Artificial intelligence (AI) can promote equity for emergent bilingual students in two ways: 1) by creating powerful, individualized learning pathways and 2) by quickly producing data that has historically been absent or extremely delayed. AI can create personalized learning for each student based on their current language level and deliver on-time data that educators can use to make informed instructional decisions.
This is important because educators typically have had to rely on data collected from a time-consuming one-to-one exchange between a proctor and the student, and the results of that test were not immediately available.
Thus, an educator likely had to begin teaching emergent bilingual students without knowing their true language proficiency level or which language standards had been mastered. But with individualized learning pathways, AI, and data, those educators can use their valuable time to make academic decisions that will move the needle for their students.
Celebrating Emergent Bilinguals
Emergent bilinguals (also known as English learners) are students who are developing skills in their heritage languages while also learning a new language. Using a term like emergent bilingual celebrates the linguistic knowledge of the heritage languages that learners bring with them to school. Those funds of knowledge can and should be leveraged to help the students become bilingual (in this case, by adding English to their repertoires).
Those students’ heritage languages and cultures are assets to focus on as deep knowledge, and something that can be built upon.
Currently, about one in every ten students in US public schools is an emergent bilingual; that number is growing. Thanks to technological advancements, we can now use AI to help support bilingual and multilingual students.
In fact, if you have a program that individualizes the student experience and provides data based on what learners have done online—or that pushes information to you in some other way—you’re probably already using AI in your classroom.
Five Ways AI Promotes Equity and Supports Emergent Bilinguals
Defined as a computer’s ability to complete tasks normally performed by humans, AI can be used to listen to speech and respond as well as to collect and aggregate data. Because AI also helps machines learn from experience and adjust to new inputs, it’s the perfect tool for helping emergent bilinguals in the classroom.
Here are five ways that an AI-enabled language program helps promote equity and supports emergent bilingual students:
- Continually adjusts to the students’ needs through personalization.
Excellent AI will determine the language level of the student and start them on a pathway that meets them right where they are. The algorithm will systematically move the student along an upward trajectory of learning. However, if a student makes a mistake, there should be scaffolds in place to provide corrective feedback. The student experience should also be coded in a way that alerts the educator whenever a learner is facing difficulties. All activity that the learner engages in online (e.g., when the student speaks, makes selections via multiple choice, or inputs answers on a keyboard) should be captured as data for the educator. Using this data, the AI can continually learn and adjust to individual students. For example, a speech recognition engine can “listen” as the learners talk and get to know the way they speak, including their individual accents. All this data can readily be pushed out to the educator in a usable and actionable way. In the best case, the AI should provide offline support in the form of standards-based lessons and help the educator group students according to their needs.
- Helps educators prioritize instruction with current, accurate data.
Teaching emergent bilinguals an entirely new language is not a “set it and forget it” exercise by any means. The data is constantly changing based on how the student progresses. Using an AI-enabled platform, educators, principals, and district leaders can all monitor how well this student demographic is doing. This is a far cry from the past, when educators had to wait for an annual language proficiency test to find out if a learner was making progress (or not). Now, they can log in and check progress year-round. Better yet, the embedded progress monitoring and offline resources help educators prioritize and target instruction to meet the individual needs of all the students.
- Works in tandem with educators and their knowledge.
Early on, there was concern over whether AI would somehow “replace’’ human beings. We’ve always known that when it comes to the classroom, there is no replacement for a human educator who understands the art of teaching and has the expertise and skill set to make instructional decisions with the data that the AI is providing. In the best-case scenario, an equity-focused program will provide offline lessons that are connected to the online experience. It should also help educators continue the personalization in face-to-face environments by providing suggested groupings of students based on their online performance. Indeed, educators can use their discernment to deliver meaningful lessons.
- Provides speaking practice.
Aided by AI, embedded speech recognition technology can be coded to listen to students and determine whether they’re mastering the language proficiency standard they’re being taught. This is important because other programs may only look at and factor in reading standards. While reading comprehension is a main goal, the fact is, oral language comprehension will set the foundation for both reading comprehension and proficiency in writing. Oral language is the bedrock for the other domains, and speaking practice is essential to oral language proficiency.
- Embeds equity into the curriculum.
At our company, we’re constantly thinking about how to proceduralize equity in the language-learning classroom. We spend time thinking about creating infrastructure around the concept of equity and how to embed equity into the curriculum versus having it just be an afterthought or an add-on. If we start from this point of view, every subsequent decision will follow suit. One of the ways that we aim to proceduralize equity is through an algorithm that includes AI, speech recognition, human intelligence, and data.
There’s More to Come
At a fundamental level, AI uses computers and machines to mimic human decision-making, perception, and other processes needed to complete tasks.
It also creates individualized learning pathways for students, gathers data on their performance, and presents that data to the educator in a way that makes sense. Artificial intelligence should also correlate and connect with the state standards and objectives, show educators what a student has mastered, and reveal what work still needs to be done.
As we begin to see more AI make its way into educational technology, there’s no doubt that its positive impacts will continue to materialize—both for emergent bilinguals and for all learners.
Maya Valencia Goodall, MEd, MA, is senior director of emergent bilingual curriculum at Lexia Learning (www.lexialearning.com).