Combining structured planning templates with closed artificial intelligence systems can transform standards-aligned instructional planning for English learners, significantly reducing educators' time spent on administrative tasks while enabling more focused, collaborative instructional design that addresses both early literacy and disciplinary literacy needs.
Objective
The primary goal of this study is to address the complex challenge of integrated instructional planning for the more than five million K-12 English learners (ELs) in the United States. The author aims to provide Georgia educators with research-based guidance and practical planning templates for designing concise, standards-aligned unit goals and lesson objectives that seamlessly integrate three distinct types of educational standards: English language development (ELD), foundational literacy, and academic content standards in English language arts, mathematics, science, and social studies. Additionally, the study introduces an innovative artificial intelligence workflow using closed-AI systems to significantly reduce the burden of manually aligning multiple sets of educational standards, thereby empowering educators to shift from time-consuming administrative tasks to more meaningful collaborative instructional design.
Methods
The article employs a Georgia-specific case study approach to demonstrate the integration of multiple K-12 instructional standards frameworks: Georgia Standards of Excellence (GSE) for content areas, the WIDA ELD Standards Framework, and Georgia's newly revised K-12 ELA Literacy Foundations Standards. The methodology follows a backward design approach informed by WIDA implementation guidance, using standards-aligned unit goals to drive focused, measurable lesson objectives.
The study introduces a closed artificial intelligence system—specifically Google NotebookLM—limited to pre-vetted, standards-based documents to support consistent, efficient generation of unit goals and lesson objectives. This approach involves uploading vetted source documents including state academic standards, WIDA Language Expectations, state correspondence mappings that connect content standards to language standards, and relevant instructional planning templates. The author provides detailed step-by-step guidance for educators to use this closed-AI system, including sample prompts designed to generate integrated unit goals and three types of lesson objectives: discipline-specific academic language expansion lessons, focused language study lessons, and ELD-embedded foundational literacy lessons.
The research draws upon established theoretical frameworks including the Sheltered Instruction Observation Protocol (SIOP), Universal Design for Learning (UDL) principles, and genre-based pedagogy rooted in Systemic Functional Linguistics. These frameworks emphasize multimodal and linguistic scaffolding to make academic content accessible while promoting English language development.
Key Findings
The article presents several significant findings regarding integrated instructional planning for English learners:
Standards Integration Framework: The study successfully demonstrates how educators can create integrated unit goals that embed ELD standards within content area contexts, specifically tied to WIDA Key Language Uses (narrate, inform, explain, and argue). The framework ensures that ELD instruction is not decontextualized but rather shaped by and embedded within specific content area learning targets.
Three-Tiered Lesson Objective Model: The research validates a comprehensive approach to lesson planning that includes three complementary types of objectives. First, discipline-specific academic language expansion lessons target prominent language functions within grade-level cluster Language Expectations. Second, focused language study lessons provide explicit instruction on language features needed to carry out specific language functions, improving students' metalinguistic awareness. Third, ELD-embedded foundational literacy lessons use a dual-target model to ensure English learners develop literacy skills while building knowledge about how language works for particular genres.
AI-Enhanced Planning Efficiency: The closed-AI system approach demonstrates significant potential for streamlining the complex task of aligning multiple standards frameworks. By restricting the AI's knowledge base to vetted documents, the system reduces risks of confabulation (fabricated or inaccurate information) while automating routine administrative tasks. The AI-generated sample unit for Grade 1 Social Studies ("American Heroes") successfully integrates content standards, language expectations, and foundational literacy skills across a three-week instructional sequence.
Collaborative Planning Support: Research cited in the article indicates that greater attention to collaborative, integrated planning produces statistically significant gains in English learners' vocabulary knowledge, argumentative writing, content comprehension, and oral language proficiency. The AI-supported approach gives educators more time for these meaningful collaborative planning conversations by handling time-consuming standards retrieval and alignment tasks.
