Bias in Assessment: How to Identify and Reduce It in Veterinary Education
Even the most well-intentioned assessments can accidentally reinforce inequities if we’re not intentional in how they’re built. In veterinary education—where clinical stakes are high and patient outcomes matter—bias in assessment design isn’t just about fairness, it also involves accuracy.
What Is Assessment Bias?
Assessment bias occurs when an exam or question systematically advantages or disadvantages groups of learners based on factors unrelated to their actual competence. These can include:
Cultural assumptions baked into case scenarios
Overreliance on prior experience (not yet taught content)
Ambiguous language or idioms that obscure what’s being tested
Gendered or racialized framing in examples
Unconscious instructor expectations when grading open-response items
According to the American Educational Research Association, biased assessments “fail to provide equally valid inferences for all test-takers.” In clinical education, this can misrepresent student readiness and impact confidence, progression, or opportunity. AERA Standards for Educational Testing
Why It Matters in Veterinary Programs
Veterinary education has increasingly diverse cohorts—from first-generation students to international learners and career changers. But many assessment items still assume:
Prior exposure to specific species or clinics
Familiarity with North American idioms or client interaction norms
Unspoken expectations around “professionalism” or communication style
These aren’t assessments of knowledge—they’re assessments of background.
Example: Rewriting a Biased Clinical Scenario
Original Question:
“Mrs. Jones brings her male Labrador Retriever to a small, suburban animal clinic because he’s been ‘acting off.’ She mentions that he hasn’t wanted to go on his usual morning walk and ‘turned up his nose at his favorite foods’ this morning. What’s your top differential diagnosis?”
Bias Triggers:
Assumes familiarity with client communication style
Cultural specificity in “Mrs. Jones,” suburban setting, and pet behavior
Vague descriptors (“acting off,” “turned up his nose”)
Prior exposure to Western pet ownership norms
Improved Version:
“A 9-year-old neutered male Labrador Retriever presents with lethargy and anorexia for the past 12 hours. The dog is normally active and has no significant medical history. On physical exam, temperature is 102.8°F, heart rate is 110 bpm, and capillary refill time is <2 seconds. What is your top differential diagnosis?”
Why It Works:
Focuses on objective, observable clinical signs
Removes non-essential context that could introduce bias
Maintains clinical relevance while enhancing fairness
Allows all students to focus on clinical reasoning, not cultural interpretation
This type of revision improves construct validity while promoting equity—the gold standard in assessment design.
Three Ways to Identify Hidden Bias
Welcome a diverse review team
Have assessments reviewed by faculty or students from different cultural, professional, and academic backgrounds.Check for construct irrelevance
Ask: Does this question test what I intend—or something else?
If a learner misses a question due to language or framing—not concept mastery—that’s a red flag.Analyze performance patterns
Look for disparities in question-level data across demographic groups. If a single question consistently trips up a subgroup, it merits review.
How to Reduce Bias Without Reducing Rigor
Use clear rubrics for grading open responses
Eliminate idiomatic language
Align each question to a specific, observable learning outcome
Represent diverse clients, species, and clinical environments
Pilot test high-stakes questions before full rollout
A Thought-Provoking Quote
“Fairness in testing doesn’t mean giving everyone the same thing—it means giving every learner an equal opportunity to demonstrate what they know.”
— Dr. Valerie Shute, assessment researcher and psychometrician
Final Thought
Bias in assessment isn’t always obvious—but it is always impactful. At V.E.T.S., we believe better questions build better clinicians.When assessments are designed with equity in mind, they don’t just become fairer—they become better at measuring what truly matters: clinical competence.
