When the public talks about AI in the classroom, it鈥檚 often to fret over students who get 鈥渃hat鈥� to write their essays. But the AI revolution in education is happening on an institutional scale鈥攁nd it goes much deeper than homework shortcuts.
Tech companies are releasing lineups of AI-focused education devices, and school districts are buying them in bulk. Educators are reconfiguring their pedagogical practices, departments their curricula. When applied effectively, AI teaching tools have helped K鈥�12 teachers streamline workflows and personalize lesson plans to accommodate diverse learners. In higher education, faculty are seeing unprecedented course completion rates on AI-powered instruction platforms.
Yet there鈥檚 plenty of evidence that AI use weakens students鈥� critical thinking and research skills. Many educators believe that AI also dilutes human connection and the value of in-person learning, which has only just been revived post COVID-19.
So what鈥檚 the solution?
A Handbook for 鈥淗ealthy Skeptics鈥�
According to learning sciences faculty in the 茄子视频 Ruth S. Ammon College of Education and Health Sciences, there鈥檚 no one right answer.
鈥淭he most empowering thing we can do for educators right now is help them see that they don鈥檛 have to demonize or fetishize AI,鈥� said Elizabeth de Freitas, PhD, professor of learning sciences. But educators should also slow down and reflect. 鈥淩ather than simply leaping to a particular use of AI, we have to spend more time understanding the nature of the specific models we are using and how these come to us historically.鈥�
Dr. de Freitas and her Adelphi colleague Matthew Curinga, EdD, associate professor of learning sciences, co-edited (University of Minnesota Press, 2026), a new collection of 27 essays exploring links between machine learning and culture from scholars in media studies, philosophy, computer science, math, linguistics and education. The book serves as an interdisciplinary guide for educators trying to make sense of how human learning, and learning more generally, is being newly reconceived by AI.
鈥淭here鈥檚 a lot of polarization in the AI conversation. You鈥檙e either for or against it,鈥� Dr. Curinga said. 鈥淏ut the book is not 鈥榙o this, don鈥檛 do that.鈥� We鈥檙e providing the nuance that educators need to evaluate AI for themselves, to develop their own theories and philosophies, to become healthy skeptics of technology.鈥�
Beyond the Hype and the Headlines
For Dr. de Freitas, current large language models (LLMs) are another mode of language production and can be studied for how they develop fluency. Much like a human journalist might speak to sources and then draft a story, AI collects data from the internet and then generates hypothetical claims. And, much like a human journalist, AI models will exhibit bias and specific perspectives. It鈥檚 not a truth teller or sage, she says, nor is it a 鈥渂lack box鈥濃�攁 phenomenon that鈥檚 beyond the ken of anyone but computer scientists. 鈥淭he 27 essays in the book help readers understand the history of these models, as part of the history of computing more generally, and that鈥檚 empowering: 鈥淓ducators can help to demystify AI by understanding this history.鈥�
AI isn鈥檛 all-powerful, either. 鈥淲e can鈥檛 just read the headlines that tell us AI will replace a million jobs by 2050 and shrug our shoulders,鈥� Dr. Curinga said. He and Dr. de Freitas reject the concept of technological determinism, in which AI is an inevitable force driving its own future, impervious to human intervention. Instead, their work equips educators with the agency to keep learning firmly human-driven, even when it involves machines.
Research That Resists Easy Answers
Other learning sciences faculty are resisting the good-bad binary in their research and teaching.
Aaron Chia Yuan Hung, EdD, associate professor of education, who鈥檚 in the early stages of research on generative AI and learning, said his interest is 鈥渓ess on whether generative AI helps or hurts, but what kind of pedagogy has to be in place for it to help a specific group of learners.鈥�
鈥淪o far, research on AI and learning has been inconclusive. Dr. Hung notes that: 鈥淭here鈥檚 compelling evidence that heavy reliance on large language models carries a real cognitive cost.鈥� But other studies have shown positive results: an increase in creativity when brainstorming, an AI-powered tutor that鈥檚 engaging students more deeply than active instruction. 鈥淧ut together, these studies suggest that GenAI used as a substitute for effort has real costs, and that GenAI used as a well-designed instructional or creative tool can offer meaningful support.鈥� What a user is actually asking the tool to do, Dr. Hung says, is likely the most important variable.
Tracy Hogan, PhD, professor of learning sciences, is intrigued by the gendered dimensions of AI technology. Her research investigates the relationship between a user鈥檚 background and how they interact with chatbots鈥攕pecifically, if users with computer science experience are representing chatbots through a neutral gender lens, while less well-informed users are representing their bots through a specific gender identity. She鈥檚 brought these ideas into her Critical Literacy in Mathematics and Science Education class, where she works with students to explore inherent biases in AI input and output.
Training Tomorrow鈥檚 Educators for the AI Classroom
Whether producing their own research or referring to others鈥�, Adelphi faculty are applying evidence-based teaching methods in their classes, preparing a generation of educators to adapt to an ever-evolving AI landscape.
In the University鈥檚 Manhattan Center, students in the PhD in Learning Sciences program engage in complex machine learning scenarios in both the classroom and the center鈥檚 STEAM Innovation Lab. Though the STEAM lab is largely analog, Dr. Hogan encourages her students to play around with AI tools when designing and coding projects. Most of what these tools generate is incorrect or incomplete, so students are forced to draw on their own knowledge to critically examine AI鈥檚 output鈥攁 process that, in the end, reflects the STEAM program鈥檚 commitment to learning by experience and experimentation.
In Dr. Hung鈥檚 classroom, students are invited to cast light on the 鈥渂lack box鈥� of AI. Many of his students don鈥檛 actually know how it works, even though they use it regularly. For Dr. Hung, explaining the basics to students鈥斺�渨hy GenAI responses aren鈥檛 like a search engine鈥檚, why it鈥檚 prone to hallucination, why it鈥檚 suited to some tasks and not others, why it almost never says it doesn鈥檛 have an answer鈥濃�攊s easy enough. The real challenge, for him and other educators, is keeping up with the pace of innovation. 鈥淢ore than once in my first semester teaching this class, a model we鈥檇 just discussed got a significant update midsemester that changed the conversation,鈥� he said.
Although AI tools are imperfect, Adelphi learning sciences faculty aren鈥檛 opting out altogether. There鈥檚 much good that can be done with these tools, they believe, if applied rigorously and ethically. Most importantly, today鈥檚 students must be ready to lead the classrooms of tomorrow.
As Dr. Hung puts it, 鈥淥ur students鈥� future students won鈥檛 opt out of AI, and more workplaces will expect graduates to arrive with some baseline AI literacy. The risks are real enough to design around carefully, but the technology isn鈥檛 going anywhere, so pretending otherwise doesn鈥檛 serve our students.鈥