Advancing Translational Science Through AI-Enhanced Teacher Learning for Early Language and Composing

Composing in early childhood classrooms offers a critical opportunity to strengthen children’s language skills, yet many teachers feel underprepared to provide this instruction. This study examines whether an AI-enhanced digital platform (L4C) can serve as a sustainable, community-based professional development model that bridges theory and practice. Twenty-nine teachers in the southeastern United States engaged with L4C, a professional learning model designed to integrate principles from the Science of Literacy, Learning, and Instruction into a cohesive platform that links teachers’ content and pedagogical knowledge-building with lesson planning and reflective practice. Data sources included surveys, pre- and post-lesson plans, and AI usage logs from the lesson planning tool. Findings showed that teachers initially reported significant barriers to composing instruction and sought professional learning responsive to their classroom needs. After using L4C, teachers demonstrated notable growth in their knowledge of language components and the quality of their composing lesson designs. Teachers evaluated the platform positively, particularly valuing the linked videos and scripted lesson tools for making theoretical concepts actionable. These findings suggest that AI-driven platforms like L4C can advance teacher learning in practical, individualized, and contextually relevant ways, offering a promising pathway for professional development in early literacy instruction.

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Publication Details

Journal
Education Sciences
Published
2025-11-05
DOI
https://doi.org/10.3390/educsci15111496
Primary Topic
Child Development and Digital Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

Advancing Translational Science Through AI-Enhanced Teacher Learning for Early Language and Composing

JeanMarie Farrow, Chenyi Zhang, Michael James Farrow
Education Sciences
Child Development and Digital Technology
article

Advancing Translational Science Through AI-Enhanced Teacher Learning for Early Language and Composing

JeanMarie Farrow, Chenyi Zhang, Michael James Farrow
article en

Abstract

Composing in early childhood classrooms offers a critical opportunity to strengthen children’s language skills, yet many teachers feel underprepared to provide this instruction. This study examines whether an AI-enhanced digital platform (L4C) can serve as a sustainable, community-based professional development model that bridges theory and practice. Twenty-nine teachers in the southeastern United States engaged with L4C, a professional learning model designed to integrate principles from the Science of Literacy, Learning, and Instruction into a cohesive platform that links teachers’ content and pedagogical knowledge-building with lesson planning and reflective practice. Data sources included surveys, pre- and post-lesson plans, and AI usage logs from the lesson planning tool. Findings showed that teachers initially reported significant barriers to composing instruction and sought professional learning responsive to their classroom needs. After using L4C, teachers demonstrated notable growth in their knowledge of language components and the quality of their composing lesson designs. Teachers evaluated the platform positively, particularly valuing the linked videos and scripted lesson tools for making theoretical concepts actionable. These findings suggest that AI-driven platforms like L4C can advance teacher learning in practical, individualized, and contextually relevant ways, offering a promising pathway for professional development in early literacy instruction.

Education SciencesVol. 15(11)
Georgia State University (US), Rowan University (US)
Openalex Percentile: Top 65%
Child Development and Digital Technology
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Advancing Translational Science Through AI-Enhanced Teacher Learning for Early Language and Composing — JeanMarie Farrow, Chenyi Zhang, et al. · Education Sciences (2025) | TGRS Research Map | TGRS