How to Use Generative A.I. to Design Better Scaffolds and Supports

For all the talk of students using Artificial Intelligence to cheat, we can easily miss the reality that A.I. has made differentiated instruction more feasible for educators. In this week’s article and podcast episode, I explore how we can leverage A.I. tools for differentiated instruction. I share ideas for designing scaffolds and supports for ELL / EL students and students with learning differences. We explore an approach a teacher might use in creating leveled readers. But ultimately, if our goal is to empower students, we will need to find ways in the future for students to design, select, and curate scaffolds and supports using machine learning.

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Reaching the (Zone of Proximal Development) ZPD

We all have things that we can do independently but other things that are impossible for us to do. Not to brag but I’m pretty good at loading a dishwasher. On the other hand, I can’t slam dunk a basketball. However, there are also certain things in a middle zone that you can’t do on your own quite yet, but you can accomplish them with a little help. You might need help from a teacher, a peer group, or a resource. With proper training, hours of practice, and a trampoline, I could eventually slam dunk a basketball. Maybe. Okay, probably not.

Vygotsky described this middle space as the Zone of Proximal Development (ZPD). Here’s how ZPD works. At the center, you have the things you can do on your own. On the outside, you have the things you cannot do. But in this middle zone you have the Zone of Proximal Development, which are the things you can do with guidance and support.

In 1976, Jerome Bruner applied Vygotsky’s theory to the K-12 educational setting with the concept of scaffolding. Here, educators provide supports, called scaffolds, to help students master the learning. Then, like the scaffolds in a building, teachers pull back the supports as students master the knowledge. At this point, the ZPD grows outward as students master new knowledge with new scaffolds.

As an educator, you might also use A.I. for this scaffolding process. It could be as simple as an algorithm with “recommended tutorials” for students who are struggling. You might use it to design scaffolds from scratch or to modify existing ones. But for something more intensive, students might use generative A.I. as a type of tutor with a back-and-forth question-and-answer (for more on this process, check out the interview I did with Sal Khan from the Khan Academy).

Designing Scaffolds for Neurodiverse Students

A few months ago, I had a student who was struggling to keep up with the frantic schedule of our master’s program. So many courses started at one date and ended at a different date. We had one credit courses and three credit courses and elective classes mixed in. Some were synchronous, others asynchronous. This particular student has ADHD and challenges with executive function. Though she is hard-working, intelligent, and creative, she struggles to keep up with too many details in multiple directions.

During office hours, I met with her and three other students to show how you could use ChatGPT to create a day-by-day course plan. We copied and pasted information from Canvas sites and syllabi. We then had it break down the assignments and projects into smaller chunks with to do lists. From there, we turned the to do list into a single, doable, color-coded spreadsheet. We then used the plan to fill in a Google Calendar and set alarms on the phone. What started with me demonstrating ChatGPT quickly became a conversation among students where they shared coping strategies and talked about what was harder and easier for them.

This was was focused on the technology but it was also deeply human. More than anything else, these teacher candidates needed to feel known and affirmed. They needed a peer group of other professionals to say, “You’re not alone. We’re living in a world that wasn’t made for us.”

Each student ended up with a personalized approach that reduced extraneous cognitive load and allowed them to stay focused on the learning. One person used a specific calendar app that uses AI to modify the daily schedule. Another person chose a specific to do list and reminder app that he has been using. We then talked about what it might look like to teach while having ADHD.

At one point, a student said, “I wish I had generative AI when I was in high school. That would have been a game-changer for me.”

Another talked about how they might use the approach I had just shared to break down big projects into smaller chunks for students who felt overwhelmed in a project-based learning classroom.

This is just one example of how you might use A.I. to generate structures and scaffolds for neurodiverse students. Here are a few more ideas:

• Providing additional handouts to facilitate task-analysis and executive function
• Using A.I. to help schedule small groups
• Using A.I. speech recognition software as an assistive technology to help students with writing
• Using A.I. image generators to help students who need a more concrete example of what they are learning in class
• Designing targeted skill practice. For example, you might use a chatbot to generate word problems for students who struggle with 2-step equations, or you might use it to create a high-interest non-fiction text at a student’s reading with sample questions
• Using A.I. to modify assignments to reduce cognitive load (fewer steps) while encouraging students to still access the grade level content.
• Using A.I. to reduce the amount of work while still maintaining a high challenge level. For example, a student with dyscalculia might need fewer problems but can still master the math content at the same grade level.

None of these supports should replace the goals within an Individualized Education Plan (IEP). We don’t want to replace educators with algorithms. We can, however, use the A.I. as a starting place for designing more personalized scaffolds and supports. Here, the A.I. platform saves time and makes the differentiation process more feasible for teachers. It works like an assistant to create something general that you can then modify based on your own expertise and knowledge of students.

