FILL the Future: Personalizing Language Learning with AI
Ryan Allen, Delmar School District (United States)
Tom Welch, National Council of State Supervisors for Foreign Languages (United States)
Abstract
Facilitated Interdependent Language Learning (FILL) is a proficiency-centered approach to world language education that replaces pacing guides and teacher-directed curricula with a flexible system organized around what learners can do with language. In a FILL classroom, instructional authority shifts decisively toward the learner: students choose the language and topics they wish to study, set proficiency-based goals, select resources, and reflect on their progress, while the certified teacher operates as a facilitator who designs the conditions for language acquisition rather than directing daily learning. Graduation credit is earned through demonstrated proficiency rather than seat time, positioning learners as accountable agents in their own growth. Students function as autonomous yet interdependent learners, articulating proficiency targets through Can-Do Statements (ACTFL, 2017) aligned with national proficiency frameworks (ACTFL, 2024). Proficiency serves as both the organizing principle for learning and the primary metric for assessment, prioritizing communicative ability over content coverage. Artificial intelligence plays a central, transformative role by enabling personalization at scale: learners use AI to generate level-appropriate texts, design individualized practice, explore linguistic structures, and engage in written and spoken interaction aligned to their goals and interests. Positioned not as a shortcut but as an adaptive learning partner, AI supports exploration, strategy development, and iterative refinement. Rigor and transparency are ensured through externally benchmarked proficiency assessments (e.g., Avant’s STAMP test), providing independent validation of growth and allowing proficiency to carry institutional meaning beyond the classroom. This session presents FILL as a transferable framework with implications extending beyond world language education, demonstrating how learner-centered, AI-supported, proficiency-based systems can expand access to less commonly taught and heritage languages without adding staffing or programs. For underresourced schools in particular, FILL offers a sustainable model for broadening opportunity, preserving rigor, and redistributing power toward learners while maintaining accountability at scale.
Keywords: Facilitated Interdependent Language Learning; AI-Assisted Language Learning; Language Access; Learner Autonomy
REFERENCES:
American Council on the Teaching of Foreign Language. (2017). NCSSFL-ACTFL can-do statements. ACTFL, https://www.actfl.org/educator-resources/ncssfl-actfl-can-do-statements
American Council on the Teaching of Foreign Languages. (2024). Proficiency Guidelines 2024. ACTFL. https://www.actfl.org/uploads/files/general/Resources-Publications/ACTFL_Proficiency_Guidelines_2024.pdf
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