4 Tips for Designing AI-Resistant Assessments
In an age where generative AI is increasingly accessible, educators face the challenge of designing assessments that encourage authentic student engagement and minimize the use of AI-generated content. Here are some strategies to help you create AI-resistant assignments while fostering deep learning:
1. Personalized Assignments Rooted in Local Context
Encourage students to personalize their work by drawing on their local environment or personal experiences. AI tools often struggle with assignments requiring unique, nuanced applications of knowledge that rely on individual perspectives.
Example: Instead of assigning a generic analysis of climate change, ask students to explore how climate change has impacted their local area. They could:
Conduct interviews with local farmers, gardeners, or fishermen to gather firsthand accounts of how changing weather patterns have affected agriculture, wildlife, or water resources.
Visit local government offices or community organizations to learn about initiatives or policies addressing climate change in their region.
Collect data from local weather reports, historical archives, or environmental studies to assess the changes in their area over time.
These tasks require students to collect information not readily available to AI tools, pushing them to engage more deeply with their own community and develop a more personal understanding of global issues.
2. Incorporate Multimodal Assessments
Use assessments that require students to express their understanding through various media forms. Most AI tools are limited to generating text and struggle to combine different modes of expression effectively.
Example: In a biology class, students could be asked to:
Create a multimedia presentation about an ecosystem. Alongside a written explanation of the ecosystem’s dynamics, they could include original photos, diagrams, or digital artwork illustrating different species and their interactions.
Develop a short video documentary, using narration, video clips, and animations to showcase a specific plant or animal’s role within its environment.
Produce a podcast episode where they interview local ecologists, conservationists, or park rangers, discussing their findings and including sound clips from fieldwork or nature sounds they recorded.
By incorporating diverse forms of media, students engage with the material on multiple levels, making it challenging to rely solely on AI-generated content and encouraging them to think creatively about how they present information.
3. Process-Oriented Assessments
Design assignments that focus on multiple stages of the student’s work, highlighting their learning journey rather than just the final product. This approach helps track student progress and involves a level of critical thinking that is hard for AI tools to replicate.
Example: For a literary analysis project:
Begin with an assignment where students brainstorm themes they want to explore in a novel of their choice. They would then draft a thesis statement, including why this theme is significant to them personally.
Ask students to submit an annotated bibliography of sources they plan to use, including literary criticisms, historical context, and related works. Require a brief explanation of how each source will support their analysis.
Next, have students submit an outline, identifying key arguments and textual evidence they will use.
Finally, after submitting a first draft, ask them to write a reflective paragraph explaining how feedback on their draft led them to revise their arguments or choose different evidence.
This step-by-step approach ensures that students engage in an iterative thinking process and develop their analytical skills, rather than simply turning to AI tools for a pre-written essay.
4. Real-Time Assessments and Interactive Activities
Real-time assessments, such as live presentations, debates, and in-class activities, challenge students to think on their feet and articulate their ideas spontaneously. These activities showcase students’ critical thinking and engagement, which AI cannot easily replicate.
Example: In a geography unit, students could:
Participate in a real-time debate where they represent different countries in a simulated international climate summit. They must quickly argue their country’s position on environmental policies, using data and case studies they’ve researched.
Deliver a live presentation about a recent natural disaster, explaining its causes, effects, and potential mitigation strategies, while answering questions from classmates on the spot.
Conduct an in-class “town hall” where students role-play as city planners, residents, and environmentalists, discussing the pros and cons of building a new public park in their community. This would involve spontaneous thinking and the ability to address questions from multiple perspectives.
Similarly, oral exams can be incorporated to assess both subject mastery and communication skills. For instance, students could record a video answering a set of randomized questions about their science project. If their performance in these oral exams significantly differs from their written work, it opens up a conversation about their understanding of the material.
Creating AI-resistant assessments involves more than just making tasks more difficult; it’s about designing meaningful learning experiences that engage students’ creativity and critical thinking. By using personal context, multimodal expression, process-focused steps, and real-time assessments, educators can ensure authentic student engagement while minimizing reliance on AI tools.