 

Using A.I. to Generate Language Scaffolds

We just examined how to help provide supports for neurodiverse students, but what about students who are learning English as a speaker of another language (ELL, ESL, ESOL students)? We can use A.I. as an initial starting place for creating language supports. These include:

• Front-loading vocabulary: you can use A.I. to identify some of the Tier 2 and Tier 3 vocabulary that students might need to master. While you’ll still need to create a list of vocabulary yourself (and rely on student feedback) the A.I. can be a great starting place. I’ve found that certain chatbots do a great job defining vocabulary in simple terms and even coming up with example sentences. If you couple this with an A.I. image generator, you can save time in generating front-loaded vocabulary, handouts, and slideshows.
• Providing translation help: While it still works best to partner students with someone who is multilingual, A.I. translators have come a long way. The dynamic aspects of an A.I. bot allow students to interact with the content in their native language while also being exposed to content in English. This is especially helpful for students are feeling shy or even scared about speaking a new language in front of their peers.
• Providing leveled sentence stems: This remains a weaker area for A.I. but I am noticing significant improvements in A.I.-generated sentence stems, sentence frames, and clozes. The key is in making the prompts specific and clear.
• Using visuals within the project to help facilitate language development: As A.I.-generated visual art continues to improve, we can potentially create additional visuals that can aid with accessing English.
• Assess language proficiency: I can work as a formative assessment tool by analyzing a student’s speech or writing. This can be particularly useful in assessing language learners who may not have access to a native speaker or who are learning in a remote setting.
• Language practice: Students can provide the A.I. chatbot with the directions to engage in a language role-playing conversation. They can set the purpose, location, and fictional person they want to A.I. to pretend to be. Then, they can practice English with the chatbot.

Notice that a teacher can begin with these A.I.-generated supports but then modify them to suit their context. Teachers might even invite students to help with this modification process. This then frees teachers up to pay attention to a students’ affective filter and finding ways to reduce fear and anxiety.

I’ve noticed that ELL teachers tend to spend a significant amount of time designing supports and scaffolds. Meanwhile, many ELL students in a non-ELL classrooms fail to receive certain supports they need. If we can leverage A.I. to save time in designing scaffolds, we can help students access the content while improving in their language development. Ultimately, you know your students best. You know what supports they need. But if you can begin with

Example: Leveled Readers

Imagine you are teaching about industrialization for a seventh-grade social studies class. You want to have a class Socratic Seminar asking the question, “Was the Industrial Revolution an overall negative or positive thing for our world?”

You might begin with a review of the Industrial Revolution. You could start with front-loaded vocabulary using A.I.-generated vocabulary and definitions connected to public domain images (or even A.I.-generated images). From there, you might review what students have already learned about the Industrial Revolution.

Next, you might have students use an AI chatbot to do a question and answer about the industrial revolution. Here, they can ask any question they want and find the answers and then ask follow up questions.

But at this point, you want students to do some in-depth, interactive reading about the industrial revolution. The problem is you’ve got students who read at multiple grade levels / lexile levels. Yet, you want them to read independently. Here’s where generative A.I. makes differentiation far more feasible. I want to share three different approaches you might take.

#1: A.I.-Generated and Human-Modified

You could start with key points you’d like to cover and then have a generative A.I. create a text written at the 7^th Grade reading level. It’s not a bad start but it’s a little boring, so you edit the text and punch it up with some humor, some bizarre examples, and a few things you think the A.I. missed. You’ll also fact-check it to make sure it’s accurate and you’ll review it for any bias or loaded language.

From there, the A.I. chatbot can take your modified version and create a leveled reading ranging from 3^rd Grade to 7^th Grade. Now every student can access the text at a level that matches their fluency level. You might also ask for a set of critical thinking questions (with sentence stems) and some vocabulary that you can put on the side of the text as a quick reference.

#2: Human-Generated and A.I.-Modified

With this model, you might write your own text or you could pull a text that’s Creative Commons (like a Wikipedia entry). You could then ask the chatbot to add a few details and change some of the language. Then, the chatbot could create the leveled readers and the vocabulary (both content vocabulary and academic language).

#3: A Mash-Up

With this option, you might provide 2-3 segments of source material and ask the chatbot to create one cohesive piece that includes key details from all three sources. You might even ask for it to quote the sources and cite them. You can then fact-check it, rework the piece, and ultimately send it back to be modified to fit different reading levels.

Notice that this approach treats A.I. as a learning tool. The process is messy, human, and inherently social. Here, the machine learning speeds up the process and makes it more feasible. But you are still the editor, creator, and curator of the content. You are the expert on your students and the supports they need.

