TELT Research Group 

• Ghaida Alrawashdeh, PhD - PI

• Emily McNamara, MATSE English Student - GA

• Qingxian Wang, PhD Student - GA

• Xunmei Luo, PhD Student - GA

• Rebecca Vornberger, PhD Student

Alumni

• Danielle Alexander, PhD Candidate 

• Yujie Mao, PhD Student - GA

• AI Interpretations of Adolescent Writing

This project aims to examine how widely used LLMs score adolescent-authored writing when demographic indicators are either included or withheld. Rather than focusing solely on numeric scores, we analyze the accompanying natural language rationales LLMs provide to understand how identity cues may shape the values and interpretations applied to student writing.

• Transforming early literacy with AI-driven personalized content assessment and feedback

 This Project explores how AI-driven personalized and adaptive learning (PAL) tools can transform early literacy instruction by tailoring content, assessment, and feedback to meet the unique needs of each student. AI tools provide real-time insights that help educators move beyond traditional assessment methods and deliver customized feedback that supports individual growth and development. By focusing on the design of these AI tools, we discuss how they can be leveraged to create more inclusive, efficient learning experiences. We also examine how personalized content delivery and assessment tools can support differentiated instruction and foster a growth mindset in young learners. We present a set of ethical guidelines to ensure safe and equitable AI applications, including considerations around bias and privacy. Drawing on current AI products, the chapter uses case studies to illustrate how these tools are being designed and deployed to support personalized learning, assessment, and feedback in preschool and primary settings.