A helpful analogy comes from hiking:
Imagine going on a hike with friends or family. The view at the top is rewarding, but the real value is in the climb, the process, the effort, the time spent together, and the learning along the way. Traditionally, the view (the essay, the correct answer, the lab result) was a signal that the climb had occurred. Now, students have access to a “helicopter” (AI) that can carry them to the view quickly, sometimes before they’ve fully engaged in the climb. Students may start calling the AI “helicopter” at the bottom of the mountain before they’ve even attempted the climb. The challenge for instructors is how to help students step out of the helicopter and engage meaningfully in the learning process.
1. Lack of Clarity:
If assignments feel vague or expectations are unclear, students may turn to AI for guidance or to fill in gaps.
2. Lack of Motivation:
Grades alone are often not enough to drive sustained engagement. If students do not feel connected to the purpose or relevance of an assignment, shortcuts are tempting.
3. Reward vs. Effort Mismatch:
When the perceived payoff seems low relative to the effort required, AI becomes an attractive option. Many students are balancing coursework with jobs, family responsibilities, and other pressures. Efficiency feels necessary.
4. Confidence or Skill Gaps:
Some students struggle with writing, math, or problem-solving. AI can feel like a safety net to get started when they are unsure how to proceed.
5. Curiosity and Exploration:
Many students are experimenting with AI simply because it is new, exciting, and normalized in workplaces they see online.
Understanding why students turn to AI allows us to respond with empathy and strategy. The goal of this approach is not to punish or prohibit, but to design learning experiences that make the climb or the process valuable in itself. Intrinsic motivation, reflection, iterative practice, and meaningful challenges reduce the appeal of shortcuts. Grades alone are sometimes not enough; students may need to experience the payoff in discovery, problem-solving, and ownership of their learning.
Clarify the Climb – Make assignment expectations, steps, and goals explicit. When students know how to approach the climb, they are less likely to rely on AI to fill in gaps.
Focus on Process, Not Just Product – Incorporate reflection, draft revisions, or step-by-step problem-solving. Reward the climb, not just the view.
Design for Intrinsic Motivation – Connect assignments to real-world relevance or personal interests. When discovery and growth are the payoff, shortcuts are less appealing.
Integrate AI Strategically – Offer AI as a tool for brainstorming, drafting, or experimentation, but require students to critically evaluate, revise, or justify its output.
Create “Human-Centric” Checkpoints – Use in-class demonstrations, live problem-solving, or lab work to ensure students actively engage with the material and reasoning behind their solutions.
Model and Discuss Ethical AI Use – Talk with students about when, why, and how to use AI responsibly in learning and professional contexts.
Practical strategies to support this shift are also explored in our article, “AI & Assessments: A Thoughtful Approach for Faculty,” which offers ideas for assignments that preserve deep learning while acknowledging the presence of AI.
Packback. (2026, February 18). Supporting original thinking: Rethinking assessment in the age of AI [Webinar]. https://packback.co/thank-you-webinar-supporting-original-thinking/