Learner Variability (LV) is the understanding that every learner has a unique learning profile of strengths and challenges. Research shows that personalized learning rooted in this understanding has a greater chance of success, but designing instruction that addresses LV through whole-child development remains a persistent challenge for busy educators managing large classrooms. Although research on differentiated instruction, social-emotional learning, and inclusive pedagogy is well established, educators often lack timely and practical tools to integrate this evidence directly into more personalized lesson-planning workflows.
AI-enabled planning tools offer new opportunities to bridge the gap between research and practice, especially when built using strong and inclusive research and development (R&D) systems. Yet there is limited empirical evidence on how educators use such tools in practice and how they shape instructional decision-making.
To better understand this dynamic, Digital Promise recently completed a five-month mixed-methods study exploring educators’ perceptions and use of an emerging AI-powered lesson-planning platform, Digital Promise Yourway, which is grounded in the research of Digital Promise’s Learner Variability Navigator. The purpose of this research was to illustrate how tech-enabled access to research on learner variability impacts lesson planning and the development of learning resources.
Below, I highlight 5 takeaways of this study that can guide the development of future products, tools, research, and applications of AI in edtech. By integrating research into LV factors and classroom strategies, instructional designers can strengthen the experiences of educators and ultimately improve student outcomes.
1. Including product teams in the research process hones the educator perspective.
As part of this collaborative education research and development (R&D) process between Digital Promise and Yourway Learning, the product team was invited to sit in on focus group conversations led by researchers to hear directly how the product was being used by educator participants. Findings from the focus groups were then directly implemented into the product design to better serve educators. For example, one finding suggested that more drop-down features could be used to make inputting easier for educators, and the product team was able to implement that change quickly.
2. AI helps translate learner-variability research into everyday instructional practice.
The research findings from this study also offer vital, generalizable insights for the broader education and edtech communities on how AI can successfully support personalized instruction. Educators consistently described the platform as a helpful tool for applying instructional practices they already value but do not always have the time, structure, or capacity to consistently implement. One educator shared, “It gave me something I might not have thought of that I could use to support my students,” indicating that AI access expanded their strategies for differentiating instruction.
3. AI-supported planning tools can influence educator’s instructional thinking, not just their outputs.
In today’s growing market of AI-supported instructional tools, many available products emphasize generic lesson generation, speed, or content production. In an effort to ease the workload of educators, these tools may inadvertently prioritize efficiency over intentionality. However, this study suggests that AI tools can function not only as productivity aids, but also as cognitive scaffolds that prompt educators to consider learning differences and LV factors earlier. One educator stated, “I am planning more intentionally and thinking about learner differences earlier than I normally would,” connecting the AI’s supports directly to deeper instructional practices. Another educator highlighted the importance of having additional resources integrated into the workflow: “I like that it gives you additional resources right in the plan. Even though I may not need them all, it helps me think about different ways to support students.”
4. AI tools are most effective when positioned as planning partners rather than replacements for teacher expertise.
Educators consistently framed the AI platform as a support that enhances intentionality and organization rather than as a substitute for professional judgment. The study found that educators prioritize coherence and relevance and are more likely to adopt AI tools when they align with educators’ existing responsibilities and planning norms. Sustained engagement with a tool depends on how well it integrates into a daily workflow and how much it allows educators to actively refine lesson plans.
5. Tech-enabled access fosters cross-educator collaboration.
AI tools can generate targeted strategies that foster teamwork across different student support systems. “I put the skill in, and it gives me strategies I can bring to my co-educators,” shared one special educator participant, describing how she used the tool to generate strategies she could share directly with her general education colleagues.
LV is a key differentiator in an increasingly crowded AI marketplace.
While many tools focus purely on efficiency, this study suggests that AI platforms grounded in research-based differentiation offer added value by shaping how educators think about instruction. Ultimately, this study illustrates that the tools we build reflect the values we embed in our R&D processes. When educators, researchers, and developers ground core design principles in learner variability research, the result is not just more efficient lesson planning—it is more equitable, research-informed instruction that better serves every learner in the room.
