React-E | Nebraska

A UNL-led multi-institutional consortium comprising of six partner institutions focused on Computing, Technology, and Engineering Education research.

Engineering the Future of AI-Responsible Teaching and Learning

The Researching Ethical AI in Computing, Technology, and Engineering (REACT-E) research lab explores the challenges and opportunities that Generative AI brings to engineering education. Our mission is to generate research that informs engineering education so it we can keep pace with the shifting landscape of AI.

REACT–E Team

Tareq Daher

Assistant Professor of Practice, Engineering Education Director, Engineering and Computing Education Core

Tareq Daher earned a B.S. in Computer Science from the University of Mutah (Jordan) and an M.A. and Ph.D. in Educational Studies from the University of Nebraska–Lincoln (UNL). He serves as Director of the Engineering and Computing Education Core (ECEC) and Assistant Professor of Practice in Engineering Education at UNL, where he leads strategic initiatives to advance engineering and computing education across the College. Dr. Daher’s work centers on transforming engineering and computing education through integrated approaches that span first-year student success, co-curricular program design, and faculty scholarship in engineering education, and graduate student teaching development. He collaborates closely with faculty to support the development and dissemination of research, leads college-wide assessment and data analytics efforts, and manages interdisciplinary teams that drive data-informed decision-making and continuous improvement. He is the co-founder of the REACT-E Lab (Researching Ethical AI in Computing, Technology, and Engineering), which investigates the opportunities and challenges of generative AI in engineering education to advance ethical, responsible, and pedagogically sound integration of AI.

Guy Trainin

Professor

Guy Trainin is a Full Professor in the Department of Teaching, Learning and Teacher Education at the University of Nebraska-Lincoln, where he studies how people learn with and through emerging technology, most recently generative AI. His research asks practical questions about what it means to be AI-fluent in an era when the tools are changing faster than the curricula designed to teach them. Trainin brings to REACT-E a record of empirical work at the intersection of AI, learning, and educator preparation. With colleagues in engineering and across disciplines, he has examined how faculty understand and integrate generative AI into instruction, how AI-generated texts perform against human-authored ones in K-12 contexts, and what fluency with AI actually requires of learners at different stages. Trainin co-hosts the podcast Azadeh and Guy on AI, writes the Substack GuyonAI, and has delivered keynotes on AI literacy for international audiences. His research has been supported by more than $19.9 million in external funding, and he has mentored more than 30 doctoral students to completion. He holds a Ph.D. from the University of California, Riverside.

Azadeh Hassani

Graduate Research Asst

Azadeh Hassani is a Ph.D. student at the Department of Teaching, Learning and Teacher Education at the University of Nebraska-Lincoln, majoring in Innovative Learning Technologies. Her research interests are Technology-Assisted Learning, AI literacy, and Teacher Education.

Jordan Wheeler

Lecturer/T

As a quantitative methodologist and psychometrician, I am interested in the development and improvement of quantitative methodologies and tools. I have a strong background in applying and exploring the utility of diverse statistical techniques within educational and psychological contexts. My research focuses on the application and development of statistical and psychometric models to analyze data from educational assessments. I am particularly interested in the use of statistical and machine learning techniques to analyze textual and process data collected from educational assessments. My recent research has focused on the development of methodologies that integrate results from textual data analyses into a variety of psychometric models, including item response theory and diagnostic classification models. This research aims to investigate how textual data analyses can inform educators and test developers about writing strategies students use on writing assessments in order to improve the validity and fairness of educational assessments that contain constructed-response items.