Why Intern at ETS?
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About the ETS AI Labs Internship
The ETS® AI Labs™ work drives the innovation and development of teaching and learning technologies that are grounded in research and powered by next-generation AI. The Labs are dedicated to working closely with end-users to uncover real-world needs, and co-designing and prototyping solutions to meet those needs. Our staff is made up of research and learning scientists, software developers, research engineers, user experience researchers and designers, instructional designers and producers, and product owners.
Applying for an Internship in the ETS AI Labs
Interns accepted into the ETS AI Labs summer program will participate in user needs, discovery and exploration, solution ideation and validation, capability and prototype development, iterative user validation and data-driven solution optimization. We work in agile development teams to apply the best of foundational learning and cognitive science to the design, development and testing of solutions to meet educator and learner needs. Upon the completion of the program you’ll have the opportunity to present your findings to teams across R&D.
Applicants who have interest and expertise in the following would be a great fit for this program:
- learning or cognitive science
- software development
- AI and ML engineering
- user experience research and/or design
- instructional design
- product ownership
Note: Applicants may apply to the RMS or ETS AI Labs Internship programs, but not both. However, all applicants may be considered for both programs, depending on qualifications and project needs.
Complete the electronic application form. On the application form:
- Identify the Lab team you are interested in and provide a written statement about your interest in the area(s) of research in the Lab team and how your education and experience align with the work of the Lab team.
- Attach a copy of your curriculum vitae (preferably as a PDF).
- If you are (or have been) actively enrolled in a graduate program attach a copy of your graduate transcripts (unofficial copies are acceptable).
- If you were accepted into a graduate program and deferred enrollment, attach proof of acceptance.
- Download the recommendation form and share it with your recommenders. The link to the recommendation form is on the application. Recommendations should come from an academic advisor, a professor who is familiar with your work as it relates to the project of interest, or an individual who you have worked with on a closely aligned project. ETS will only accept two recommendation forms. Recommendations should be sent electronically to email@example.com and must be received by February 1, 2022. If you would like to download the recommendation form for sending to your recommenders before submitting your application, the option to save your application information for later is available.
- The application period is currently closed.
- Applicants will be notified of selection decisions by March 31, 2022.
- Eight weeks: June 6, 2022–July 29, 2022
- $12,000 salary
- For interns participating on-campus:
- Transportation allowance for relocating to and from the Princeton area
- Housing will be provided for interns commuting more than 50 miles
- Completion of bachelor's degree
- Actively enrolled or accepted into a graduate program aligned to a Lab focus (students who have deferred enrollment due to extenuating circumstances will be considered)
The main criteria for selection will be the match of applicant interests and experience with the focus of the Labs.
ETS affirmative action goals will be considered. The ETS AI Labs value building teams of individuals from diverse backgrounds and with diverse experiences. We strongly encourage students from underrepresented groups and backgrounds to apply. Late or incomplete applications will not be considered.
Interns conduct user-inspired research and development leading to the production of technology enhanced learning solutions. Depending on which project an intern is selected for, they would participate in any number of research and development activities, such as:
- user needs analysis and validation
- solution ideation
- wireframing and prototyping
- user experience research and design
- back- and front-end prototype development
- user testing and iteration
- effectiveness efficacy studies
All interns will create and give presentations about their projects at the end of the internship period.
About the ETS AI Labs
The Labs aim to advance educational and employment opportunity by providing tools and services that help individuals worldwide identify, efficiently progress toward, and meet their learning and career goals.
Labs teams leverage NLP and AI capability to deliver personalized solutions to educators and learners in the K–12, higher education and workforce spaces. The solutions we develop enable efficiencies, flexibility and actionable insights.
The Personalized Learning and Assessment Lab
The Personalized Learning and Assessment Team constructs automated environments that enable targeted learning pathways, individualized supports for learners and course-wide recommendations for educators. The Lab develops adaptation capabilities based on rigorous models and actionable insights to support decision-making among learners and educators.
Students with a background in data science, learning science, cognitive science, instructional design, user experience research and design or software development would benefit from an internship in the Personalized Learning and Assessment Lab.
Possible projects for this Lab might include:
- AI and adaptivity projects that offer the opportunity for interns to think through how an assessment for learning model might be constructed, meet with users to understand what activities would support the learning model and create user data footprints that would be required to enable an adaptive experience.
- Insights and intervention projects that offer interns the opportunity to think about and develop principles for ingesting, storing, and analyzing data and using the data to reveal actionable insights and recommended interventions to educators and learners.
- Predicting outcomes through usage data projects would involve thinking through the user progressions within a system, understanding where important user interactions are, and aligning both with what data are captured/available in the system.
The Natural Language Processing (NLP) Lab
The Natural Language Processing (NLP) Teams develop capabilities that enable automation in scoring of language assessments, learning and tool development; and engines that support recommendations and interventions. The Lab will prioritize responsible application of AI and expand on our AI scoring thought leadership in adjacent areas.
Students with a background in data science, computer science, natural language processing, machine learning and/or deep learning would benefit from an internship in the Natural Language Processing (NLP) Lab.
Possible projects in this Lab might include:
- Automated scoring projects would offer an intern the opportunity to apply Natural Language Processing methods to written and spoken formative assessments to provide meaningful insights to educators and learners.
- Real-time targeted speaking feedback projects would include the use of Natural Language Processing (NLP) methods paired with theoretical research and user feedback to understand the features that are most important for users to receive feedback on and how to reveal those features and to users.
The Language Learning, Teaching and Assessment Lab
The Language Learning, Teaching and Assessment Team develops prototypes to support the establishment of an ecosystem of interconnected language learning and assessment systems that provide personalized and ongoing support for language learners domestically and throughout the world.
Students with a background in data science, learning science, cognitive science, linguistics, software engineering, software development, computer science, instructional design, or user experience research and design would benefit from an internship in the Language Learning, Teaching, and Assessment Lab.
Possible projects in the Lab might include:
- Promoting proficiency through meaningful feedback projects would involve understand user needs, consultation with the language learning literature for insights into how best to meet those needs, and the application of data gathered from both to the development of prototypes of meaningful interpretation layers of speech scoring data.
- Supporting fluency by practicing confidently projects would involve collecting and analyzing user feedback, conducting analyses of beta tester usage data within the app, and making concrete recommendations for optimization of existing features and/or development of new features in the app.
- Meeting the needs of young English-language learners projects will involve the ideation, design, and development of capabilities, product concepts, and prototypes that support young learners as they enter and progress in their language learning journey.
For more information, contact us via email.