Leveraging AI to Save Time Architecting Equitable, Student-centered Learning Experiences

Part II: Transitioning from Whole Group to Small Group to Achieve Equity in Education

The first blog in this series, “Time Efficiency vs. Equity in Education,” explored two major barriers teachers face when shifting from a whole group, teacher-led, teacher-paced approach to instruction to student-centered blended learning models. Time and control are powerful enforcers of the status quo. What if teachers could dramatically reduce the time it takes to design equitable lessons that free the teacher to work directly with individual students and small groups of learners?

Increasing Demands on Teacher’s Time

In today’s educational landscape, teachers face a daunting array of demands on their time. Large class sizes, demanding parents, a constantly evolving digital toolset, shifting leadership initiatives, and teacher shortages have created a perfect storm of time constraints. These challenges often force educators to sacrifice their prep periods, substitute for other classes, or take on additional teaching duties. Amidst these pressures, the task of crafting equitable and student-centered learning experiences becomes increasingly challenging. It’s within this demanding context that the potential of AI stands out as a promising solution, offering teachers a way to save time designing lessons while more effectively meeting the diversity of needs in a class.

Understanding Our Students’ Needs

When I work with teachers to design learning experiences that strive to meet students where they are in their individual learning journeys, the first item on the agenda is pre-assessments. Learning is not like lining up for a race shoulder to shoulder. Students come into our classrooms with different life experiences, prior knowledge, and language proficiencies. To design effectively for a diverse group of students, teachers must find out where everyone is beginning in relation to the material we plan to teach.

Beginning with an access prior knowledge activity, diagnostic, or pre-assessment before we dive into designing learning experiences can help us better understand (and design for) what students know, understand, and can do. That way, we can identify gaps, misconceptions, areas of need, and existing competencies. This is critical if we want to design equitable learning experiences that strive to give individual learners the inputs they need to reach a particular learning goal.

Using AI to Generate Pre-Assessments

Imagine a high school math teacher is about to embark on a new unit about algebraic equations. Before diving into the unit, he administers a pre-assessment to his diverse class of 30 students. The pre-assessment consists of a series of algebraic problems of varying difficulty and specific vocabulary related to algebraic equations. The results reveal that while some students grasp the fundamental concepts and basic vocabulary, a significant portion of the class struggles with basic algebraic operations, such as solving for variables, and subject-specific vocabulary. In addition, the data indicates that a few students already have advanced knowledge of algebraic equations and would benefit from more challenging material.

Given these insights, the teacher can now tailor his instruction more effectively. He can design differentiated lesson plans, grouping students based on their pre-assessment results. He can then use a model, like the station rotation, to circulate students through a series of learning activities, including a needs-based teacher-led station. For those struggling with the basics, the teacher can tailor instruction and learning activities to focus on foundational concepts and skills necessary to solve equations while also providing more scaffolds and support. For the advanced students, he can prepare extension and enrichment tasks to keep them engaged and challenged. As a result, the teacher’s approach becomes more equitable, addressing the specific needs of each student, and increasing the likelihood that all students will successfully reach the learning goal of mastering algebraic equations. This example illustrates how starting with a pre-assessment can be pivotal in designing equitable learning experiences.

Teachers hesitant to use pre-assessments because of the time it takes to generate them can now lean on AI technology to do that heavy lifting for them. MagicSchool.ai is a robust free teacher resource that makes creating diagnostics and pre-assessments quick and painless. If the teacher in our scenario above wanted to generate a pre-assessment to better understand where each student was in terms of their prior knowledge and understanding of algebraic equations, they could input the topic and grade level to have MagicSchool.ai generate a list of 5, 10, or 15 questions they can include in their pre-assessment.

Whenever AI generates questions for a pre-assessment (or anything else for that matter), it is important to look at the content and consider the following before using it.

• How reliable is the data generated by the AI tool? Are there any potential biases or inaccuracies in the questions or answers provided?
• Will the AI-generated questions effectively gauge students’ knowledge and skills related to the subject or topic?
• Are the questions clear and unambiguous? Are there any issues with the way the questions are worded that might create confusion or incorrect interpretations?
• Do the pre-assessment questions generated vary enough in difficulty or complexity to reveal where students are in terms of the depth of their understanding or abilities?
• Do the questions include words or symbols that students might not understand or be familiar with?

MagicSchool.ai even has a translation tool that is in Beta that can translate your pre-assessment into another language to ensure the math teacher is getting an accurate representation of what every student knows and can do.

