• Lizdelia Piñón, Ed.D. • IDRA Newsletter • March 2024 •
Bilingual educators are often challenged by a lack of resources, outdated data and evolving demands of assessments, particularly in linguistically diverse schools. Artificial intelligence (AI) in language instruction offers bilingual educators access to powerful tools.
The 5 million emergent bilingual (English learner) students enrolled in public constitute 10% of the total student body (NCES, 2023). Among these students, Spanish is the predominant language spoken at home, accounting for over 75% of the linguistic background of emergent bilingual students in U.S. public schools. They are often taught by bilingual educators tasked to help them in the complex process of acquiring the English language as they maintain their native language.
AI can revolutionize how bilingual educators assess, educate, engage and support students. According to a recent study by Wei, AI-mediated language instruction significantly enhanced English learning achievement, L2 [second language] motivation, and self-regulated learning” among emergent bilingual students (2023).
This technology stands poised to impact emergent bilingual students significantly. By leveraging AI tools, bilingual educators can address several crucial challenges. These tools provide support for novice teachers and those lacking experience working with emerging bilingual students as well as offer personalized instructional strategies.
Linguistic Diversity with AI
One of the primary challenges bilingual educators face is the intricate task of accommodating the diverse linguistic and cultural backgrounds of emergent bilingual students while ensuring their ability to meet academic standards in English. Students enter schools with varying proficiency levels in their native language and English, necessitating the teacher’s ability to assess and support their progress in both languages. Moreover, each student advances through their language acquisition journey at a unique pace.
By integrating AI technology, bilingual teachers can provide students with personalized learning experiences, enhanced practice opportunities, and immediate feedback, thereby accelerating their English language acquisition process.
Examples of how educators can address linguistic diversity with AI tools include the following.
Personalized Learning Pathways – AI tools can provide personalized learning pathways for emergent bilingual students based on their language proficiency levels, learning preferences and learning styles. This personalized approach also helps with effective grouping and eliminates reliance on outdated data points while providing essential native language support.
Language Assessment and Proficiency Tracking – AI technology tools can accurately assess the language proficiency of bilingual students in multiple languages. The tools can also provide information on students’ reading comprehension, oral proficiency, and content vocabulary knowledge and can be tailored to meet individual needs.
Language Translation and Transcription – AI-powered language translation and transcription tools can help bridge the communication gaps between district leaders, school staff, bilingual educators, teachers, families and students. These easy-to-use (and often free) tools can help translate content material, instructions, and assessments and provide feedback in students’ native language, making it more comprehensible and accessible.
Resources with AI
Bilingual educators often face a lack of resources due to underfunding and limited access to content area material in diverse languages and levels. Below are key ways bilingual educators can address resource limitations by leveraging AI tools.
Content Creation and Customization – AI tools streamline the development of bilingual instructional materials, quizzes, presentations, rubrics and more. AI tools can automate processes, enabling educators to generate diverse learning materials tailored to students’ language proficiency, individual needs and academic levels.
Virtual Tutoring by AI and Language Practice – Bilingual educators can set up AI tutoring platforms to offer personalized support for each student. Numerous free platforms provide opportunities for conversation practice, interactive language lessons and feedback in multiple languages that are accessible any time.
Open Educational Resources (OER) – Bilingual educators can review and share OER materials that align with their district’s curriculum and the language proficiency levels of their students. This ensures that resources effectively meet specific educational needs.
Assessment with AI
Bilingual educators can address the lack of native language assessments with AI tools for the following purposes.
Adaptive Assessments – Bilingual educators can easily use AI tools to create academic assessment items that take into account emergent bilingual students’ language proficiency level, interest, learning ability and even their language.
Speech Recognition and Pronunciation Assessment – AI technology can assess a student’s pronunciation, fluency and intonation in multiple languages. While monitoring potential AI accent bias, bilingual educators can use this to help students practice their oral language development, which is often the most difficult to assess and develop.
Data Analytics – AI-powered data analytics can help bilingual educators analyze assessment data to identify areas of improvement needed by students’ language learning. It helps target the areas where the teacher can provide student support. This can also help educators make accurate, data-informed decisions, differentiate instruction, and allow for a student’s personalized style of learning and interest.
Natural Language Processing (NLP) – NLP uses AI technology to analyze students’ spoken and written language samples by assessing their grammar, vocabulary use, writing fluency and language skills. For example, Texas will use AI-powered technology similar to this next year to score standardized tests (Peters, 2024).
Challenges and Limitations
While AI technology offers much support for bilingual educators with their students, it also comes with limitations and challenges. Educators must be aware that AI tools may have language bias, lack cultural relevance, breach privacy and hinder data security. Addressing these, as well as other challenges and limitations, requires teamwork among administrators, bilingual educators, IT teams, and even policymakers to make sure that AI is being used ethically and responsibly.
At the same time, hesitancy risks excluding particular student groups, like emergent bilingual students, from using and benefiting from AI education (Bojorquez & Martínez Vega, 2023).
Amidst the challenges, bilingual educators should integrate AI technology into their teaching practices, as the benefits of enhancing language learning and supporting emergent bilingual students outweigh the obstacles. It can help foster fair and more inclusive lingual education experiences for emergent bilingual students.
IDRA provides practical workshops on using AI tools in the classroom. If you would like to know more or to schedule a session at your school, email lizdelia.pinon@idra.org.
Resources
Bojorquez, H., & Martínez Vega, M. (May 2023). The Importance of Artificial Intelligence in Education for All Students. IDRA Newsletter.
NCES. (2023). English Learners in Public Schools. Condition of Education 2023. U.S. Department of Education, National Center for Education Statistics.
Office of Educational Technology. (May 2023). Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations. U.S. Department of Education.
Peters, K. (April 9, 2024). Texas schools are using artificial intelligence to grade standardized tests. Experts call it ‘alarming.’ Texas Tribune.
Wei, L. (2023). Artificial Intelligence in Language Instruction Impacts English Learning Achievement, L2 Motivation, and Self-Regulated Learning. Frontiers in Psychology, 14, 1261955.
Lizdelia Piñón, Ed.D., is an IDRA education associate. Comments and questions may be directed to her via email at lizdelia.pinon@idra.org.
[© 2024, IDRA. This article originally appeared in the April edition of the IDRA Newsletter. Permission to reproduce this article is granted provided the article is reprinted in its entirety and proper credit is given to IDRA and the author.]
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