Ideas

Harvard Account Sign In

Sign in with your HarvardKey to access my.Harvard Student, Teaching/Advising, Delegate, Harvard Chan, Admissions, and Admin.

Brown, MGH, & Tufts Students

Register for access to participate in Harvard cross-registration.

  1. Course Search
  2. EDU S442Y

Doctoral Practicum for Using Artificial Intelligence Tools in Quantitative Educational Research
EDU S442Y

Course Information

Description

Artificial Intelligence (AI) tools are rapidly changing how we can learn quantitative methods and conduct quantitative research in education. This doctoral practicum is a collaborative, project-oriented space that complements concurrent quantitative methods courses and research. The goal is for students to develop proficiency using AI tools, including large language models and agents, to accelerate and improve their quantitative learning and research.

The course is organized around two related questions. First: How can AI help me teach myself and others? Students will explore how to use AI tools to generate new examples, alternative explanations, simulations, and applications of quantitative methods, going beyond what they have learned in concurrent or recent coursework. Second: How can AI accelerate and improve my research? Students will develop practical workflows for using AI agents and tools across the research process, from data exploration and analysis to interpretation and communication. Transparency, replicability, and ethical use are first-order goals throughout.

In and between class meetings, students will bring challenges from their coursework and research. We will work together to develop, share, and evaluate AI-assisted strategies. Peer teaching, demonstration, and collaborative problem-solving will be the primary mode of learning; direct instruction will be limited. Because the landscape of AI tools is evolving with unusual speed, we will maintain deliberate humility about our knowledge of what is currently possible and appropriate. Critical judgment and adaptability are core competencies alongside technical fluency. Grades will be based on active contribution to the learning community, low-stakes oral exams, and project presentations.

Prerequisites: Concurrent enrollment in or prior completion of S-052 or the equivalent; that is, students must have a foundation in statistics and regression and be enrolled in or have taken a more advanced course (S-052 covers causal inference methods, multilevel data, and measurement). Some prior independent use of large language models is required. Second-year doctoral students and above preferred; first-year students with active research projects or substantial prior quantitative training are welcome with instructor permission. The course operates as sequential modules through the academic year. Students who enroll in S440Y in the fall must enroll in S442Y in the spring; students interested in S442Y in the spring must enroll in S440Y the prior fall.

School Graduate School of Education
Credits 2
Cross Reg

Available for Harvard Cross Registration

Department Education
Course Component Regular Course
Instruction Mode In Person
Subject Education
Grading Basis HGSE Student Option (Letter Graded, Sat/Unsat)
Course Requirements Must be Ph.D. or Ed.L.D or have permission of the instructor
Learning Goals <p>This practicum extends students' data-analytic capabilities by developing practical fluency with AI tools, including large language models and agents, as complements to the quantitative methods proficiency acquired in concurrent and prior coursework. We have designed S440Y and S442Y to contribute to the emerging toolkit that researchers will need to teach themselves new methods independently, accelerate their own quantitative workflows, and contribute productively to a research community navigating a continuously and rapidly changing technological landscape.</p>
Career Focus <p>This practicum supports careers that require both AI literacy and AI fluency in quantitative research settings. Literacy goals include critically evaluating AI-assisted analyses, understanding the capabilities and limitations of current tools, and maintaining transparency, replicability, and ethical responsibility in AI-assisted research. Fluency goals include using AI tools and agents productively across the research process and teaching these approaches to collaborators, students, and research teams. Common next steps include academic research and faculty careers, postdoctoral research trajectories, research and policy think tanks, governmental and intergovernmental organizations, educational technology and data science roles, and the wide array of for-profit and not-for-profit organizations actively integrating AI tools into their quantitative research practices.</p>