Human-Technology Collaboration Cross-Disciplinary Team

From getting customized news headlines on our phone or individualized learning content in online courses, to using ride sharing apps for getting to work or guiding blind people as they run marathons, intelligent technologies (derived from data sciences, machine learning, predictive analytics, artificial intelligence, natural language processing, and other advances) are transforming our daily lives. Recognizing this continued expansion of technology and computational thinking in our classrooms and workplaces, the George Washington University is offering a dynamic cross-disciplinary PhD in Human-Technology Collaboration. Drawing upon faculty and experts from education, data science, engineering, psychology, business, public health, and medical informatics, the PhD takes an interdisciplinary approach to education and research into how the collaborations of people and machines shape the future.

Being prepared to create, train, interact, and collaborate with intelligent technologies is an immediate challenge in the preparation of the global workforce. All of us must develop new skills and effective strategies to ask the right questions, interpret data analytics, apply data to improving performance, assess machine uncertainty, make ethical and policy judgments that integrate both data and social values, and find new collaborative ways that engage technologies as our partners in learning and work.

Preparing for data intensive environments powered by intelligent technologies requires research-based approaches to answering these and other questions:

  • How can educational institutions and private sector organizations collaborate in preparing a workforce for increasing collaborations with intelligent technologies?
  • How do varying levels of algorithm transparency influence the application of those algorithmic outputs in the decisions being made in the workplace?
  • What factors lead to faster and deeper learning in virtual reality training environments?
  • Which grounded instructional strategies are most effective for teaching critical and computational thinking skills to students of all ages?
  • How can data visualization improve the efficiency of professionals (e.g., lawyers, teachers, nurses, engineers, school administrators) in interpreting predictive data outputs?
  • What variables do people consider when trusting machines that make mistakes, or when machines are not certain about their uncertainty?
  • What are the policy and ethical implications of algorithm design, transparency, and accountability within a learning analytics context?
  • Is there a balance of human intuition and machine learning automation that best facilitates creative design while also fully leveraging analytical capability?
  • How do team dynamics and collective intelligence change when intelligent machines are team members?

Join our Cross-Disciplinary Research Team (CRT) and the vanguard exploring the frontiers of Human-Technology Collaboration. Learn more about news and other related events regarding the program by visiting GW's Human-Technology Collaboration Research Lab website.



Admissions Requirements


Master’s degree in a field relevant to cross-disciplinary study and research in the area of Human-Technology Collaboration.


Official transcripts from every institution attended whether or not a degree was completed; graduate and undergraduate. The CRT goal is an average GPA of at least 3.7 in previous undergraduate and graduate programs.

Standardized Test Scores

GRE is optional. Official GRE Test scores not older than five years. The CRT goal is approximately 70th percentile on GRE-Verbal and GRE-Qualitative. International students must also submit TOEFL scores not older than two years. TOEFL score minimum for admission is 100 on the Internet-based or 600 paper-based; IELTS of 7.0. The institutional code is 5246.

Recommendations Required

Three (3) letters of recommendation, with one preferred from a professor in the applicant’s Master’s degree program. Letters will document potential for analytical thinking, research skills/experiences, scholarly writing capabilities, and capacity to explore cross-disciplinary/complex issues.

Statement of Purpose

An essay of less than 1200 words, in which the candidate states his/her purpose in undertaking cross-disciplinary graduate study including: (a) rationale for seeking a Ph.D. in the specified cross-disciplinary research focus; (b) articulation of personal research interests; and (c) how his/her background and related qualifications have prepared him/her for this work and will align with long term goals. Please list your specified CRT at the top of your statement of purpose.

Curriculum Vitae

Current curriculum vitae.

Writing Requirement (Optional)

Candidates are encouraged to submit a current writing sample. The sample should reflect the candidate’s abilities to articulate complex ideas and to utilize evidence in support of his/her arguments. The writing sample should also provide an example of the candidate’s research skills, as well as her/his engagement with scholarship in pursuing his/her research interests.


Interview by faculty to include a scholarly discussion of how the candidate’s work will fit with the proposed topic of the CRT.

Application Deadline

Please contact if you are interested in applying.

If you have questions about submitting your application, please contact our Admission Office at or at 202.994.9283.



Required courses in Educational Foundations (12 credits)

SEHD 8100 Experimental Course/ Foundations of Education I and II (taken twice; 3 credits each time)
SEHD 8100 Experimental Course/Pro-Seminar (taken twice: 3 credits each time)

Research (12 credits)

EDUC 8120 Group Comparison Designs and Analyses (3 credits)
EDUC 8122 Qualitative Research Methods (3 credits)
6 credits from the following:
EDUC 8130 Survey Research Methods (3 credits)
EDUC 8131 Case Study Research Methods (3 credits)
EDUC 8140 Ethnographic Research Methods (3 credits)
EDUC 8142 Phenomenological Research Methods (3 credits)
EDUC 8144 Discourse Analysis (3 credits)
EDUC 8170 Educational Measurement (3 credits)
EDUC 8171 Predictive Designs and Analyses (3 credits)
EDUC 8172 Multivariate Analysis (3 credits)
EDUC 8173 Structural Equation Modeling (3 credits)
SEHD 8100 Experimental Course/(Advanced research method) (3 credits)
Or other research and analysis courses outside of GSEHD approved by the advisory committee and the instructor

Cross-disciplinary concentration (24 credits)

Graduate-level courses determined in consultation with the advisor at the time of admission. Course selections are determined by the focus of the cross-disciplinary research team and the specific interests of the student.

Dissertation research (12 credits)

SEHD 8999 Dissertation Research

The successful completion of:

  • Second-year research project
  • Comprehensive examination
  • Oral dissertation proposal defense
  • Dissertation
  • Dissertation oral defense


Open Science

The CRT is dedicated to the principles and practice of open science for accelerating scientific progress. The CRT supports the development of research and analysis that is widely available for peer feedback and potential replication or reproduction. The sharing of collaborative and student research is encouraged at all phases from conceptualization and design to publication and dissemination. Replication studies, preregistration of research proposals, open sharing of data and code, and the preprinting of publications are among the tools used within the CRT to open our science.



Corry, Michael Professor, Educational Technology
Milman, Natalie B. Professor, Educational Technology
Nakamura, Yoshie Tomozumi Assistant Professor, Human and Organizational Learning
Scully-Russ, Ellen Associate Professor, Human and Organizational Learning
Watkins, Ryan Professor, Educational Leadership

College of Professional Studies

Hooshangi, Sara

Associate Professor; Program Director, Integrated Information, Science and Technology

(202) 994-1692

Columbian College of Arts and Sciences

Behrend, Tara

Associate Professor, Industrial-Organizational Psychology

(202) 994-3789

Medsker, Larry

Professor, Physics ; Research Professor, Education


Sarah Shomstein

Professor, Cognitive Neuroscience


Milken Institute School of Public Health

Helmchen, Lorens

Associate Professor, Health Policy and Management

(202) 994-3816

School of Business

Hill, Sharon

Associate Professor of Management

(202) 994-1314

School of Engineering and Applied Sciences

Barba, Lorena

Associate Professor, Mechanical and Aerospace Engineering

(202) 994-3715

Broniatowski, David

Assistant Professor, Engineering Management and Systems Engineering

(202) 994-3751

School of Medicine and Health Sciences

Morizono, Hiroki

Associate Research Professor, Genomics and Precision Medicine

(202) 476-6029



Learn more about the Cross-Disciplinary Doctorate in Human-Technology Collaboration program located on campus (202-994-3023).