning in higher education?<br/><br/>One helpful framework, not in the AI literature reviewed here, is from Manu Kapur, a professor of learning sciences and higher education. Kapur (Citation2016) has a four-quadrant conception of learning experiences that, for our purposes, distinguishes between “productive failure” and “unproductive success” in teaching and learning. In the first, even though students “generate suboptimal or even incorrect solutions” to a problem, their failure prepares them “to learn better” in the short and long term (pp. 289–290). On the other hand, “unproductive success” occurs in an educational experience when students “maximize performance in the shorter term without maximizing learning in the longer term,” creating “an illusion of learning” (p. 290).<br/><br/>Although Kapur wrote nearly a decade ago, this distinction frames a—perhaps the—central challenge for educators and educational institutions posed by the recent rise of AI. How can we help students do the hard, and often frustrating, work of learning when, all around them, AI’s champions are promising quick and easy answers to every question? The illusion of learning is only one click away. Why take the time and make the effort—and pay the tuition—necessary to develop deep knowledge and expertise?}},
author = {{Forsyth, Rachel}},
issn = {{0009-1383}},
language = {{eng}},
number = {{3}},
pages = {{40--47}},
publisher = {{Taylor & Francis}},
series = {{Change: The Magazine of Higher Learning}},
title = {{Learning and Teaching with AI}},
url = {{http://dx.doi.org/10.1080/00091383.2025.2496117}},
doi = {{10.1080/00091383.2025.2496117}},
volume = {{2025}},
year = {{2025}},
}