This AI Literacy Review covers the Pope’s encyclical letter discussing AI, Microsoft’s 2026 Work Trend Index, Strada Institute’s report on AI literacy and entry-level hiring, launch of the Sustainable AI Group, US Air Force’s plan to train every Airman on AI, Malta and OpenAI’s national AI for All programme, Penn Nursing leaders on AI’s growing role in patient care, AI literacy in healthcare and health education, Thomson Reuters 2026 Law Student Pulse Survey, Gallup and Pearson and AWS reports, India’s AI literacy for teachers, Google and ISTE+ASCD’s AI Educator Series, HEPI study of 96 UK university AI policies, library-led AI literacy support, Harvard educators on ‘never-skilling’ problem, AI literacy for researchers, a four-domain AI literacy model for higher education, comparison of AI literacy frameworks, AI literacy in the English classroom, why AI literacy must be discipline-specific, a UC Davis Library intern building AI literacy resources, AI literacy across the curriculum, and more.
General
Pope Leo XIV’s encyclical letter Magnifica Humanitas contains a chapter on Technology and Dominance: The Grandeur of Humanity in Light of the Promises of AI that addresses concerns about technological progress and calls AI a valuable tool that requires vigilance and should be open to discussion and debate. He also touches on the importance of access and education and participation in development and decision-making: “To speak of the universal destination of goods means finding ways of ensuring universal access to both technologies and the education needed to use them. To speak of subsidiarity calls for protecting the ability of communities to make choices and corrections, rather than confining their role to mere oversight after the standards have been set elsewhere.” (section 109).
Microsoft’s 2026 Work Trend Index Annual Report: Agents, human agency, and the opportunity for every organization shows that many people feel either stalled, blocked, or not yet able to take advantage of AI at work, 65% of AI users have a fear of falling behind if they don’t use AI to adapt, but 45% feel safer focusing on current goals rather than redesign work with AI.
Strada Institute for the Future of Work’s report Entry-Level Hiring in the AI Era What Employers Are Thinking (and Doing) surveyed almost 1,500 executives and leaders in the US and found that employers rate AI literacy as the least important skill evaluated for entry-level college graduate hires, with critical thinking, communication, and collaboration ranked as most important. Work experience (non-internship) is also considered more important than a degree.
AI sustainability leaders Sasha Luccioni and Boris Gamazaychikov launch the Sustainable AI Group to address the environmental aspects of AI and offer benchmarks such as the AI Energy Score to measure AI model energy use.
In Agentic literacy debt: a structural problem the AI literacy field has not yet named Rohith Nama argues that existing AI literacy frameworks were built for a old world of AI outputs needing human evaluation, not AI with decision-making authority, and that AI literacy must be reframed as a governance capability rather than an evaluative capability.
Government
US Senators Adam Schiff and Mike Rounds introduce the Literacy in Future Technologies (LIFT) Artificial Intelligence Act, which would authorize higher education or nonprofit organizations to be awarded funds for K-12 AI literacy curriculum development, teacher professional development, and evaluation methods, with OpenAI, Google, Microsoft, HP, American Federation of Teachers, and others endorsing the measure.
Congressperson Randy Fine introduces the K–12 AI Literacy and Readiness Act of 2026 to amend the Elementary and Secondary Education Act to explicitly allow federal funds for student instruction on AI and professional development for teachers and educational staff to use and teach AI.
The EU Council approve conclusions calling for a human-centred approach to AI in education that centers teachers as guides and critical thinkers, calls on member states to build educators’ AI and digital skills, and marks the first time AI-teaching relationships have been addressed in EU education policy.
OpenAI and the Government of Malta announce AI for All, a program giving all Maltese citizens ChatGPT Plus for a year after completing an AI literacy course developed by the University of Malta that covers what AI is, what it can do, and how to use it responsibly.
The US Air Force plans to train every Airman on AI as part of a service-wide AI strategy to create a baseline level of AI literacy throughout the force and take advantage of AI.
Healthcare
In Penn Nursing Leaders Speak Out on AI’s Growing Role in Patient Care, University of Pennsylvania School of Nursing leaders George Demiris, Antonia Villarruel, and Connie Ulrich discuss the potential in AI and that nurses need AI literacy to understand the capabilities and limitations of AI systems, including bias, accuracy, and privacy and not to trust them completely.
In AI literacy as a potential mediator between attitude and self-efficacy among PICU nurses: a cross-sectional study Yu Liu et al. studied 221 registered nurses and found a significant direct effect of AI attitude on self-efficacy and that AI literacy had a notable mediating role.
in AI and Algorithmic Literacy Among Health Workers: A Scoping Review Through a Digital Health Literacy Lens,Ihoghosa Iyamu et al. examine how AI and algorithmic literacy among health workers is underdeveloped and inconsistently measured, and often overlooks communicative competencies important to clinical practice. They also suggest that workforce development needs to include AI and algorithmic literacy rather than it being left to individual health workers to manage.
Beyond ‘Check the Source’: Information Literacy for Health Decisions in the Age of AI by Elaine Kong argues that the traditional “check the source” framework is insufficient in a complex information environment including social media and AI-mediated information, and proposes that AI literacy must be a core competency and information literacy’s future needs to be both individual and structural.
