Opportunity Information: Apply for 11 SN 0019
Apply for 11 SN 0019
- The Office of Naval Research in the science and technology and other research and development sector is offering a public funding opportunity titled "Special Program Announcement for Office of Naval Research STEM Grand Challenge Adaptive, Generalizable Intelligent Tutors for STEM and Naval Training and Education" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.300 Basic and Applied Scientific Research.
- This funding opportunity was created on Jun 15, 2011 and posted on Jun 15, 2011.
- Applicants must submit their applications by Sep 30, 2011 White papers are due by the date specified in the Special Notice. Full Proposals should be submitted under ONRBAA11 001 by the date specified in the Special Notice. Full Proposals received after that date will be considered only insofar as agency time and continued availability of funding permit. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The funding agency has allocated a total of $8,000,000.00 to eligible and selected applicants.
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
- See ONRBAA11 001 for Eligibilty Information
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Opportunity Summary:
The Office of Naval Research (ONR) announced a STEM Grand Challenge grant opportunity focused on developing the next generation of intelligent tutoring systems for STEM and Navy-relevant training and education. The core aim is to fund research that creates adaptive, individualized tutoring technologies that go beyond basic test-score gains or memorization. ONR is looking for approaches that can improve learner comprehension within a fixed instructional time and curriculum, help students reach higher standards of competence closer to true expertise, and speed up the development of problem-solving, reasoning, and decision-making skills. In practical terms, the program is centered on building tutors that can adjust to each learner in real time while still being effective across complex STEM content and operationally relevant training settings.
A major theme of the announcement is that intelligent tutors must be both adaptive and generalizable. ONR is not just seeking one-off systems built for a single course or narrow topic; the program emphasizes underlying methods and architectures that can transfer across different educational domains (for example, different STEM subjects) and different populations of learners. That generalizability goal is paired with a focus on effectiveness: the tutor should produce meaningful learning gains within constraints that reflect real training environments, such as limited time, set curricula, and required standards of mastery.
The opportunity highlights six technical and scientific problem areas that proposers are expected to address in some way. First, ONR wants computational cognitive models of student performance, meaning models that can represent and predict how learners think, where they struggle, and how their knowledge changes over time. Second, the program calls for realistic and effective student-tutor interactions, which points to research on interfaces and dialogue, feedback timing and style, and interaction designs that feel natural while still driving learning. Third, ONR emphasizes individually tailored instructional strategies capable of delivering a large body of course material, suggesting a need for personalization at scale rather than handcrafted branching for a few lessons. Fourth, the program explicitly requires that the technology generalize across multiple domains and populations, reinforcing that solutions should not be brittle or tied to one context. Fifth, ONR expects the work to incorporate modern empirical findings from the science of learning and evidence-based pedagogical methods, with self-explanation given as one example of the kinds of learning strategies that could be embedded. Sixth, ONR is interested in tools and methods that reduce the time, labor, and specialized expertise required to build intelligent tutors, indicating that authoring tools, content generation pipelines, reusable components, and streamlined development workflows are part of the intended research landscape.
From a submission and process standpoint, this announcement requires white papers before a full proposal can be submitted. The white paper due date and other detailed instructions are contained in the Special Notice referenced in the Grants.gov posting, and applicants are directed to access it by selecting the "FULL ANNOUNCEMENT" section on the synopsis page. Full proposals are to be submitted under the broader ONR Broad Agency Announcement referenced as ONRBAA11-001, following the dates and instructions specified in that Special Notice. The notice also indicates that proposals submitted after the specified date may only be considered if agency time and funds remain available, which is a typical statement for competitive research solicitations.
Administratively, this is a discretionary grant opportunity under CFDA 12.300 (Basic and Applied Scientific Research), with no cost sharing or matching requirement. The posting lists an estimated total funding amount of $8,000,000. The opportunity was posted on June 15, 2011, with an original and current closing date of September 30, 2011, and an archive date of October 30, 2011, reflecting that it was a time-bound competition. Eligibility is not fully spelled out in the synopsis and instead points applicants to ONRBAA11-001 for the formal eligibility language, with the Grants.gov listing indicating "Others" and directing readers to the eligibility text in the full materials.
For questions related to accessing the full announcement, the contact listed is Sean Palmer, Contract Specialist, at 703-696-0942, with additional mention of Grants.gov support for technical submission issues. Overall, the grant opportunity is best understood as an ONR-driven push to advance the scientific and engineering foundations of intelligent tutoring systems so they can deliver stronger learning outcomes, support higher-level competence, scale to large curricula, transfer across domains, and be built faster and more efficiently than traditional tutor development approaches.
FAQs: ONR STEM Grand Challenge - Intelligent Tutoring Systems
What is this grant opportunity about?
This Office of Naval Research (ONR) STEM Grand Challenge opportunity focuses on research to develop the next generation of intelligent tutoring systems for STEM and Navy-relevant training and education. The intent is to support adaptive, individualized tutoring technologies that strengthen understanding and higher-level competence, not just memorization or simple test-score gains.
What is the main goal ONR is trying to achieve with this program?
The core goal is to fund research that enables intelligent tutors to improve learner comprehension within fixed instructional constraints (limited time and a set curriculum) while helping learners reach higher standards of competence closer to true expertise. ONR also emphasizes accelerating the development of problem-solving, reasoning, and decision-making skills.