Implications
The findings contribute significantly to the field of AI in education, particularly for multilingual learner instruction:
Addressing Persistent Achievement Gaps: The integrated planning approach directly addresses the needs of two vulnerable EL subgroups: early elementary students (especially those with special education needs) who show slower early reading growth compared to non-EL peers, and long-term English learners (LTELs) in middle school who may plateau in oral proficiency but lag in disciplinary literacy development.
Practical Implementation Pathway: The study provides educators with actionable tools and templates grounded in federal requirements (Every Student Succeeds Act) and decades of established ELD research. The standards correspondence mappings created by expert educator panels offer practical options for connecting content and language standards, satisfying both federal peer review requirements and local planning needs.
Responsible AI Integration: By emphasizing closed-AI systems limited to vetted resources, the study models responsible AI use in education that maintains educator agency and oversight. This approach contrasts with uncritical reliance on open-access AI that may expose users to bias, outdated information, or fabricated sources. The framework positions AI as a tool to enhance rather than replace professional educator judgment.
Scalability and Flexibility: While focused on Georgia, the templates and AI workflow can be adapted to other states' standards frameworks. The approach allows local educators flexibility to design or select curricula that meet their students' specific needs while aligning with state-level expectations, supporting the principle of local control in education.
Limitations
The study acknowledges several limitations:
Limited Empirical Validation: The article presents a conceptual framework and case study demonstration rather than empirical research with student outcome data. While grounded in existing research on content-based language learning and integrated instruction, the specific AI-enhanced planning approach has not been tested through controlled studies measuring its impact on educator practices or student achievement.
Technology Access and Training Requirements: Successful implementation requires educators to have access to closed-AI systems, technical literacy to use these tools effectively, and training to thoughtfully prompt, evaluate, and customize AI outputs. These prerequisites may create barriers in under-resourced schools or districts.
Context-Specific Focus: The Georgia-specific case study, while providing a detailed model, may not directly transfer to states using different ELD standards frameworks or content standards. Additionally, the sample focuses on elementary grade levels, with less attention to secondary implementation.
AI Variability: The study acknowledges that AI large language models determine responses based on probability, meaning their output is inherently variable rather than fixed. Even slight changes to prompts or updates to the LLM can produce different results, requiring ongoing educator review and refinement.
Scope of Instructional Planning: The templates address unit goals and lesson objectives but do not comprehensively cover all aspects of instructional planning, including detailed activity design, assessment development, differentiation strategies, or culturally responsive pedagogy considerations.
Future Directions
The author identifies several important areas for future research:
Empirical Impact Studies: Pilot studies should examine how AI tools affect the quality and efficiency of collaborative planning among content, language, and literacy educators. Research should measure the impact on educators' planning time, the nature of their collaborative conversations, and the quality of resulting goals, objectives, activities, assessments, curricula, and scaffolding. Critically, studies must investigate whether AI-enhanced planning ultimately improves English learner outcomes.
Long-term Implementation Research: Future research should explore the sustained use of AI-enhanced planning tools across multiple school years and in diverse educational settings. Studies involving larger, more diverse participant groups would strengthen the evidence base.
Expanded Scope: Research should extend beyond unit goals and lesson objectives to investigate AI support for developing comprehensive curricula, including detailed activity sequences, formative and summative assessments, differentiation strategies, and culturally responsive instructional materials.
Secondary Education Focus: While this study emphasizes elementary grades, future work should specifically address the disciplinary literacy needs of long-term English learners in middle and high school contexts, where content becomes increasingly specialized and linguistically complex.
Technology Refinement: Additional research could focus on refining AI tools to better support specialized instructional needs, including integration of Universal Design for Learning principles, culturally and linguistically responsive pedagogy, and specialized instruction for English learners with disabilities.
Iterative Development Cycle: The author emphasizes the need for an iterative cycle of research and refinement to continuously improve both AI-enhanced planning practices and English learner learning outcomes, suggesting ongoing collaboration between researchers, technology developers, and practitioners.
Title and Authors: "AI-Powered, Integrated Unit Goals and Lesson Objectives for K-12 English Learners" by Lynn Shafer Willner, WIDA at the University of Wisconsin—Madison.
Published on: October 2025
Published by: GATESOL Journal, Volume 34, Issue 1, pages 17-34