Artificial intelligence in mathematics education: The good, the bad, and the ugly

Integrating Artificial Intelligence [AI] into mathematics education offers promising advancements and potential pitfalls. Striking a balance between AI-driven developments and preserving core pedagogical principles is critical in the teaching and learning environment. AI has emerged as a transformative force in various fields, including education. In the realm of mathematics education, AI technologies offer a spectrum of potential benefits (including personalize instruction, adaptive assessment, interactive learning environments, and real-time feedback, among others) and challenges (such as lack of creativity and problem-solving skills, inability to explain reasoning, bias in data and algorithms, absence of emotional intelligence and data privacy and security concern etc). This conceptual study used autoethnography as the methodology and qualitative content approach to analyze data. The study discussed historical background of AI and considered ethical issues around AI. It was concluded that the journey to harness the full potential of AI in mathematics education requires careful navigation of the good, the bad, and the ugly aspects inherent in this technological evolution.

Terence Tao – Kepler, Newton, and the true nature of mathematical discovery

“And what those stories teach us about how AI will revolutionize math”

AI is not superhuman

What metaphor should drive the field of AI research?

AI That Evolves in the Wild | Edge.org

I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.

The Edge of Mathematics

Terence Tao, the legendary mathematician, explains the promise of generative AI.

What it Means to Be a Mathematician When AI Does the Math

Researchers debate motivation, purpose, and the field’s future

Mathematical methods and human thought in the age of AI

Artificial intelligence (AI) is the name popularly given to a broad spectrum of computer tools designed to perform increasingly complex cognitive tasks, including many that used to solely be the...

The AI Revolution in Math Has Arrived | Quanta Magazine

AI is being used to prove new results at a rapid pace. Mathematicians think this is just the beginning.

AI is doing something weird to Science

In both good and bad ways, and it won't go away.

Many Minds: Science, AI, and illusions of understanding

AI will fundamentally transform science. It will supercharge the research process, making it faster and more efficient and broader in scope. It will make scientists themselves vastly more productive, more objective, maybe more creative. It will make many human participants—and probably some human scientists—obsolete… Or at least these are some of the claims we are hearing these days. There is no question that various AI tools could radically reshape how science is done, and how much science is done. What we stand to gain in all this is pretty clear. What we stand to lose is less obvious, but no less important. My guest today is . Molly is a Professor in the Department of Psychology and the University Center for Human Values at Princeton University. In a recent , Molly and the anthropologist presented a framework for thinking about the different roles that are being imagined for AI in science. And they argue that, when we adopt AI in these ways, we become vulnerable to certain illusions. Here, Molly and I talk about four visions of AI in science that are currently circulating: AI as an Oracle, as a Surrogate, as a Quant, and as an Arbiter. We talk about the very real problems in the scientific process that AI promises to help us solve. We consider the ethics and challenges of using Large Language Models as experimental subjects. We talk about three illusions of understanding the crop up when we uncritically adopt AI into the research pipeline—an illusion that we understand more than we actually do; an illusion that we're covering a larger swath of a research space than we actually are; and the illusion that AI makes our work more objective. We also talk about how ideas from Science and Technology Studies (or STS) can help us make sense of this AI-driven transformation that, like it or not, is already upon us. Along the way Molly and I touch on: AI therapists and AI tutors, anthropomorphism, the culture and ideology of Silicon Valley, Amazon's Mechanical Turk, fMRI, objectivity, quantification, Molly's mid-career crisis, monocultures, and the squishy parts of human experience. Without further ado, on to my conversation with Dr. Molly Crockett. Enjoy!   A transcript of this episode is available .   Notes and links 5:00 – For more on LLMs—and the question of whether we understand how they work—see our with Murray Shanahan. 9:00 – For the paper by Dr. Crockett and colleagues about the social/behavioral sciences and the COVID-19 pandemic, see .  11:30 – For Dr. Crockett and colleagues’ work on outrage on social media, see this .  18:00 – For a recent exchange on the prospects of using LLMs in scientific peer review, see . 20:30 – Donna Haraway’s essay, 'Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective’, is . See also Dr. Haraway's book, . 22:00 – For the recent essay by Henry Farrell and others on AI as a cultural technology, see . 23:00 – For a recent report on chatbots driving people to mental health crises, see . 25:30 – For the already-classic “stochastic parrots” article, see . 33:00 – For the study by Ryan Carlson and Dr. Crockett on using crowd-workers to study altruism, see . 34:00 – For more on the “illusion of explanatory depth,” see with Tania Lombrozo. 53:00 – For more about Ohio State’s plans to incorporate AI in the classroom, see . For a recent essay by Dr. Crockett on the idea of “techno-optimism,” see .   Recommendations , by Adam Becker , by L. A. Paul , by Miranda Fricker   Many Minds is a project of the , which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by , with help from Assistant Producer and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by . Our transcripts are created by . Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter ! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com.  For updates about the show, visit or follow us on Twitter () or Bluesky ().

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

Mathematicians are grappling with the possibility that AI might eclipse them

I talked to 20 mathematicians about rapid AI progress in their field.

"AI" is Automated Inequality

Tech bros still dominate the discussions about so-called "AI" with false claims. Even most "AI"-critical researchers spend much of their time meticulously debunking (always only a subset of) claims, leaving vast areas of the economic consequences of "AI" unexplored. (Even the "AI"-evangelist Economi

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The philosophical perils of conflating discoveries and inventions, and how to overcome them

From Genius Science to Scenius Science - Cosmik Labs

Why personal AI tools might not be all we need to revolutionize science