This AI Literacy Review covers the AI Rights Project’s amicus brief to a court on AI training and fair use, a theory-informed critique of AI literacy frameworks and power, Anthropic’s launch of Claude Academy, regulating AI in medicine, nursing and AI literacy, library programming on AI, an anti-AI librarian’s reflections on teaching an AI literacy course, UNICEF’s initiative turning child AI users into builders, Singapore’s “Four Learns” AI-in-education framework, MIT’s report on AI in teaching and research, Day of AI’s K-12 curriculum, more AI literacy programs and minors in universities, LA Times on AI classes filling up as computer science enrollment drops, Digital Moment grant for bilingual AI critical-thinking materials, Mathematica/Data Science 4 Everyone survey on data and AI literacy gaps, NFTE brief on AI literacy and entrepreneurship education, New Zealand report on AI and education in schools, AP and CNN coverage of youth AI safety, Purdue food science AI course, a call for student-led AI councils, arguments against bolting AI literacy onto first-year courses and for accountable AI literacy, a community college dean’s skepticism of employer AI literacy demands, an OECD AI literacy framework mapped to Australia’s digital curriculum, an AI-for-all undergraduate case study, AI Literacy Heptagon framework, framework for AI/data extracurricular programs, and AI literacy and student anxiety.
General
In AI Literacy: An Exercise in Power-Knowledge Brady D. Lund and Zoë Abbie Teel argue that AI literacy frameworks enforce a consumer orientation toward AI rather than one of agency and activity, with theoretical concepts from Foucault and Freire used to propose AI literacy as a critical practice that helps people use AI but also critically evaluate it, resist its assumptions, and govern it.
Anthropic launches Claude Academy to offer AI training on how to work with AI and how AI works, including information on their product Claude and more general learning about AI.
The AI Rights Project files an amicus brief in a court case regarding whether AI/machine training on copyrighted material constitutes fair use, and asks that the question of whether the extension of fair use to AI be examined not assumed.
Healthcare
In Why AI Literacy Isn’t Enough to Fix Chatbots: 6 Questions with Medical AI and Health Law Scholar Sofia Palmieri Vincent Joralemon from UC Berkeley Law talks with health law scholar Palmieri about advancements in medical research and why AI literacy may no longer be enough to prevent individuals from carrying the weight of responsibility of avoiding the problems with AI systems, and that taking a regulatory approach as in gambling may be a path forward.
In Artificial intelligence literacy and associations with thriving at work among nurses in Anhui Province, China: a latent profile analysis Zhenni Xie et al. seek to understand nurses’ AI literacy and its relationship with thriving at work and self-efficacy through a study of 1,000 nurses across 62 hospitals in China in January 2026.
In Artificial Intelligence Literacy, Critical Thinking Disposition, and Clinical Competence Among Nursing Students: A Cross-Sectional Study Yeowon Jeong et al. study the associations of AI literacy and critical thinking disposition with clinical competence among 200 nursing students in Korea.
In The Associations of AI Literacy, AI Self-Efficacy and AI Attitudes Among Nursing Students: A Cross-Sectional Path Analysis Shinhi Han et al. survey 100 nursing students in New York to determine whether AI literacy was positively associated with AI self-efficacy and AI attitudes, and suggest that nursing curricula could use more structured AI education.
In Exploring nursing students’ motivation for generative AI use: Academic libraries and AI literacy instruction [paywalled with snippets available] Kyoungsik Na and Yongsun Jeong look at the motivational factors that drive or dissuade nursing students from effectively engaging with GenAI.
Libraries
The Northern NY Library Network publishes a resource guide What’s going on with AI information literacy these days? based on a gathering and inquiry of library workers within the network.
The Free Library of Philadelphia hosts the “Avoiding AI: A Hands-on Workshop” as part of its Critical AI Literacy series to show people how to disable unwanted AI features.
Self-styled anti-AI librarian Eleanor Ball in All About My AI Literacy Course shares her reflections on running an AI literacy course–AI, Algorithms, and (Y)our Future, a one-credit elective about AI–noting that most students thought they knew more about AI than they did or felt lost in info overload, and that she chose learning objectives that didn’t require students to use AI, relying instead of readings or videos and structured small-group discussions.
