AI's impact and your discipline

Understanding AI's Impact on Education

We've explored how to teach and assess AI fluency. Now, let's take a step back to get a better understanding of something that's on all of our minds: How is AI changing what we teach, and what do we do about it?

This video examines how AI disrupts our specific disciplines and how we might respond. The next video will explore embedding your subject expertise into the "4Ds." Together, they will help you effectively integrate AI fluency with your broader curriculum while staying true to what matters in your field.

AI fluency isn't about automating away your expertise. It's about amplifying what we know and do best by augmenting our capabilities, knowledge, insights, and judgment through thoughtful partnership. Our disciplinary knowledge doesn't become obsolete; it becomes the foundation for unprecedented achievement. This is the message we must embody and help our students understand.

Preparing Graduates for an AI-Driven Future

Let's consider for a moment what our graduates will face:

• What gets automated? Which routine tasks in their future careers are likely to be automated by AI? And what does that mean for what we teach today and how we teach it?
• Where is the potential partnership? Where will human-AI collaboration be most impactful? These may become priority areas for developing both domain expertise and AI fluency together.
• Who is in charge? When AI does work independently in their field, how do students manage and remain accountable for what it does? How can we prepare them for this today?

AI creates disruption—both positive and negative—in every discipline across our curricula, pedagogy, and assessment practices. But the disruption isn't uniform. For example, maybe your curriculum stays stable while your assessments are transformed. Or your pedagogy might evolve while your core curriculum stays constant. Some disruptions are opportunities to be leveraged; others are problems to solve. Understanding which is which is where your expertise comes in.

Leveraging Your Professional Expertise

Your response to AI disruption builds on three types of expertise you already have:

1. Disciplinary Expertise: You understand not just your field's content, but its core values, methods, and ways of thinking. You know which skills are foundational versus peripheral, which concepts unlock future learning, and what makes someone truly fluent in your discipline.
2. Pedagogical Expertise: You know where students in your field typically struggle, achieve breakthroughs, and develop mastery. You understand the emotional journey of learning your subject—where students resist, where they get excited, and where they need support.
3. Assessment Expertise: You know how to recognize genuine understanding when you see it. You can design evaluations that reveal deep learning, whether AI is involved or not.

AI and the Curriculum

AI doesn't replace disciplinary knowledge, but it does challenge us to reconsider what we teach, when, how, and why we teach it. It forces us to ask hard questions: What does domain mastery mean in an AI context? How do we help students develop the judgment, creativity, and deep personal understanding to recognize when AI generates quality content or is genuinely helpful to them?

The fundamentals stay fundamentals, but students will question why they need this knowledge at all. They might ask: "Why learn research methods when AI can search for me?" or "Why study writing when AI generates text?" We need compelling, discipline-specific answers that connect deep learning to enhanced capability and the students' own goals.

Take time to work with your colleagues to deeply assess AI's impact on your curriculum:

• Identify what in your discipline AI systems are capable of automating.
• Determine which foundational concepts become more or less important when AI handles routine tasks.
• Identify how AI augmentation changes and can improve best practices in your discipline.
• Determine how to help students direct AI agents effectively.

Transforming Pedagogy and Learning

AI will also transform how we teach and how our students learn. Personalized tutoring at scale, interactive simulations, and immediate feedback—things we dreamed about just a few years ago as tools to support student learning—are suddenly possible.

AI-based edtech applications challenge our assumptions about classroom dynamics, homework, and the very nature of teaching relationships. But as we all know, not all AI enhancement actually enhances learning. Some AI systems genuinely help students learn; others just shortcut it. The key is understanding which is which in your specific context.

Accordingly, in your discipline, you need to define how AI either enhances or diminishes different types of learning. Together with your peers, it will be important to figure out:

• How AI systems can enable learning and teaching in your discipline.
• How AI systems can inhibit learning and teaching.
• Which teacher and student collaborations with AI best leverage AI's potential as an educational assistant.

We encourage you to consider AI as an interactive environment or partner for learning activities. Finally, determine which pedagogical approaches best leverage AI's potential for your specific teaching practice and which should be actively avoided or discouraged.

Reimagining Assessment in the Age of AI

Perhaps most urgently, AI challenges how we measure learning. When students can generate essays in seconds, what are we actually assessing? How do we create evaluations that honor individual growth, creativity, and problem-solving?

We need to consider how to authentically assess both AI-assisted and human-only performances of understanding and mastery. Hopefully, we emerge with a clearer understanding of what skills are core to mastery in our fields, with richer measurement methodology that may involve AI in the assessment process.

Again, your expertise here is essential. You know what genuine understanding looks like in your field. To build new assessments, you should:

• Identify what an authentic demonstration of learning looks like in AI-assisted work.
• Determine how to create assignments that students can't shortcut with AI tools.
• Explore different kinds of assessment that can demonstrate even deeper understanding when students work with AI.
• Identify how assessments can value process and growth, not just products.
• Determine where AI collaboration enhances versus undermines learning objectives.

How can we adapt to the knowledge that students will likely turn to AI regardless? Perhaps we pivot to assessing their skills in critical thinking and working with AI, rather than without it. We know all of that is easier said than done. We encourage you to talk to your students and work with your colleagues and institutions to create assessment strategies that work for your specific needs.

Conclusion: The Future of AI Fluency

In every discipline, humans bring irreplaceable capabilities: domain expertise, real-world context, judgment in ambiguous and messy situations, creative problem-solving, ethical reasoning, and relationship building. Our job is to ensure that AI amplifies these capabilities.

This AI fluency works both ways:

1. The deeper your subject matter expertise, the better you can identify meaningful AI applications.
2. The more fluently you work with AI, the further you can push your discipline's boundaries.

Keep these three things in mind: AI disrupts our students' futures and our teaching in both exciting and concerning ways. Responding effectively requires leaning into the critical assessment of the technology—from developing AI fluency via practice to collaborating with colleagues on building an intentional strategy for your specific context.

AI fluency begins and ends with your expertise. You are well-positioned to drive change for the better for your institution and for your students' futures.