What kinds of tutoring systems is ONR looking for?
ONR is looking for intelligent tutors that can adapt to each learner in real time and still remain effective across complex STEM content and operationally relevant training settings. The program is explicitly centered on adaptive and individualized instruction at scale.
What does ONR mean by "adaptive" in this context?
Adaptive means the tutor can adjust to the individual learner in real time based on how the learner is performing, what they understand, and where they struggle, rather than delivering the same fixed path to every learner.
What does ONR mean by "generalizable" (or "generalizable across domains")?
Generalizable means ONR is not seeking a one-off tutor tied to a single course or narrow topic. Instead, the emphasis is on methods and architectures that can transfer across different educational domains (for example, different STEM subjects) and across different learner populations, rather than being brittle or locked to one context.
Is the program focused only on improving test scores?
No. The announcement highlights that the objective goes beyond basic test-score gains or memorization. The focus is on deeper comprehension, higher standards of competence, and faster development of reasoning and decision-making skills within realistic training constraints.
What real-world constraints does ONR expect the tutoring system to handle?
The opportunity stresses that effectiveness should be demonstrated within conditions that reflect real training environments, including limited instructional time, fixed curricula, and required standards of mastery.
What technical and scientific problem areas does ONR highlight?
The announcement highlights six problem areas that proposers are expected to address in some way:
- Computational cognitive models of student performance
- Realistic and effective student-tutor interactions
- Individually tailored instructional strategies capable of delivering large bodies of course material
- Generalization across multiple domains and populations
- Use of modern empirical findings from the science of learning and evidence-based pedagogy (self-explanation is cited as an example)
- Tools and methods to reduce the time, labor, and specialized expertise needed to build intelligent tutors
What are "computational cognitive models of student performance" in the context of this program?
In this program, computational cognitive models are models intended to represent and predict how learners think, where they struggle, and how their knowledge changes over time. These models are a foundation for making real-time tutoring decisions that are personalized to the learner.
What is meant by "realistic and effective student-tutor interactions"?
This refers to research on how the learner and tutor interact, including interfaces and dialogue, the timing and style of feedback, and interaction designs that feel natural while still driving learning.
What does "individually tailored instructional strategies at scale" imply?
It implies personalization across a large body of course material without relying on handcrafted branching for only a small set of lessons. The intent is to support individualized instruction while still covering broad curricula.
Does ONR require the approach to incorporate learning science?
Yes. ONR expects the work to incorporate modern empirical findings from the science of learning and evidence-based pedagogical methods. The announcement gives self-explanation as one example of learning strategies that could be embedded.
Is ONR also interested in reducing the effort needed to build intelligent tutoring systems?
Yes. A stated theme is reducing the time, labor, and specialized expertise required to build intelligent tutors. This includes interest in authoring tools, content generation pipelines, reusable components, and streamlined development workflows.
How much funding is estimated to be available under this opportunity?
The posting lists an estimated total funding amount of $8,000,000.
What CFDA number is associated with this opportunity?
This opportunity is listed under CFDA 12.300, Basic and Applied Scientific Research.
Is cost sharing or matching required?
No. The synopsis indicates there is no cost sharing or matching requirement.
Is this a discretionary grant?
Yes. The posting describes it as a discretionary grant opportunity.
When was the opportunity posted, and what were the key dates?
The opportunity was posted on June 15, 2011. The original and current closing date listed is September 30, 2011. The archive date is October 30, 2011, indicating it was a time-bound competition.
Does this opportunity require a white paper before submitting a full proposal?
Yes. The announcement requires white papers before a full proposal can be submitted.
Where do applicants find the white paper due date and detailed instructions?
The white paper due date and detailed instructions are contained in the Special Notice referenced in the Grants.gov posting. Applicants are directed to access it by selecting the "FULL ANNOUNCEMENT" section on the synopsis page.
How are full proposals submitted?
Full proposals are to be submitted under the broader ONR Broad Agency Announcement referenced as ONRBAA11-001, following the dates and instructions specified in the Special Notice.
What happens if a proposal is submitted after the specified date?
The notice indicates that proposals submitted after the specified date may only be considered if agency time and funds remain available.
Who is eligible to apply?
Eligibility is not fully spelled out in the synopsis. The Grants.gov listing indicates "Others" and directs applicants to the formal eligibility language in ONRBAA11-001 and related full materials.
Where should applicants look for the formal eligibility language?
Applicants are directed to ONRBAA11-001 and the full materials referenced through the Special Notice accessible from the Grants.gov "FULL ANNOUNCEMENT" section.
Who is the contact for questions about accessing the full announcement?
The listed contact is Sean Palmer, Contract Specialist, at 703-696-0942.
Who should applicants contact for technical submission issues?
The posting also mentions Grants.gov support for technical submission issues.
What is the overall purpose of the ONR STEM Grand Challenge in this announcement?
Overall, the opportunity reflects an ONR effort to advance the scientific and engineering foundations of intelligent tutoring systems so they can deliver stronger learning outcomes, support higher-level competence, scale to large curricula, transfer across domains and populations, and be built faster and more efficiently than traditional approaches.
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