Filippe Oliveira from Monash University’s piece AI Is Everywhere in Health Education—the Hype Is Outrunning the Evidence documents how Generative AI is part of everyday health education but there is limited evidence for some of the claims being made about its usefulness, and calls for AI to be held to the same standards of evaluation as other things in health and medicine.
Law
The 2026 Law Student Pulse Survey from Thomson Reuters finds that 72% of 1,874 US law students identify AI literacy as an essential professional skill while 74% worry that over-reliance will undermine development of core legal competencies, yet 32% say their school is not giving them the AI skills they need and 48% report AI policies vary by professor with no institutional coherence.
Education
K-12 Dive’s Naaz Modan reports on a Gallup and Walton Family Foundation survey of 2,000 public school teachers that finds over 60% of teachers received no guidance on applying AI to their jobs, nearly 60% lacked guidance on using AI for grading or feedback, and under 50% received guidance on using AI to create assignments or class materials. Teachers said that much of the AI guidance they receive is informal, and teachers in higher-needs schools were less likely to receive guidance than those in wealthier schools.
Pearson and AWS Global Research’s report AI Readiness: Building the Bridge from Higher Education to Work surveys 2,700 learners, higher education leaders, and employers in six countries, finding that 53% of employers say their main challenge is finding graduates with the right AI skills and only 14% of graduates say they have a high level of proficiency in applying AI tools to a professional workflow. The report calls out four key capabilities and skill competencies for the optimal AI-ready graduate, including ability to use AI tools effectively and ability to identify where AI can create value.
An EdWeek Research Center survey of teachers, reported in More Schools Are Providing AI Training for Teachers—Is It Any Good?, finds that 42% of teachers have received no training on using Generative AI, 22% have had multiple training sessions, and 9% have ongoing training. Eagerness to learn about integrating AI into teaching practice was mixed, with 63% somewhat or very eager, 24% slightly eager, and 13% not at all eager.
The Computer Science Teachers Association’s “AI PD Weeks” initiative will provide foundational computer science and AI skills for teachers in K-12 classrooms through hands-on learning and practical strategies, funded by $11 million from the U.S. National Science Foundation.
In India, Bodhan AI at IIT Madras announces the launch of the AI Literacy for Teachers program which aims to train over one million teachers in AI for teaching by 2027 through focusing on lesson planning, content creation, and multilingual delivery and evaluation, with the first public cohort launching on Teachers’ Day, September 5.
Google and ISTE+ASCD launch the Google AI Educator Series with free bite-size training for educators to build an AI literacy learning journey with a badge for completion, with the series covering core AI concepts, critical thinking skills, and responsible use.
Sam Illingworth’s Higher Education Policy Institute (HEPI) report What UK University AI Policies Actually Do: A Study of 96 Institutions finds that 41% of UK institutions have no publicly accessible AI policy, most existing policies use the language of learning while functioning as compliance frameworks, and that where the policy sits predicts function more so than the language used in the policy. The report identifies four exemplars that genuinely extend trust and develop critical AI literacy through assessment design not detection.
In AI literacy is the bridge between fear and the graduates we need, Australia’s first pro vice-chancellor for AI, Phil Laufenberg discusses how he sees AI literacy as a core graduate capability and that universities need to move away from prohibition and toward ensuring their graduates have built capability with AI before moving into the job market.
Build AI and Information Literacy Through Targeted Library Support by University of Exeter staff Amy McEwan, Isobel Eddyshaw, and Jenny McGarvey details their efforts to deliver AI workshops and provide online resources for students, to co-create AI literacy support with students to reduce anxiety, and to develop AI literacy alongside library and study skills.
The Harvard Gazette’s ‘Deskilling’ Is Bad. This Is Worse. by Liz Mineo covers a conversation by Harvard Education Press featuring educators Stephanie Smith Budhai and Marie Heath who wrote “Critical AI in K–12 Classrooms” who use the concept “never-skilling” to describe students who never acquire foundational skills because they use AI for everything from the start, and recommend critical AI literacy is needed in teacher education programs.
In A Conversation with Lance Eaton: Reimagining the Liberal Arts in the Age of AI Stefan Bauschard and Anand Rao discuss with Eaton the state of higher education and how faculty resistance to AI is an act of privilege, and that abstinence is not the path forward but instead radical engagement and real-time debate. Eaton draws on history to call out that previous era tech bros (such as Carnegie) provided a lot of the structure of education (e.g. meeting in 50-minute chunk through a semester of 15 weeks) and that it is not grounded in how people learn, and that faculty effort and discomfort will be needed to meet students where they are at and which tools are embedded into their devices.
Jessica Parker and Kimberly P. Becker in Defining and Assessing AI Literacy for Researchers Across the Research Lifecycle argue that existing AI literacy frameworks focus on students or general citizens rather than researchers as knowledge producers, and propose a new capability map that defines AI literacy across functional, critical, and rhetorical dimensions and across different stages of the research lifecycle.