Avoiding Learned Helplessness

“I just want you to know that three of our exceptional learners were in tears today. We were working on the STEAM projects, and I thought I provided the right supports but . . .”

“Good,” our special education teacher interrupted.

“What do you mean?” I asked.

“They came into their next class and told me all about it,” she said.

“I’m confused,” I admitted.

“They didn’t tell me about the tears. They told me about the struggles they faced but they also told me all about what they did to get their solar ovens to work,” she answered.

“I feel bad, though,” I pointed out.

“You shouldn’t feel bad at all. Look, they cried because they cared. Do any of your other students cry during projects?”

“Sometimes,” I admitted.

“The same should be true of students with learning differences. They need to experience productive struggle. A lot of them have developed learned helplessness. Teachers with the best of intentions have given them the answers instead of giving a scaffold,” she said.

“You really think it’s okay that they cried?” I asked.

“My goal has been to get them to be self-directed. I want them to be their own advocates. But I also want them to do so in a way that fosters resilience.”

Learned helplessness refers to a psychological phenomenon where students develop a belief that they are incapable of performing certain tasks or learning specific skills. They may become passive, disengaged, or unmotivated in the learning process.

This can happen when a teacher gives too much help too early and fails to encourage productive struggle. It can also happen when there’s a lack of support or an overly critical environment. When students repeatedly experience failure or perceive a task as too difficult, they may begin to feel that their efforts are futile.

As we implement A.I. for scaffolding, we need to ensure that it doesn’t short-circuit productive struggle. If students get immediate help with any question they have, this might develop into a form of learned helplessness.

As we think about designing supports for students, it will ultimately be about the implementation rather than merely the design of the scaffolds we use. Ultimately, teachers have the relational knowledge to provide the necessary scaffolding to help students master the standards. A.I. simply makes this more feasible.

Empowering Students to Use A.I. for Supports

A few semester ago, I had a student ask for the transcript from our class Zoom session. He used the chatbot to delete the time stamps and translate it to Spanish. As a dual language student, he likes using both languages as he wrestles with ideas and compares the transcription to his notes in both languages.

I share this story because every time there’s a new technology and people are scared about cheating, I always ask, “How are people using this to scaffold their own learning?”

In other words, how might an exceptional learner use this? How might an English Language Learner use this? How might someone who hasn’t had the same advantages use this? Because what might seem to some as a “chance to cheat” might be a game-changer for someone else.

As educators, we can empower students to self-select the scaffolds they use. So, while you might make modifications for specific students (like the previously mentioned checklists or modified assignments) you might also have a bank of different tutorials and scaffolds that students can access if they need additional help. These supports should be available to all students. This approach embodies Universal Design for Learning. A quick teaser here. Next week, I’m going to have Katie Novak on my podcast to talk more in-depth about UDL and what it means for K-12 educators.

Built around cognitive neuroscience, UDL is an inclusive educational framework that seeks to remove barriers while also keeping the learning challenging for all students. A UDL approach includes certain paradigm shifts:

• From a deficit mindset to neurodiversity
• From singular accommodations to universally accessible scaffolds and supports
• From a teacher-centric view to a student-centered approach centered on student agency

In the 1990’s, Dr. David Rose and the Center for Applied Special Technology (CAST) articulated the three UDL principals:

1. Multiple Means of Representation: Presenting information in different ways (like text, images, videos, or audio).
2. Multiple Means of Action and Expression: Allowing students to engage with and demonstrate their knowledge and skills in various ways (such as through writing, speaking, multimedia, or hands-on activities).
3. Multiple Means of Engagement: Fostering motivation and interest in learning by offering choices, using relevant content, encouraging collaboration, and employing strategies to keep students engaged.

Note that students should be empowered to select the scaffolds and supports they need. The focus here is on their own agency and autonomy.  Students are empowered throughout all three UDL principles.

With A.I., Universal Design is more feasible. We might use A.I.-driven virtual labs or simulations to help students solve challenging problems. We can provide students with options for the media format of their finished products. The students can then leverage A.I. for their creative work. We can use A.I. to design better projects and choice menus. We might even provide access to A.I. tutors. Universal Design for Learning embraces the diversity of all learners so that all students are empowered to become self-directed problem-solvers and lifelong learners – the very skills they will need as they navigate the maze of an uncertain future.

Ultimately, we can’t predict how A.I. will change learning. We can observe how it is changing creativity, information literacy, personalized learning, and assessment. But we can’t predict what this will look like in a decade from now. What can do is empower our students to be adaptable as they experience these changes. We can help them see how they can use A.I. as a tool to make the learning more accessible – whether that’s with scaffolds and supports or through machine learning for feedback.

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