Google’s granting arm grants $3 million to the Brooklyn Public Library, New York Public Library, and Queens Public Library to expand AI literacy programming for both staff and members of the public. The libraries plan to use the funding to expand training for staff and offer AI workshops for the public.
Education
UNICEF’s Tinkering with Tech: Equipping children with AI literacy looks at a hands-on initiative that empowers students, particularly girls, to shift from being a user of AI to a builder.
Singapore’s Ministry of Education includes a webpage on AI in education showing its ‘Four Learns’ approach: “for every student to Learn about AI, Learn to use AI, Learn with AI and most importantly, Learn beyond AI – to learn what AI is, how it works, what its risks and limitations are, how to use it safely and responsibly”. It includes video examples of what this looks like for different age groups.
MIT shares the final report of the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training that recommends the institution respond to Generative AI by creating AI-aware educational processes to prepare students for the world, center people and community, and build processes, teams, and tools for continuous improvement to keep up with the rate of change in AI.
Day of AI publishes a grade-by-grade K-12 AI literacy curriculum with a scope and sequence informational resource and implementation and planning guide. It also shares information about the Student Voices in AI program that is an in-school youth council program for middle and high school students that helps them guide how AI is used in their school.
The University of Louisiana at Lafayette’s College of Liberal Arts launches a Critical AI Studies minor to better prepare students for a career through an interdisciplinary minor with courses across business, IT, history, philosophy, psychology, and informatics.
Cornell University expands its AI Critical Literacy Program to all incoming students and faculty and staff after a successful pilot, with four modules in the Canvas learning management system: What is Generative AI?, Ethical Questions Surrounding Generative AI, GenAI and Learning; and Creating an AI Policy for Personal Use.
Penn State launches AI literacy resource “AI for Writing” that has been placed into an introductory writing course for first-year students to help students evaluate AI and apply Mike Caulfield’s SIFT Method, and develop effective writing practices when working with AI tools.
The LA Times’ Heather Hollingsworth’s article As computer science enrollments drop, artificial intelligence classes fill up shows how higher education is responding to the demand for AI literacy by offering AI minors targeted at students outside of computer science majors, and this comes at a time when enrollment in computer and information sciences are down over 8% from 2025, possibly as a result of software development job postings declining.
Digital Moment receives a Net Good by CIRA Grant to offer bilingual, unplugged learning materials designed to foster AI critical thinking skills for 600 students in grades 3-8 in Canada.
The report What Students Need to Thrive in a Data- and AI-Driven World: Perspectives from Parents and Educators by Grady Deacon, Kara Conroy, and Hannah Weissman covers findings from a survey by Mathematica and Data Science 4 Everyone of over 1,300 parents, teachers, and people in education in the US that show 88% agree that all students should be data literate before they graduate high school, but only 58% agree that schools are providing opportunities for students to develop data skills. And only 49% agreed that their school is effectively bringing AI tools into the classroom.
The Network for Teaching Entrepreneurship (NFTE)’s policy brief Entrepreneurship Education in the Age of Artificial Intelligence: How AI Literacy, Entrepreneurial Learning, and Human Skills Can Prepare Students for the Future of Work looks at how AI literacy can be combined with entrepreneurial learning so students develop both technical skills and durable human skills to leverage AI effectively, think critically about AI outputs, and navigate the changing workforce.
The Education Review Office of New Zealand publishes the report Ready or not: How are schools responding to Artificial Intelligence? which shows around 3 in 4 schools are in an unplanned category without guidance for teachers or students, without a policy for data privacy and security, and with teachers and leaders wanting clearer and more specific central guidance. One of the report’s recommendations is to support the developed of capability through targeted professional development.
News outlets AP and CNN cover the increasingly popular topic of AI literacy through articles on helping youth see the flaws in AI chatbots and staying safe amid the rise of nude deepfakes.
Institute of Food Technologists article Building AI Literacy by Purdue University’s Hanyu Chen discusses what she has learned from developing an AI Applications in Food Science and Industry course, including the need to teach fundamentals not tools, fit training to the job, and make data literacy nonnegotiable.
The University of Virginia student news outlet WUVA News’ Jack Larmoyeux has a video interview with Dean of Libraries Leo Lo about the AI Literacy and Action Lab and Lo’s efforts to prepare students for an AI-integrated future.
