Integrating Critical AI with Content Knowledge
New Mexico State University
The SITE 2026 Landscape
What’s the AI SIG Talking About?
I analyzed the paper titles and abstracts from across the SITE 2026 AI SIG submissions to map what our community is focused on this year. What themes dominate? What’s missing?
Explore the analysisThe Core Argument: Critical AI Engagement Is Discipline-Specific
Reading AI Like a Veterinarian, Evaluating AI Like an Artist: How Content Knowledge Shapes Critical AI Engagement
We tend to treat AI literacy as a generic skill — teach students to spot hallucinations, write good prompts, and they’re set. But what if the ability to critically evaluate AI is fundamentally shaped by what you know about your field? We analyze two cases: a veterinary science class where students caught an LLM recommending different treatments based on client income, and an art class where advanced students rejected AI-generated images because they didn’t match their creative vision. In both cases, it was disciplinary knowledge — not generic AI literacy — that powered the critique, and dialogue that made it meaningful.
“My Experience Differs”: A Simple Approach to Teaching Creative, Equity-Focused AI Collaboration
Most AI literacy instruction stops at “is this accurate?” But accuracy is only one dimension. This session introduces a five-category color-coding framework that helps students evaluate AI outputs for accuracy, completeness, context-dependency, personal experience, and generative thinking. The blue category — “this may be true for some, but my experience differs” — is where equity-conscious critique lives. Students learn to recognize when AI’s “universal” response erases important differences, and to position their own knowledge and lived experience as essential to the collaboration. See more about the theoretical base for this work here
Designing Teacher Education for the AI Era
AI in Teacher Education Across Contexts and Stakeholders
If AI integration in teacher education is going to be more than surface-level, we need to see the full landscape: who is affected, what competencies matter, and how we keep human connection at the center. This symposium brings together seven research perspectives spanning early childhood AI literacy, special education, equity frameworks, ethical analysis, teacher competencies, program redesign, and the social-emotional dimensions of AI integration — moving the field beyond fragmented conversations toward a more comprehensive view.
Teacher Education in the Era of AI: An Exploration of Tools and Research Directions That Support Technology Infusion
Technology infusion — the program-wide, program-deep integration of technology across coursework, field experiences, and assessment — has been guided by frameworks developed over two decades. But has AI disrupted those foundations? I examine TPACK’s relevance for AI integration, arguing that GenAI’s generative and social qualities demand an expanded emphasis on contextual knowledge. Co-panelists address teacher educator competencies and program design principles. Together, we explore whether these tools have withstood the test of time — and where they need to evolve.
Teachers Navigating AI in Practice
Teaching with GenAI: Teachers’ Evolving Role as They Integrate GenAI with Content Areas
The theoretical arguments and program designs matter — but what’s actually happening when teachers bring GenAI into their content areas? This brief paper draws from the same larger study as the full paper above, focusing on how teachers’ roles shifted as they moved from a summer AI workshop into classroom implementation. We examine the evolving knowledge teachers drew on — pedagogical, technological, content, and contextual — as they navigated the messiness of real integration with real students.