Tugce Aldemir, Ph.D.
Department of Teaching, Learning & Culture · Texas A&M University

Tugce Aldemir, Ph.D.

Assistant Professor of STEM Education · CoHALE Lab

I design and study AI- and emerging technology-supported learning ecologies that make disciplinary computational practices and high-quality collaboration teachable, observable, and sustainable in everyday classrooms and in teacher preparation and professional development.

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Portrait of Tugce Aldemir

News & highlights

Feature · Aug 2026
Texas A&M CEHD highlights NSF CAREER project
A new College of Education & Human Development story highlights the 3AforCT project and its work with rural middle school students.
Read the story →
Publication · 2026
New paper accepted at Interactive Learning Environments
A five-gate diagnostic framework for screening whether process data from virtual makerspaces holds up as evidence of computational thinking.
Publication · 2026
New paper accepted at Cogent Education
Challenges, safeguards, and professional learning needs for AI integration, from a two-week AI literacy module with preservice teachers.
Grant · 2026
New NSF award ($2M, Co-PI, 2026–2031)
AI-driven professional learning to strengthen preservice teachers' content knowledge, pedagogical content knowledge, and mathematical creativity.

About

I am an Assistant Professor of STEM Education (Technology Education emphasis) in the Department of Teaching, Learning & Culture at Texas A&M University, and Director of the CoHALE Lab (Collaborative Human-AI Learning Ecologies in STEM+C). My research program is grounded in the learning sciences and sits at the intersection of design, collaboration, and computing. I design and study learning ecologies in which students and teachers develop disciplinary computational practices and high-quality collaboration across classrooms, teacher preparation, and professional development. Building on my longstanding work in computer-supported collaborative learning, I increasingly investigate AI agents as participants in these ecologies and how their designed roles shape reasoning, learning, and agency.

I co-design with teachers and study enactment in authentic settings through design-based research with mixed methods. Across projects, I operationalize models of collaborative and computational competence through learning designs, scaffolds, and analytic tools, using evidence of participants’ activity to examine what those models capture or miss and to refine both the designs and theoretical accounts of practice. I hold a Ph.D. in Learning and Performance Systems with an emphasis in Learning Sciences from The Pennsylvania State University and completed postdoctoral work at the University of Pennsylvania and the University of Connecticut.

Three research strands

Funded projects →

The strands run through the whole program rather than dividing it, so individual projects often work across more than one.

Disciplinary computational practices
Disciplinary computational practices
How computational thinking, data practices, and AI literacy are learned, enacted, and assessed within disciplines. I study how these practices take their shape from disciplinary problems and forms of reasoning, from the data an agronomist interprets to the judgments a teacher makes about an AI tool, and how students and teachers develop them outside computer science classrooms.
High-quality collaboration
High-quality collaboration
How collaborative competence develops, what models of collaborative quality capture or miss, and how participation and trust shape collaborative learning and co-design. I study these questions across student teams, human-AI interaction, and teacher-researcher-student co-design.
AI agents as participants in learning ecologies
AI agents as participants in learning ecologies
How the designed roles of AI agents shape reasoning, learning, and agency within collaborative and disciplinary activity. I investigate agents that participate as simulation partners, cognitive partners, and peer mentors, asking what intellectual work they should support and what must remain with learners and teachers.

Projects across the strands

NSF CAREER · PI · 2026–2031
3AforCT: AI-guided Ag-STEM inquiry in rural middle schools
Rural middle school students investigate problems in their school gardens using sensors and data across the inquiry cycle. An AI peer mentor supports teams' disciplinary reasoning and inquiry while teachers co-design the learning ecology they enact in their classrooms.
Disciplinary computational practices High-quality collaboration AI agents as participants in learning ecologies
NSF IUSE · Co-PI · 2026–2031
AI-driven professional learning in mathematics
How preservice teachers and a generative-AI cognitive partner build knowledge together, how initiative and agency are distributed in their interactions, and how that joint work shapes teachers' disciplinary reasoning and professional learning.
AI agents as participants in learning ecologies High-quality collaboration Disciplinary computational practices
Teacher preparation · design studies
XR making and AI literacy in teacher preparation
Designing XR maker experiences and AI-literacy modules through which preservice teachers develop computational and AI practices within their own disciplines while learning with and about emerging technologies.
Disciplinary computational practices AI agents as participants in learning ecologies
CSCL · continuing line of work
Collaborative competence and the technologies that support it
WHow collaborative competence develops and whether learners can recognize and improve the quality of their own joint work, from systems that return collaborative criteria to student groups to chat-based simulation partners for practicing collective sensemaking. This work also examines what prevailing models of collaborative quality capture or miss, particularly when collaboration involves identity and difference.
High-quality collaboration AI agents as participants in learning ecologies
© 2026 Tugce Aldemir · CoHALE Lab · Texas A&M University taldemir@tamu.edu