How Assessment Is Changing in the AI Era: New Testing Methods for 2026

Traditional closed book exams are becoming extinct in many classrooms. As AI tools become universally accessible, educators are fundamentally rethinking how they measure student learning and skills.

The Rise of Open AI Examinations

Forward thinking institutions are embracing what seemed impossible just years ago: exams where students can use AI tools freely. This approach recognizes that restricting technology during assessments no longer reflects real world conditions.

Universities like Arizona State University and Georgia Tech have pioneered open AI testing formats. Students access ChatGPT, Claude, or other AI assistants during exams, but questions are redesigned to test critical thinking rather than information recall.

Instead of asking students to list causes of World War I, open AI exams present complex scenarios requiring analysis, evaluation of AI generated responses, and synthesis of multiple sources. Students must demonstrate understanding by critiquing AI outputs, identifying errors, and building arguments beyond what AI can generate alone.

Research from Harvard’s Derek Bok Center shows these assessments better measure higher order thinking skills that employers actually value. Students learn to use AI as a research tool while proving they understand material deeply enough to evaluate and improve AI suggestions.

Project Based Assessment Replacing Multiple Choice

Another major shift is the move toward extended projects that showcase authentic skill development. These assignments are difficult to fake with AI because they require personal reflection, iterative development, and presentation of unique insights.

High schools are assigning semester long research projects where students document their process through journals, draft revisions, and presentation of findings. Teachers evaluate the learning journey, not just the polished final product.

Coding bootcamps and computer science programs have adopted portfolio assessment where students build actual applications, contribute to open source projects, or solve real business problems. According to Computing Research Association data, these authentic assessments reduce AI cheating by 67% compared to traditional coding exams while better preparing students for professional work.

Process Documentation Becomes Critical

Successful project based assessment requires students to document their thinking process. Learning management systems now include features for students to submit multiple drafts, explain decision making, and reflect on how they used various resources including AI tools.

Teachers review this documentation to verify students genuinely understand their work rather than copying AI outputs. This approach teaches valuable metacognitive skills while making academic dishonesty far more difficult and time consuming.

Oral Examinations Making a Comeback

Face to face assessment is experiencing a renaissance. Oral exams allow instructors to probe student understanding through follow up questions that AI cannot prepare students for in advance.

Medical schools never abandoned oral examinations, and now liberal arts colleges and business programs are reintroducing them. Students present their work, then answer spontaneous questions testing whether they truly grasp the underlying concepts.

A 2025 survey by Inside Higher Ed found that 43% of colleges now incorporate oral assessment components in at least some courses, up from just 12% in 2023. Faculty report these conversations reveal student understanding far better than written exams ever did.

Academic Integrity Tools Evolving Beyond Detection

The arms race between AI detection software and AI writing tools has largely ended with detection tools losing. Schools are abandoning unreliable AI detectors that flag innocent student work while missing actual AI misuse.

Instead, effective integrity approaches focus on learning behaviors and engagement patterns. Learning analytics platforms track how students interact with course materials: time spent reading, resources consulted, and progression through assignments.

Tools like Turnitin have pivoted from AI detection to similarity checking and unusual pattern identification. Rather than claiming to catch AI writing, they flag submissions inconsistent with a student’s previous work or completed impossibly quickly.

Designing Out Cheating Through Better Assignments

The most effective integrity strategy is creating assignments where using AI superficially produces obviously inadequate results. When prompts require personal experience, local context, or specific class discussions, generic AI responses stick out clearly.

Professors are sharing assignment banks of AI resistant prompts through organizations like Open Educational Resources. These questions leverage current events, campus specific situations, or require integration of multiple class specific sources that AI tools cannot access or synthesize appropriately.

Balancing Fairness and Innovation

Schools struggle to balance innovative assessment with equity concerns. Not all students have equal AI access at home, creating potential advantages for wealthier students in open AI testing environments.

Successful institutions provide AI tool access during school hours, offer technology lending programs, and design assessments completable with school provided resources. The goal is testing student capability, not their family’s technology budget.

Assessment in 2026 reflects a mature understanding that AI is a permanent fixture in education and professional life. Rather than fighting technology, smart educators are redesigning evaluation to measure the skills that matter: critical thinking, creativity, communication, and the wisdom to use AI tools effectively while maintaining intellectual integrity.

The schools thriving in this new landscape treat assessment as an opportunity to teach better skills rather than simply catching cheaters.