"Postdoctoral Associate - National Tutoring Observatory"

Description

The National Tutoring Observatory (NTO) is based in Cornell's Ann S. Bowers College of Computing and Information Science under the direction of Principal Investigator Rene Kizilcec. The NTO is a collaboration among Cornell University, Carnegie Mellon University, and the Massachusetts Institute of Technology. The NTO works closely with tutoring providers, school and district leaders, and education researchers to create the world's largest open-access dataset of tutoring (the Million Tutoring Moves [MTM] dataset), along with open-source applications that support data processing and analysis grounded in Responsible AI principles. A core part of the NTO's mission is to develop open data and computational resources for the broader education research and developer community.

The Postdoctoral Associate will participate in cross-disciplinary research and development projects related to learning engineering and AI in education, working with a team of postdoctoral researchers, PhD students, and Master's/undergraduate researchers across multiple universities and organizations. The research team collaborates closely with an industry-leading engineering team at FreshCognate, which supports tooling for data processing, annotation, and analysis. The research team works closely with NTO engineers to investigate the validity of data-processing workflows, evaluate effective tutoring moves using the MTM dataset, and share innovations and best practices for analyzing tutoring data at scale.

NTO research encompasses (but is not limited to) identifying effective tutoring practices, running experiments on AI tutors in simulated and real-world environments, fine-tuning AI models to classify qualitative data, and building generative AI pipelines to process multimodal data for downstream analysis. Applicants with experience in some--but not necessarily all--of these areas are encouraged to apply.

The Postdoctoral Associate will have opportunities to engage in ongoing collaborations with research groups such as PLUS at Carnegie Mellon University, the SCALE Initiative at Stanford, and the Penn Center for Learning Analytics at the University of Pennsylvania--each of which is advancing cutting-edge work at the intersection of AI and education. The NTO also maintains active partnerships with leading educational technology companies and tutoring providers (e.g., Carnegie Learning, Eedi, TeachFX, Third Space Learning) that support data acquisition and applied research. The research team has access to more than 100 generative AI models through Cornell's AI gateway and high-performance computing resources via Empire AI. This position provides opportunities to collaborate with leading AI companies, such as Anthropic and Alphabet, present at education and AI conferences, and publish in top-tier journals.

The NTO seeks applications for a Postdoctoral Associate who can start at least part-time as early as February 2026. Applications will be reviewed on a rolling basis, and candidates seeking a later start date (up to May 2026) will also be considered. Selected postdocs will work with a primary mentor on projects at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning, and education research.

Research Role

Research themes for the NTO Postdoctoral Associate include, but are not limited to:

• Developing or evaluating methodologies for processing, cleaning, annotating, and privacy-protecting large-scale tutoring multimodal datasets to generate actionable insights.
• Experimental or quasi-experimental analysis of effective tutoring moves that support student motivation, engagement, and learning.
• Design and evaluation of AI tutoring agents leveraging high-quality tutoring data (e.g., training tutors directly on tutoring data; exploring a wide range of fine-tuning and adaptation strategies for foundation models).
• Other relevant research directions, as proposed by the applicant.

Strong applicants will bring depth in some of the following areas (not all are required):

• Large-scale data analysis and learning analytics methods
• Experimental or quasi-experimental design; validity and measurement
• Working with LLMs, including prompting, fine-tuning, or evaluation
• Machine learning or NLP for classification, prediction, or multimodal data processing
• Experience with annotation tools, data pipelines, or AI-assisted labeling workflows
• Strong research communication skills and a record of scholarly publication
• Background in tutoring, learning sciences, or educational technology
• Commitment to Responsible AI and privacy-preserving data practices

Mentors include Rene Kizilcec (primary mentor, Cornell University), Kirk Vanacore (Cornell University), Ken Koedinger (Carnegie Mellon University), and Justin Reich (Massachusetts Institute of Technology). Additional mentorship will be provided by members of the NTO research team and a National Advisory Board. The NTO also collaborates with leading research groups such as the Stanford SCALE Initiative and EdTech partners.

Expectations

Postdocs are expected to integrate fully into the NTO's core research team and contribute actively to the technical development, research, and dissemination efforts of the Observatory. Responsibilities include:

• Serving as a domain expert in one or more of the research themes above.
• Participating in research and cross-team meetings, design sessions with developers and tutoring providers, and discussions with scientific advisors.
• Publishing in high-impact scientific journals and presenting at conferences.
• Attending the NTO's annual in-person convening.
• Conducting research that is directly linked to NTO priorities or closely related work that can be translated into the NTO ecosystem.
• Contributing to use-inspired basic and applied research with a strong AI component, grounded in real-world education applications.

Faculty mentors will engage closely with their postdoc, providing research guidance, professional development, and career support. Each postdoc will work with their mentor to develop and update an Individual Development Plan.

This is a hybrid position with the possibility of remote work. Some travel will be required.

Application Process

Postdoctoral researchers will work under the supervision of Cornell faculty members Rene Kizilcec and Kirk Vanacore in the Cornell University Department of Information Science, along with senior members of the NTO. We encourage applicants with terminal degrees from various disciplines, including but not limited to Information Science, Computer Science, Learning Science, Statistics, Economics, Psychology, Linguistics, and Sociology.

Applicants must submit an up-to-date CV, transcript, three references, and a research statement that clearly articulates their prior research experience and future interests, including their relevance to tutoring data infrastructure and student outcomes. Please articulate your expected alignment with the research themes and goals of the NTO.

Files to submit:

• Applicant's CV
• Applicant's transcript showing proof of PhD; or intended completion date, which must be confirmed in a separately submitted letter from the current PhD advisor (can be in a reference letter)
• Research statement
  • Maximum 3 pages, 11-point font, 1-inch margins, single spaced
  • Will be reviewed according to the evaluation criteria listed in the Application Process section
  • Applicants are encouraged to highlight connections between their work and the research themes listed above and the goals of the NTO
• Names and emails of three (3) references who will be invited to submit reference letters separately

Applications will be evaluated based on:

• Intellectual merit and scientific excellence
• Potential to advance AI for tutoring
• Cross-disciplinary integration
• Broader societal impact

A review panel will evaluate applications. A short list of applicants will be invited for a Zoom interview. Applications will be reviewed on a rolling basis until the position is filled. The projected start date is Spring 2026.

Pay Ranges: $62,232 - $88,745