As generative AI tools become ubiquitous in higher education, instructors across disciplines are grappling with a core challenge:
How do we authentically capture student learning when an AI can generate a polished essay, summary, or response to prompts in seconds?
While detector tools often prove unreliable or punitive, pedagogical redesign offers a far more sustainable solution. Rather than focusing solely on detecting AI usage at the point of submission, AI-resilient course design focuses on making the learning process visible, collaborative, and reflective.
FeedbackFruits offers a suite of learning tools integrated directly into Canvas that helps shift the focus from final product to active engagement. Read on to learn how you can use FeedbackFruits to build assignments that foster genuine student learning in the age of AI.
What Makes a Learning Activity "AI-Resilient"?
AI-resilient learning activities tend to share three core characteristics:
- Assignments that focus on process over product, emphasizing drafting, feedback, revision, and reflection rather than just a single final file submission.
- Activities that require social and collaborative engagement, requiring peer-to-peer interaction, critique, and community dialogue that AI cannot replicate on behalf of a student.
- Processes that require in-context metacognition, prompting students to explain why they made specific choices and how their thinking evolved during the assignment.
FeedbackFruits Tools for Active Learning
Peer Review Tool
Generative AI can write a paper, but it cannot engage in meaningful, asynchronous peer dialogue with classmates about their specific work. Using FeedbackFruits Peer Review, students exchange drafts, review peer submissions against custom rubrics, and reply to feedback they receive. The Peer Review tool tracks student learning by capturing progress data across every stage of the review workflow. Instructors can monitor whether students submitted work on time, provided constructive feedback according to specified rubrics, and read the reviews left by their peers. Real-time dashboard analytics display completion percentages, time spent giving feedback, and rubric score distributions, enabling instructors to identify students who struggle with applying evaluation criteria or articulating constructive feedback. Instructors can configure the grading breakdown to include not just the draft, but the quality of feedback students give to their peers and their written reflections on how they plan to implement that feedback in their final revisions.
Interactive Documents & Videos
When students read articles or watch lectures, how do you know they are actively processing the material rather than asking AI to summarize it? The Interactive Document tool tracks reading engagement and comprehension by transforming static documents into active learning activities. Detailed analytics capture metrics beyond simple page views, including student time spent on readings, response accuracy on embedded quiz questions, and the frequency of student-generated annotations and peer discussions. These data points allow instructors to pinpoint specific passages or concepts that caused widespread confusion and adjust upcoming instruction accordingly.
The Interactive Video tool tracks student engagement with video content by embedding formative questions, discussion prompts, and reflections directly into media timelines. Learning analytics measure viewing completion rates, video re-watch frequency on specific segments, and individual performance on embedded checks for understanding. Instructors can leverage these analytics to track where student attention wanes and identify complex lecture topics that require further clarification in class. The Interactive Document and Video tools allow you to track actual student engagement with a text instead of an AI-generated summary of information.
Group Member Evaluation
AI can generate group project reports, but it cannot participate in team collaboration or demonstrate interpersonal problem-solving. The Group Member Evaluation tool tracks collaboration dynamics and individual accountability within team projects. By collecting structured peer ratings and qualitative feedback based on customizable rubrics, the tool gives instructors transparent insight into group workload distribution and interpersonal participation.
By setting up this activity in FeedbackFruits, instructors can seamlessly track progress indicators showing who has submitted evaluations, monitor individual contribution scores, and step in early if group friction or uneven effort arises. Group Member Evaluation allows students to evaluate their peers' contributions, collaboration skills, and communication throughout a semester-long project. It helps elevate soft skills, teamwork, and individual accountability, something AI cannot do well for them.
Start Small; Take One Step
The thought of redoing class activities in light of our AI-integrated world can feel incredibly overwhelming. We encourage you to start small with one change this semester.
Identify one assignment in your course where a student could easily input the prompt into an AI tool and receive a high-scoring response without engaging with the course materials. Then, join ATSS via Zoom to learn how to set up the FeedbackFruits activities mentioned in this post:
- FeedbackFruits: Use Interactive Study Tools for AI-Aware Teaching; Wednesday, September 30 from 10 am-11 am.
- FeedbackFruits: Use Peer Review & Group Member Evaluation for AI-Aware Teaching; October 8 from 11 am-noon.
Note: Check the above links for available video recordings of these training sessions if you are viewing this blog after the above session dates.
By integrating these strategies into your course, you turn learning back into an active, social, and human-centered process.
Author/Contributors
- Annette McNamara, Instructional Designer, Academic Technology Support Services
- Rebecca George-Burrs, Academic Technologist, Academic Technology Support Services
Note: Gemini AI assisted in some parts of the creation of this blog post. All content was comprehensively reviewed, edited, and fact-checked by a human team for accuracy and the removal of potential AI-introduced bias.
Like what you read? Subscribe to the Extra Points Google Group for an email notification when the next blog post goes live.