AI-Resilient Assignment Design — Master Framework
An institution- and course-independent framework for designing assignments so AI can support learning without substituting for the evidence of learning.
View Framework PDF →A second set of eyes before you submit.
A coordinated toolkit for instructors and students working in an environment where capable generative AI is readily available. It combines an instructor design framework, a student-facing pre-submission review agent, a Cursor quick-start guide, and a student agent user guide.
These resources serve different roles but share the same principle: AI can assist learning while the human remains responsible for understanding, validation, judgment, and the final submission.
An institution- and course-independent framework for designing assignments so AI can support learning without substituting for the evidence of learning.
View Framework PDF →Reviews completed work against the actual assignment requirements, surfaces possible omissions or inconsistencies, and points the student toward items worth reconsidering.
Download Agent V1.0 →New to Cursor? Start with the Cursor Quick Start Guide. Then use the Student Agent User Guide to install and run the AIFE Assignment Pre-Submission Review Agent.
The AIFE Assignment Pre-Submission Review Agent helps students review completed homework, laboratory reports, projects, programming assignments, and other coursework against the actual assignment requirements.
Give the agent your assignment instructions, rubric if applicable, and completed work. It examines the requirements and evidence, checks for missing or inconsistent elements, and identifies places that may deserve another look.
The agent is intentionally designed not to grade your work, optimize it for a perfect score, or decide when it is ready to submit. Instead, it behaves more like an instructor walking around the classroom: it may point something out, ask you a question, or give a limited hint when appropriate— but the thinking and the final decision remain yours.
Complete the assignment as you normally would. This agent is designed to review your work—not do the assignment for you.
Use the downloaded AIFE V1.0 agent in the supported AI development/chat environment.
Provide the assignment instructions, rubric if separate, completed submission, and any other relevant files needed to understand the work.
FULL REVIEW: review the entire submission against the applicable requirements. FOCUSED REVIEW: review a particular section, requirement, calculation, figure, table, code segment, or other identified issue.
A NEEDS ATTENTION finding does not automatically mean something is wrong. The agent may be asking you to compare evidence, reconsider an assumption, check a calculation, examine an inconsistency, or determine whether a requirement has actually been satisfied.
Recheck your own work. If stuck, you may ask for a limited hint or conceptual explanation. The agent is designed not to complete assessed work for you.
The agent does not determine whether work is “good enough,” predict a grade, or tell you that the assignment is ready to submit.
The AIFE Assignment Pre-Submission Review Agent was developed as an educational review tool for AI-augmented learning.
AI can help review the work. The human remains responsible for understanding, validation, judgment, and the final submission.
The agent can miss problems and can raise questions about things that are actually correct. Its review is not a grade, does not replace assignment instructions or instructor feedback, and does not guarantee correctness, completeness, or any particular score.
Students remain responsible for complying with the AI-use policies of their course and institution.
Initial public release. Validated using strong, average, weak, and known-issue student-work cases across engineering laboratory reports and homework assignments in multiple engineering courses.
Continued testing across additional assignment types and disciplines is underway. Future releases will incorporate improvements supported by classroom use and validation.