Post Doctoral Fellow

Carnegie Mellon University; False Pass (AK); On-site; USD 60,000 - 75,000; Full time

Benefits offered by this job

Competitive salary and benefits

Tuition benefits at CMU

Comprehensive health insurance

Research environment with leading LM/H

Collaborative, fast-scaling project

Job summary

Carnegie Mellon University’s Human-Computer Interaction Institute hosts the PLUS project, aiming to double middle school math learning through hybrid human-AI tutoring. We invite a technology‑oriented postdoctoral fellow to contribute to research on tutoring effectiveness and AI-assisted support tools, advised by Prof.

Ken Koedinger and Dr. Danielle Thomas, with collaboration across CMU, Stanford, and NTO partners.

Qualifications

• PhD in Cognitive Science, Learning Science, Computer Science, Information Systems, or related fields.
• A record of publications in Cognitive Science, Learning Science, AI in Education, and/or Educational Data Mining (conferences such as AIED, LAK, EDM, etc.).
• Collaborative problem-solving skills to work effectively with state assessment professionals toward innovation in assessment.
• Experience working with discourse analyses, such as audio transcription analysis and multimodal data analysis.
• Data analysis skills with R/Python/or similar programming languages.

Responsibilities

• Collaborative problem-solving toward innovation in tutoring with different stakeholders.
• Design and implement evaluations of innovative programs.
• Data analyses and modeling using Python/R/or similar programming languages.
• Co-author research papers on the results of student learning modeling and studies.
• Attend required meetings and participate in seminars and training workshops.

Skills

Python

R

Data analysis

Multimodal data analysis

Education

PhD in Cognitive Science, Learning Science, Computer Science, Information Systems, or related fields

Job description

The PLUS - Personalized Learning Squared (http://tutors.plus) project, run by the Human-Computer Interaction Institute at Carnegie Mellon University aims to double the rate of math learning in middle school students, particularly those who have been historically underserved.

This project is operated in collaboration with Carnegie Learning and Stanford University and is led by Principal Investigator Prof. Ken Koedinger and Research Lead Dr. Danielle Thomas. PLUS features a hybrid tutoring platform that combines human and AI tutoring to deliver personalized learning for each student.

The National Tutoring Observatory (https://nationaltutoringobservatory.org/), is a research infrastructure led by Prof. Rene Kizilcec at Cornell University, along with researchers from Carnegie Mellon University, Massachusetts Institute of Technology, and educational strategy company FreshCognate. The mission of the National Tutoring Observatory is to observe and record great teachers and tutors at work in one-on-one and small group interactions with learners. By partnering with a range of tutoring providers, we will create the world's largest repository of video and transcript data about tutoring interactions and the incredibly important work of teachers. The Observatory aspires to create a Million Tutor Moves dataset that records at least one million interactions between teachers and students. These new data will advance the science of instruction, provide important data for technologists developing AI tools, and allow the Observatory to create a pipeline for the creation of open-source AI tools.

About The Role

We are looking for a postdoctoral fellow who will contribute to original research to improve student learning outcomes through improved tutoring for the PLUS project and the NTO. This fellow would be advised by Prof. Ken Koedinger and Dr. Danielle Thomas at Carnegie Mellon, while closely collaborating with Rene Kizilcec and Co-PIs of The National Tutoring Observatory. This includes work on improving tutor training, improving tutor-student matching, and developing AI‑assisted support tools for tutors. This is an excellent opportunity for a technology‑oriented postdoc researcher to make an impact on an exciting and fast‑scaling project in the areas of educational data science, learning science, and education technology.

The ideal candidate will demonstrate strong problem‑solving skills and apply interesting research methods and procedures to answering the following example research questions:

• What are the key drivers of effective teaching and tutoring, and how do they vary across different tutoring modalities, implementations, and student demographics?
• How can we best measure and isolate the tutoring actions and behaviors that correlate with, and ultimately predict, gains in student learning and engagement?
• How can AI‑driven tutoring tools be used to improve human tutor training and professional development?
• How can AI‑driven tutoring tools be used to improve the learning experiences and outcomes for students?

The ideal candidate will also possess strong quantitative analysis skills, and your responsibilities will include:

• Collaborative problem‑solving toward innovation in tutoring with different stakeholders
• Design and implement evaluations of innovative programs
• Data analyses and modeling using Python/R/or similar programming languages
• Co-author research papers on the results of student learning modeling and studies
• Attend required meetings and participate in various seminars and training workshops to maintain or update skills

What We Offer

• Competitive salary and benefits package including an automatic 8% monthly contribution to a retirement plan
• Professional development opportunities including tuition benefits at CMU for you and your dependents
• Comprehensive health insurance and generous PTO
• Chance to work with the world's foremost learning science researchers
• A fun, high‑octane team that will work with you every step of the way

Salary: $60,000-$75,000

Qualifications

• Ph.D. in Cognitive Science, Learning Science, Computer Science, Information Systems, or related fields
• A record of publications in Cognitive Science, Learning Science, AI in Education, and/or Educational Data Mining (conferences such as AIED, LAK, EDM, etc.)
• Collaborative problem‑solving skills to work effectively with state assessment professionals toward innovation in assessment
• Experience working with discourse analyses, such as audio transcription analysis and multimodal data analysis
• Data analysis skills with R/Python/or similar programming languages
• Experience in public education is a plus