Artificial intelligence is rapidly becoming part of everyday teaching and learning in higher education. Since generative AI tools such as ChatGPT became widely accessible in 2022, educators have entered a new era where AI can generate essays, discussion posts, and summaries in seconds. While these tools offer new opportunities for learning, they also raise important questions about how assignments measure student understanding.
One of the most common concerns faculty share today is the feeling that they may be grading the work of a machine rather than the thinking of a student.
A helpful analogy illustrates this challenge: using AI to write a paper is like using a forklift to lift weights—you may complete the task, but no strength is built.
The goal of education is not simply producing a finished product; it is developing the thinking skills behind that product. When AI completes the intellectual work, students miss valuable opportunities to practice those skills.
AI is not going away, and banning it entirely is neither realistic nor productive. Instead, educators can shift the focus from AI as a shortcut to AI as a learning partner.
Behind the scenes (during course design):
Brainstorming assignment ideas
Generating discussion prompts
Drafting rubrics or activity structures
In the classroom (during instruction):
Think–Pair–AI–Share activities where students discuss a concept, consult AI, and critique the output
Co-creating rubrics with students and using them to evaluate AI-generated responses
Comparing AI answers with course readings to identify inaccuracies or missing perspectives
These strategies help students develop AI literacy and critical evaluation skills, which are increasingly essential in today’s information environment.
One promising approach is authentic assessment. Authentic assessments focus on applying knowledge in meaningful, real-world contexts and emphasize the learning process as much as the final result.
Unlike traditional assignments that AI can easily replicate, authentic assessments make student thinking visible.
Examples include:
Learning portfolios showing drafts, revisions, and reflections
Oral defenses or short video explanations of written work
Experiential projects involving community or workplace engagement
Interviews or surveys connected to real-world contexts
Applied projects related to professional practice
Think–pair–share discussions grounded in personal experiences
These types of assignments emphasize analysis, reflection, and application—areas where human insight remains essential.
Rather than focusing on policing AI use, instructors can design assignments that encourage meaningful engagement with learning. A few practical strategies include:
1. Explain how AI tools may or may not be used in completing the assignment.
2. Evaluate outlines, drafts, reflections, or research notes—not just the final submission.
3. Ask students to explain how they developed their ideas, what sources influenced them, and whether they used AI tools during the process.
These strategies help ensure that assignments measure thinking, understanding, and skill development, not simply text generation.
The emergence of generative AI presents a powerful opportunity for educators. Instead of focusing solely on preventing misuse, instructors can design learning experiences that:
emphasize human reasoning
develop ethical AI use
encourage critical thinking
support deeper engagement with course content
By shifting from product-focused grading to process-rich learning, educators can ensure that assignments remain meaningful in the age of AI.