Overview
Experts in education and technology encourage K-12 public schoolteachers to leverage generative artificial intelligence (AI) tools to enhance multilingual learners’ (ML) linguistic responsiveness and agency, while addressing ethical and access concerns related to AI’s integration through Culturally and Linguistically Relevant Pedagogy (CLRP) frameworks for more inclusive and accessible learning in public classrooms. According to the National Center for Education Statistics (NCES), in fall 2021, English learners (Els) represented 10% or more of public-school students in 13 states with a lower percentage receiving services in English language instruction educational programs (LIEPs) compared to 10 years ago. The evidence shows that generative AI tools such as ChatGPT are beneficial for ML teaching and learning by facilitating multilingual and translingual communication, by deconstructing historical language barriers and perceptions and by bridging the linguistic gaps between multilingual teachers and students in public and public charter schools. Advanced generative AI tools are opening new possibilities for dialogue and cross-cultural exchange of knowledge between students and teachers to deconstruct and challenge existing deficit language and ML perceptions.
Findings
Findings from two studies on the uses of ChatGPT and a preservice and in-service teacher inclusion of CLRP when teaching multilingual learners (MLs) show eight instances of instructional uses of ChatGPT within the CLRP framework dimensions. ML teachers implemented culturally relevant curricula by actively getting to know their students to build a classroom community and drawing on student-centered and community-relevant curricula. They focus on accommodating ML translanguaging (the practice of using multiple languages to communicate and learn) and purposefully structure learning to ensure equitable access; actively challenge deficit language ideologies and ML perspectives in their classrooms; and practice critical examinations of self and society.
Quick Facts
The two primary-level uses for ChatGPT in ML public classrooms
- facilitate communication and
- differentiate content for MLs
The two secondary-level examples included instructional applications of ChatGPT
- structured support for student prompting
- designing an experimental activity to support multimodal literacy
(Donley, 2024)
CLRP Framework Dimensions
- Implementing culturally relevant curricula
- Embracing students' full communicative repertoires
- Dismantling deficit-based perspectives of MLs
- Practicing critical consciousness in teaching and learning
(Wesley-Nero & Donley, 2024)
Translanguaging stance is a student-centered theory of language and pedagogy that challenges notions related to race, culture and linguistic ideologies and learning nuances among culturally diverse MLs.
Policy Takeaways
Engaging with generative AI tools through a culturally and linguistically responsive pedagogical approach can have transformative effects on ML, non-ML and teachers by creating a more equitable and accessible learning environment in which ML cultural capital enhances learning for all in public and public charter schools. Pre-service training and in-service professional development programs can benefit from structured and purposeful uses of generative AI for enhancing learning for ML classrooms. While these studies demonstrate clear benefits of AI integration, the studies emphasize and agree with experts on the need to engage critically with AI tools to address the challenges and ethical considerations associated with the growing use of AI in public education.
Methodology
In Donley (2024), a study between the fall 2022 and spring 2024, in-service ML teachers from Washington, D.C., public schools and public charter schools participated in a professional development course, as part of a larger research initiative known as Project ELEECT, which aimed to expand access to culturally and linguistically responsive pedagogies (CLRP). A total of 61 ML teachers from pre-K to secondary (6-12) education participated, 42 of which were observed (in-person) during at least one full lesson or class period. Semi-structured interviews via Zoom provided rich details and context to expand on the observations of examples of CLRP used in the classroom.
The study draws on data from six preservice teachers enrolled in a teacher residency preparation (TRP) program and 21 in-service teachers participating in a professional development (PD) program. The TRP and the PD were housed in a graduate education program focused on developing and supporting effective, anti-racist educators for multilingual students. Data collection methods included post-observation reflection responses from preservice teachers, observations and post-observation prompts for preservice teachers and semi-structured interviews for in-service teachers.
References
Donley, K. (2024). Teaching with ChatGPT as a linguistically responsive tool for multilingual learners. Technology in Language Teaching & Learning, 6(3), 1719. https://doi.org/10.29140/tltl.v6n3.1719
National Center for Education Statistics. (2024). English Learners in Public Schools. Condition of Education. U.S. Department of Education, Institute of Education Sciences.
Retrieved [December 1, 2024], from https://nces.ed.gov/programs/coe/indicator/cgf.
Wesley-Nero, S. & Donley, K. (2024). Culturally and linguistically responsive pedagogy: Examining teachers’ conceptualization of affirmative instructional practices for multilingual learners. TESOL Journal, 15, e881. https://doi.org/10.1002/tesj.881