Once teachers have pre-assessment data, it’s time to design differentiated student-centered lessons!

AI Drastically Reduces The Time Required to Design Differentiated Learning Experiences

As our classrooms become increasingly diverse in terms of skills, abilities, learning preferences, and language proficiencies, differentiating instruction is an essential part of the equity puzzle.

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“Differentiation means tailoring instruction to meet individual needs. Whether teachers differentiate content, process, products, or the learning environment, the use of ongoing assessment and flexible grouping makes this a successful approach to instruction.”

— Carol Ann Tomlinson

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When teachers differentiate instruction, they adjust the content, process, or products to meet the specific needs of individual students or groups of students. This may involve modifying the depth or complexity of the material or the supports and scaffolds provided. For instance, advanced students might delve deeper into a topic, while those struggling may receive more foundational instruction.

As teachers, understanding the concept of differentiated instruction is just the first step. The real magic happens when we put this approach into practice. Now, instead of being daunted by the need to differentiate, AI can do much of the heavy lifting for us!

Let’s look at how we can use MagicSchool’s conceptual understanding generator to help students at different levels. To extend our previous math example, I selected 9th grade and entered “algebraic equations” as my topic in the conceptual understanding generator, and it produced the following ideas.

Building Conceptual Understanding in Algebraic Equations

1. Real-world Problem Solving: Present students with real-world scenarios that can be modeled using algebraic equations. For example, ask them to create equations to determine the cost of buying multiple items at a store or the distance covered by a moving object over time. By connecting algebra to practical situations, students can develop a deeper conceptual understanding of how equations represent relationships and solve problems.
2. Visual Representations: Use visual aids, such as graphs, diagrams, or manipulatives, to represent algebraic equations. For instance, when introducing linear equations, plot points on a graph and guide students to identify the pattern and relationship between the variables. By visually representing the equations, students can better comprehend how changes in variables impact the equation and its graphical representation.
3. Concrete to Abstract Progression: Begin by using concrete examples or physical objects to introduce the concept of algebraic equations. For instance, use objects like blocks or tokens to represent variables and constants. Students can then manipulate and rearrange the objects to solve simple equations. Gradually transition to more abstract representations, such as using letters or symbols instead of objects, to help students understand that algebraic equations can represent a wide range of situations beyond the specific objects initially used.

These activities designed to build conceptual understanding of algebraic equations can be adjusted for students at different skill or ability levels to ensure the learning activity or guided instruction can be differentiated for different groups to ensure all students can feel successful.

If I wanted to use the first suggestion to have students engage with real-world problem-solving, my next stop would be to jump over to ChatGPT and ask it to generate real-world scenarios that can be modeled using algebraic equations that are at three different levels of rigor and complexity.

A math teacher might use these real-world scenarios in a teacher-led station to help students understand how to use algebraic equations to solve real-world problems and challenge them to create their own scenarios or provide them with practice problems at an appropriate level of rigor.

Once students have received instruction on solving algebraic equations, these types of real-world activities could be used to reinforce conceptual understanding and encourage independent or group practice at offline learning stations.

Reimagining Our Approach to Designing and Facilitating Learning Experiences with AI

The emergence of AI tools has opened up a world of possibilities for customizing and enhancing learning experiences. From differentiating texts and lessons to providing tailored scaffolds, support, and assessments, the options are abundant. I’ve used several education-focused AI tools, including EduAide.ai, Diffit, and Curipod, and each with its unique strengths. Just as learners vary in their needs, preferences, and strengths, so do we as teachers. There’s no one-size-fits-all when it comes to AI tools. What truly matters is the willingness to invest some time in exploration and experimentation. It’s astonishing how, with AI as our ally, we can swiftly craft engaging and equitable learning experiences.

I realize AI might be scary for some teachers, but these education-focused AI tools provide teachers with a safe way to use AI to enhance and simplify our work. I encourage you to invest time exploring and playing. Not only is it incredible what we can accomplish as architects of learning experiences in a short amount of time with AI, but if you are anything like me, you’ll leave the experience feeling inspired and energized, buzzing with ideas about how you can use AI to be more effective and efficient. I hope the experience leaves teachers feeling invigorated and equipped with new ideas and strategies to better meet the diverse needs of our students. (And the time our future selves will save designing lessons will thank us!).

In the next, and final blog in this series, we’ll turn our attention to the transition from teacher control to student agency and how AI can help us to provide students with meaningful choices and flexible pathways.