Rebecca Winthrop in Enhancing student agency through school AI councils calls for schools to establish student AI councils to enable students to lead conversations around AI use, review which AI tools are procured, test products and assignments, and lead AI literacy resource development.
In AI literacy cannot be bolted on to first-year courses Nicole Brownlie from the University of Southern Queensland discusses how clear, explicit, and specific guidance around AI use will help students engage in responsible AI use in the context of what they are learning in a course.
The Conversation piece Students need more than AI skills — they need accountable AI literacy by Sibo Chen from Toronto Metropolitan University defines accountable AI literacy as the ability to explain why a person used AI, assess what AI generated, and take responsibility for the resulting work, and discusses how students need enough knowledge about their subject to judge AI outputs.
Inside Higher Ed column Confessions of a Community College Dean features ‘AI Literacy’ To understand a technology, you need to know the problem it was meant to solve by Matt Reed, who questions what employers mean when they say they want AI literacy, and whether advancing one’s fluency in AI makes them much less fluent in everything else.
The Transform your Teaching podcast speakers Rob McDole and Jared Pyles reflect on definitions of AI literacy and how changes in AI literacy are affecting workforce literacy in episode 179 AI Literacy – Lessons Learned.
Coby Reynolds maps the OECD’s AI Literacy Framework to the Victorian Curriculum F–10 Version 2.0 Digital Literacy learning continuum in Australia and creates a free interactive AI literacy mapping resource with filters for year level and digital literacy elements.
The Association for Computing Machinery’s case study AI Literacy as Experimental Practice: Students as Investigators by Amarda Shehu et al. discusses how to teach AI literacy to undergraduate students across majors via a three-week midterm project in an AI-for-all course at George Mason University.
In The AI literacy heptagon: A structured approach to AI literacy in higher education Veronika Hackl et al. examine how AI literacy in higher education is defined and how it can be separated from related concepts like data and media literacy, and then develop an AI Literacy Heptagon with seven dimensions of AI literacy: technical, applicational, critical thinking, ethical, social, integrational, and legal.
In A scoping review of artificial intelligence literacy in secondary education Rahul Ghosh et al. cover peer-reviewed studies published 2020-2025 about AI in secondary schools, finding that research on AI literacy in this area is expanding but uneven, being concentrated in North America and East Asia; conceptualizations of AI literacy vary widely; and assessment practices are still underdeveloped and rely on self-reporting.
In Governing the unseen: A systematic review of AI literacy among language teachers in higher education Yanyao Deng et al. conducts a review of 32 studies between 2022 and 2026 on AI literacy in language teachers in higher education, finding that AI literacy is often conceptualised in terms of competency-based, multi-dimensional models, but critical and domain-specific dimensions are not well-developed, professional development is often unstructured and not well planned, and assessment is self-reported.
In Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs Jung-Sup Bae et al. explores how extracurricular programs in AI/data can complement formal coursework in higher education, and offer a 24-component framework as a concrete checklist for AI/data extracurricular program design and self-assessment.
In AI literacy and subject specialization in pre-service teacher education: a mixed-methods study of dimensional profiles and perceived pedagogical challenges Yu Hu and Lan Wang examine self-reported AI literacy and perceived pedagogical challenges among pre-service primary teachers in Central China, showing that math students reported higher scores than Chinese and English students.
In Developing and Validating an AI Literacy Scale for English Language Teachers: A Mixed-Methods Study Kuysin Tukhtaeva et al. validate an AI literacy scale designed for English language teachers using a five-factor model: Understanding AI in Education, Proficiency in Using AI, Pedagogical Alignment, Ethical Awareness, and AI for Feedback and Assessment.
In Nature article Empowerment or burden: promoting university students’ sense of gain in generative AI application based on the CAB-O framework Xiaojin Wang et al. survey 850 students from 14 universities in China about their reliance on AI support and find that AI literacy positively predicted both content‑based and experience‑based gain but also increased AI usage anxiety.
In Building AI Literacy Across the Curriculum: Integrating GeoAI and Big Data to Prepare Future Geospatial Leaders [paywalled] Jane Southworth et al. discuss the University of Florida’s AI Across the Curriculum initiative which includes structured programs, experiential learning, and cross-disciplinary collaboration.
