Reimagining Assessment: How AI Input Transforms Learning Design
The landscape of educational assessment continues to evolve through fundamental transformation. Keppler, Sinchaisri, & Snyder's (2024) groundbreaking research reveals a fascinating pattern: educators who use AI not just for creating materials (output) but for exploring instructional strategies and assessment design (input) achieve significantly better results. This insight perfectly aligns with the principles of backward design and formative assessment.
The Input-Output Paradox
When educators approach AI solely as an output tool, generating worksheets, quizzes, or lesson plans, they miss its deeper potential. The real transformation happens when we use AI as a thought partner in backward design, starting with our desired learning outcomes and working backward to design meaningful formative assessments. This approach echoes Wiggins and McTighe's foundational work while adding a powerful new dimension through AI's analytical capabilities. The solution lies in starting with input, using AI to explore assessment strategies and student thinking patterns, before moving to output. This approach ensures that formative assessments align with learning goals and provide meaningful data to guide instruction.
Rethinking Assessment Design
The integration of AI into assessment design represents more than just a technological shift. It offers a fundamental reimagining of how we approach the evaluation of student learning. While traditional assessment design often begins with the creation of materials, a more sophisticated approach emerges when we consider AI's potential as a thought partner in the entire assessment ecosystem. Platforms like Braide.AI exemplify this evolution in assessment design, offering teachers tools that go beyond simple grading automation to provide strategic insights into student learning patterns and opportunities for targeted intervention. At the most basic level, educators might use AI simply for output generation: creating quizzes, rubrics, and feedback templates. While this provides efficiency gains, it barely scratches the surface of AI's potential in assessment design.
What if we used AI as a thinking partner rather than just a production tool? In this case we would explore various assessment strategies, diving deep into essential learning standards, prerequisite skills, learning progressions, and investigating common student misconceptions before creating any materials. This strategic engagement allows teachers to anticipate learning challenges and design more effective formative checkpoints that truly measure potential learning gaps.
At the deepest level of integration, AI becomes seamlessly woven into backward design itself. At this level, AI becomes a collaborative tool for mapping essential learning progressions and identifying the most critical moments for assessment. Teachers can work with AI to design embedded formative assessments that feel natural within the learning process rather than interrupting it. These assessments become seamlessly woven into the fabric of instruction, creating continuous feedback loops that inform both teaching and learning. For example, we might use AI to analyze common patterns in student writing development, helping to identify precise moments when different types of feedback would be most effective.
Keppler's research demonstrates this layered approach. Moving from basic output to strategic input to integrated design yields significantly better outcomes than using AI solely for material generation. When teachers engage with AI to explore assessment strategies before creating materials, they develop more nuanced and effective formative practices. The key lies in using AI not just to create assessments but to think more deeply about assessment design itself, considering questions like “What evidence would truly demonstrate understanding?” and “How can we capture student thinking in progress?”
This rethinking of assessment design aligns perfectly with backward design principles. Instead of starting with assessment tasks, educators begin with desired outcomes and use AI to help map the evidence needed to demonstrate understanding. This might involve using AI to analyze successful student work samples, identify skill gaps, and design formative checkpoints that provide meaningful data about student thinking. The result is a more coherent and purposeful assessment system that serves learning rather than just measuring it.
The implications of this approach extend beyond individual classrooms. When schools and districts use AI as a thought partner in assessment design, they often discover new insights about student learning that inform their broader instructional practices. This creates a data driven cycle where better assessment design leads to deeper understanding of student learning, which in turn informs more effective teaching strategies.
Next Steps
The most successful implementations start with existing strengths, using AI to enhance your current effective practices and routines:
Begin with clear learning outcomes
Use AI to explore assessment strategies
Design formative checkpoints
Create feedback mechanisms
Implement, reflect, and refine
The future of assessment lies not in automation but in augmentation. When we use AI thoughtfully in backward design, we can create more effective formative assessment systems that truly serve learning while respecting teacher expertise and student needs. In The AI Assist: Strategies for Integrating AI into the Very Human Act of Teaching (ASCD/ISTE, 2024), I explore how the HAIL framework (Humanize, Augment, Integrate, Leverage) can guide this transformation, helping educators maintain the human element while harnessing AI's potential to enhance both backward design and formative assessment practices.
===
Dr. Nathan Lang-Raad is the founder and CEO of Raad Education, where he leads innovations in educational practice and theory. His work spans roles as a teacher, elementary and high school administrator, and university adjunct professor. Nathan has served as the director of elementary curriculum and instruction for Metropolitan Nashville Public Schools and as an education supervisor at NASA's Johnson Space Center. Dr. Lang-Raad has published 11 books and speaks internationally on AI integration in education, instructional coaching, math instruction, and innovative teaching methods. Visit his website. Follow him on BlueSky @drlangraad.bsky.social and Instagram @drlangraad or connect on LinkedIn
