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Resilience means assignments that adapt to technological change without losing their core learning objectives.
An AI-resilient assignment emphasizes process, student ownership, creativity, critical thinking, and the motivation of having a public/ readership for student work, among other thingsaspects.
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Assignment design strategies
Break down assignments into scaffolded, multi-step tasks to emphasize process and reduce opportunities for AI usemisuse.
Personalized and reflective writing, such as autobiographical or student-designed assignments, is harder for AI to replicate.
Public presentations or peer-reviewed work incentivize students to invest effort and originality.
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Encourage open conversations about AI use. Talk about if, how, when, and why , and how students are using use it.
Teach students to critically assess AI tools and recognize their limitations (e.g., bias, lack of empathy, factual errors).
Hands-on AI experimentation (e.g., comparing student work with AI-generated versions) helps demystify GenAI and build confidence.
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Labor-based or contract grading (as shared by Dr. Suzanne Edwards) shifts emphasis from outcome to effort, growth, and process.
Contract grading can also : (a) reduce pressure that often leads students to misuse AI ; and (b) promote positive risk-taking and self-directed learning.
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Dr. Haiyan Jia
Remarked that students often turn to AI because of stress, time - constraints, and feeling hopeless or adrift in a class.
Introduced a range of key terms including AI-resistant, AI-critical, and AI-organic.
Instructor techniques to make their assignments AI-resilient or AI-resistant include allocating time for project work in the classroom, introducing process-oriented assignments, and assigning deliverables at several points in the semester.
It is important to teach students to be AI-critical. This means teaching them media literacy as it pertains to AI, including doing comparative assignments in class (e.g., they do an assignment, then they do the assignment using AI as a teammate/ assistant).
To be AI-organic means encouraging computer-human interactions and incorporating AI into our classes in a purposeful way.
Her goal is to teach AI critically and organically in journalism. She emphasizes hands-on comparison and interdisciplinary learning.
Dr. Annabella Pitkin
There are benefits to having students cite and reflect on their use of AI as part of an assignment (e.g., within an appendix or addendum to assignments).
When students know a topic well, they can easily critique outputs from AI. When they don’t know a subject well, they are more vulnerable to the mistakes and biases embedded in AI.
She is committed to reflective AI use through student journaling.
A sandbox tool built by Rob Weidman, Lehigh’s Senior Digital Scholarship Specialist, helped students see into the “black box” of AI and appreciate the value and depth of their writing.
Dr. Eric Obeysekare
We are in the midst of a paradigm shift when it comes to learning how to code with AI.
There are things that humans are good at that AI does not replicate. These include critical thinking and problem solving, communication and collaboration, and cultural agility.
To be AI-resilient, have students collaborate more. Working in isolation lends itself to turning to AI as the only teammate at hand.
It is important to remember that LLMs have a “born on” date. In response, orient students to the future.
Know that students have a thirst to learn AI. Work with that! Emphasize guiding over gatekeeping.
Teach AI-supported problem-solving in engineering.
Dr. Suzanne Edwards
Her interest in contract grading and contract-based grading was a response to AI an an emergent technology.
Her pedagogy emphasizes choice: . There are 12 writing assignments prompts a student might complete in a semester. They have the freedom to work on what interests them.
One way to be AI-resilient is to emphasize qualitative feedback and conversations between the professor and the student over a numerical score.
In her experience, contract grading increases student autonomy, motivation, and responsibility for learning. More feedback and more emphasis on process leads to better student work.
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