In AI Literacy as a Meta-Skill: A Four-Domain Model for Academic Management Innovation in Higher Education Chi Che tests a four-domain AI literacy model—Engaging, Creating, Designing, and Managing—in Chinese private higher education, finding that all four domains significantly predict innovation outcomes and that AI literacy is a strategic and transferable capability.
In AI Literacy as Both Bridge and Buffer: Unraveling Its Dual Role Between Research Stressors and Teaching Excellence Guimei Yang et al. survey 253 faculty from Chinese universities about AI literacy and its impact on research and teaching tensions, and reframe AI literacy as more than merely a technical skill and something that may help advance student-centered education.
In Six Global Frameworks for Human-Centred AI Literacy and Competency: Comparative Analysis and a Way Forward Thomas K. F. Chiu and Pericles ‘asher’ Rospigliosib compare frameworks from UNESCO, the OECD/European Commission, Australia, China, UK, and US, finding broad consensus on human-centric principles but differences in audience types. They introduce the Human-Centric Artificial Intelligence Pedagogy (HCAP) framework to help educators develop these competencies in interactive learning environments.
Why the Middle Path of AI Literacy May Be the Future of English Class by David Nurenberg discusses his experience bringing AI into his 10th and 11th grade English classes and asking students to apply critical thinking skills and compare AI output to their own to develop critical AI literacy.
In Why AI Literacy Must Be Discipline-Specific Rose Luckin writes about the downsides of generic AI literacy preparation and suggests that AI guidance needs to take into account different disciplinary needs, for example in STEM, humanities, and professional fields. Luckin recommends five practical steps for institutions, including designing AI guidance at a module level, assessing explanation of AI use instead of declaration, aligning AI guidance with disciplinary purposes, coordinating expectations across teaching teams, and addressing unequal access to AI tools.
The Center for Democracy and Technology’s Advancing Responsible AI Adoption and Use in K-12 Education: Three Policy Priorities for State Legislation outlines the need for risk management of student data privacy and rights, AI governance to keep edtech vendors accountable, transparency about the purposes and guardrails in AI tools, and consideration of responsibility and already-existing legislation to avoid conflict.
In My Adventures in Teaching AI Fluency to College Students: A Case for Design Literacy librarian Kyle Bylin reflects on being embedded into the first AI fluency class at Saginaw Valley State University and navigating the information literacy and critical thinking part of working with AI and helping students consider the feasibility and usability of coding projects when the boundaries of AI tools are still often unknown.
In Thinking Less, Trusting More: GenAI’s Impacts on Students’ Cognitive Habits Rudrajit Choudhuri et al. note that they agree with the importance of AI literacy skills but find that students’ awareness of AI’s fallibility does not counteract their incentives to offload cognitive work to Generative AI and. Choudhuri commented in an interview that “Our findings make you rethink what kind of AI literacy is useful after all.”
In A Framework for institutional change in the age of AI David Perl-Nussbaum and Noah D. Finkelstein propose a framework with six dimensions for reconsidering prior change models in the age of AI and supporting institutions under conditions of uncertainty, and they show how the framework could be applied to a university physics department.
UC Davis Library intern Rayan Mansoor is using his computer engineering background to develop AI literacy resources for students so the tech feels more grounded and approachable, with a “Human x Machine” workshop offering a low-stakes place for participants to share their thoughts about AI and upcoming Canvas modules able to be imported by instructors into their courses.
In I Was a University AI Czar. I’m Not Equipped to Teach in the Age of AI. Josh Gellers reflects on serving as the Inaugural Faculty Fellow for AI at the University of North Florida and how throughout his work on a free AI certificate program for 50k people from around the world, an AI minor, a task force, public speaker, and advisor, he realized how many educators lack the time and energy to redesign their classes and end up in what he calls the Exhausted Majority, as opposed to the AI Enthusiasts or AI Resisters.
In Not the App. Not the Ban. Train the Teacher. Nik Bear Brown argues for the research-backed concept of investing more money in professional development and coaching for teachers than for Edtech tools, and to train teachers in AI through sustained, subject-specific, and grade-level-specific training.
Matthew Wemyss in We Keep Saying “AI Literacy Across the Curriculum”. Where Are the Mechanisms? points to the lack of infrastructure to support the goal of AI literacy in different disciplines, the downsides of generic AI training, and the need for training to begin where the subject lives and be tailored to the year and the subject.
In Why AI Fluency, Not Literacy, Is The Differentiator: 4 Moves For Higher Ed Aviva Legatt suggests four ideas for deans in higher education to prepare students for the workforce, including building evaluable artifacts into the degree and embedding AI use and evaluation into judgement-heavy courses.
In Your Brain on AI: Cognitive Offloading, Debt, and Atrophy Joe Pierre M.D. examines evidence that AI usage in education may be harming students and critical thinking, arguing that AI literacy must cover how AI chatbots work, their potential for hallucinations, and their risk of harms.
Chad Mairn shares an AI Literacy For College Students web tutorial with no login required and six interactive modules, designed following other university AI literacy programs.
Phillip Alcock shares some of his most popular AI fluency projects that aim to make students’ thinking visible, such as asking students to improve a weak prompt or spotting where AI gets something wrong and correcting it.
