Designing assignments to discourage AI use

Artificial intelligence (AI) tools can generate essays and answers with ease. This has left educators worried about academic integrity and tempted to police student work using AI detectors. However, focusing solely on detection is a flawed approach. AI detection software is unreliable. It often flags honest work by mistake, and even OpenAI abandoned its own detector due to poor accuracy. Many attempts to catch AI use after the fact are reactive, and they often fail to address the root problem. A more effective strategy is to prevent misuse in the first place. By rethinking assignment design and fostering a genuine learning mindset, teachers can reduce students’ incentives to cheat. Prevention through thoughtful pedagogy is a far better solution than engaging in a perpetual game of cat-and-mouse with cheating technology.

Moving beyond AI detectors

Relying solely on AI detection tools to uphold academic honesty is problematic and potentially harmful. These tools frequently produce false positives, so instructors could end up accusing innocent students unfairly. Moreover, constantly policing students with tech tools can create an atmosphere of mistrust and anxiety rather than one of learning.

Educators are increasingly recognising that a preventive approach works better. For instance, a recent study found that disengaged students – those apathetic towards their coursework – are far more likely to turn to AI for assignments. This suggests that when students feel invested in their education, they become less inclined to misuse AI. Rather than doubling down on surveillance and harsh penalties, it is more productive to address the root causes of cheating. By engaging students and making assignments meaningful, teachers can significantly curtail the temptation to use AI dishonestly. In this shift from policing to prevention, assignment design emerges as a powerful tool.

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Assignment design strategies to discourage AI misuse

Crafting assignments deliberately can reduce the chances that students will resort to AI tools dishonestly. The key is to design tasks that either a chatbot cannot easily complete, or that are inherently more rewarding for students to tackle on their own. Here are several proven strategies to make cheating with AI less tempting:

Personal and reflective assignments

One effective approach is to incorporate personal elements or reflection into coursework. AI text generators lack genuine personal experience. They struggle to produce authentic reflections or narratives about an individual’s life, feelings or growth. For example, an educator might ask students to relate course concepts to their own experiences or to reflect on how their thinking has changed over time. Because these tasks require introspection and personal input, students can see the value in doing the work themselves. They are also harder for AI to mimic convincingly.

Furthermore, personal assignments give learners a sense of ownership over their work. When students write about their own perspectives or experiences, they tend to feel more connected to the task. Thus, they become less willing to hand it over to a machine. In essence, emphasising personal voice and reflection makes it more difficult for AI to cheat. It also boosts intrinsic motivation by making learning more relevant to each student.

Oral presentations and in-class writing

Another strategy is to include oral components or in-class writing tasks as part of the assessment design. Asking students to present their ideas in person ensures that the work is demonstrably their own. Alternatively, having them write part of an assignment during class under supervision achieves a similar effect. An oral presentation requires a student to explain and defend their work live. Obviously, an AI cannot do that on a student’s behalf. Similarly, a timed in-class writing exercise can serve as a sample of a student’s authentic writing style and ability. If a polished essay submitted later is wildly inconsistent with what the student produced in class, it raises questions. But beyond the deterrent aspect, these formats also build communication skills and confidence. Students practise articulating their thoughts clearly and responding to questions, which are valuable abilities in and of themselves.

Admittedly, teachers should implement in-class or oral assessments thoughtfully. Educators should be mindful of students with anxiety or those less fluent in the language of instruction. Nonetheless, when used with supportive measures, live tasks can be a useful tool to dissuade students from relying on AI. They make it evident that each student must personally perform the work.

Multi-stage projects with drafts

Using multi-stage assignments is a powerful way to discourage last-minute cheating with AI. For example, rather than having one final submission, an instructor can break an assignment into multiple parts. These could include a proposal, a draft, a peer feedback stage, and then a final revision. This ensures that students engage with the material step by step. It becomes difficult to outsource the entire process to an AI. The instructor and peers can see the progression of ideas and writing over time. A student who tries to have an AI generate the final essay would still need to produce the earlier components and revisions. This makes any misuse more cumbersome and easier to spot.

This process-driven approach also teaches important skills such as planning, revising, and responding to feedback. It also encourages students to improve their work iteratively, which fosters deeper learning. Importantly, when learners put substantial effort into each stage, they develop a sense of accomplishment. No AI-generated quick fix can provide that. If educators structure an assignment as a journey of drafts and improvements, they reduce the practicality of AI shortcuts. At the same time, this approach promotes a more authentic engagement with the task.

Creative and critical thinking tasks

Assignments that demand creativity or higher-order thinking tend to be far less amenable to AI shortcuts. Generative AI is good at producing generic content based on existing patterns. However, it performs poorly when tasked with producing genuinely novel ideas, nuanced judgements or inventive solutions.

Therefore, teachers should emphasise projects and questions that require original thought or critical analysis rather than simple recall. For example, rather than a straightforward summary of a textbook chapter (which a language model could handle), teachers could ask students to tackle more demanding tasks. For instance, they might critique an argument, design an experiment, or propose a solution to a real-world problem. Such tasks often have no single correct answer and require students to apply concepts in innovative ways. They might also involve current events or local case studies. These are areas where AI may not have up-to-date or context-specific information.

When students tackle these challenging questions, they need to synthesise knowledge and articulate their reasoning. That is not something a student can fake simply by copying text from a chatbot. Moreover, creative assignments (like composing a short story, developing a business proposal, or crafting a unique project) tap into learners’ imagination and individual insight. They become more engaged and proud of their original work, so they have less to gain from presenting AI-generated content. By focusing on creativity and critical thinking, educators make cheating with AI much harder. This approach also enriches the learning experience, since students practise crucial intellectual skills in the process.

Fostering a culture of intrinsic motivation

All these strategies fundamentally aim to nurture students’ intrinsic motivation – their internal desire to learn and achieve. When assignments are meaningful, relevant and appropriately challenging, students generally find more personal satisfaction in doing the work themselves. They begin to see coursework as more than a box-ticking exercise for a grade. Instead, it becomes an opportunity for them to grow and explore their interests.

Education research supports this approach: students who feel connected to what they are learning are less likely to risk their integrity. Instructors can foster this connection by explaining the purpose behind each assignment. They can also give students some choice in topics and highlight how the skills gained will matter in real life.

By building a classroom culture that values curiosity and effort over shortcuts, teachers effectively reduce the appeal of AI-generated answers. In addition, showing trust in students can encourage them to take ownership of their learning instead of looking for loopholes. For example, inviting open discussions about responsible AI use and ethics helps to normalise honest behaviour. An intrinsically motivated learner has little reason to cheat with AI. They understand the true benefit of engaging fully with their work.

Conclusion

Preventing AI misuse in education is much more about smart pedagogy than high-tech policing. By proactively designing assignments that are engaging, personal and rigorous, educators can head off the impulse to rely on AI before any cheating occurs. This preventive approach is both more effective and more educationally sound than chasing after violations with detection tools. When teachers challenge students with well-crafted tasks and nurture a culture of integrity and curiosity, learners are more likely to do their own thinking. Educators who emphasise prevention over surveillance not only protect academic integrity but also help students develop stronger skills and values.

Discouraging dishonest AI use isn’t about catching students out. Instead, it’s about inspiring them to invest wholeheartedly in their own learning.