The TETL Research Group at Oakland Univerity explores how digital tools and AI can improve teaching and learning across educational settings. We study how technology can make instruction more engaging, personalized, and effective, while supporting teachers in designing meaningful learning experiences. Our work combines research on pedagogy, instructional design, and learning analytics to understand how technology can enhance learning outcomes and foster critical thinking, collaboration, and creativity among students.
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Members
Members
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
Yujie Mao, PhD Student - GA
Current Projects
Current Projects
- AI, Ethics & Human Development
This project looks at ethical questions around artificial intelligence, especially in the context of young people’s lives. At a broad level, we are interested in what it actually means to build and use AI systems in ways that are fair, responsible, and attentive to the people they affect. One part of the project focuses on young children and the growing presence of AI in their everyday experiences. Rather than treating children as an afterthought, this work asks how AI systems could be designed and governed with their needs, rights, and ways of understanding the world in mind. It looks at how children at different ages interact with AI, where current protections fall short, and what it would take to take their perspectives seriously. Another part of the project focuses on adolescents and the use of AI to evaluate student writing. In this work, we examine not just the scores AI systems produce, but how they interpret and explain students’ writing. We are especially interested in whether those interpretations shift when information about a student’s background is included or left out, and what that might reveal about bias and the kinds of writing that are valued. These two strands explore how AI is already shaping educational experiences and why it is important to think carefully about how these systems are designed, used, and evaluated.
- AI and Young Children: The Rights We Owe Them
This project looks at what it would mean to take young children seriously in how AI systems are designed and governed. It focuses on how children at different stages of development experience AI in distinct ways, and why current approaches often miss those differences. Drawing on research with children, developmental science, and policy work, the project explores how children’s rights and perspectives can be more meaningfully reflected in the systems that increasingly shape their everyday lives.
Related Publications
Related Publications
Alrawashdeh, G. S., & McAskin, M. (in preparation). AI and young children: The rights we owe them. In S. Papadakis (Ed.), AI and child development: How cognition, emotion, and identity evolve. John Wiley & Sons.
- 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.
Presentations:
Presentations:
Beyond the Score: Demographic Influence on AI Justifications in Writing Assessment (2026). American Educational Research Association (AERA), Los Angeles, USA.
Fairness vs. Overcorrection: Investigating Input Effects in AI-Powered Writing Assessment (2026). American Educational Research Association (AERA), Los Angeles, USA.
Fairness and Consistency in AI Writing Assessment: The Impact of Demographic Inputs and Rubric-Based Scoring(2026). Conference of the Comparative and International Education Society (CIES), San Francisco, USA.
Feedback or Filtering? AI and the Reproduction of Rhetorical Norms(2026). Conference of the Comparative and International Education Society (CIES), San Francisco, USA.
- 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.
Related Publications
Related Publications
Vornberger, R., Alrawashdeh, G. S., & Alexander, D. (2026). Transforming Early Literacy With AI-Driven Personalized Content Assessment and Feedback: A Review of Six Tools. In S. Papadakis (Ed.), Virtual Tutors and AI-Powered Instructional Tools in K-12 Settings (pp. 155-172). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-2637-5.ch005
Alrawashdeh, G. S., Castillo, N. M. (2025). Student agency in personalized and adaptive learning technologies: From conceptualization to application. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13772-6
Alrawashdeh, G.S., Castillo, N.M. (2025). Responsible AI in Personalized Adaptive Learning: A Global Review of 40 Products. In: Papadakis, S. (eds) AI Roles and Responsibilities in Education. Signals and Communication Technology. Springer, Cham, 171–198. https://doi.org/10.1007/978-3-031-96855-6_8.
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