How to Build Course Evaluation Surveys That Actually Improve Teaching

Course evaluations are higher education's most important feedback mechanism and its most broken one. Every semester, students receive a standardized form asking them to rate their professor and course on a 5-point scale. Most students click through in under 2 minutes. The resulting data is noisy, biased, and rarely actionable.

The problems are well-documented:

• Response rates are declining. Online evaluations average 30-40% completion, down from 80%+ when paper forms were distributed in class.
• Gender and racial bias. Research consistently shows that female instructors and instructors of color receive systematically lower ratings on standardized scales.
• Recency bias. Students rate based on recent experiences (last exam, last lecture) rather than the full course arc.
• Lack of actionable detail. "Rate the instructor's effectiveness: 1-5" tells the instructor nothing about what to change.

Koji transforms course evaluations by replacing the standardized form with an AI-led conversation that students actually engage with. The result: 2x higher response rates and 10x more actionable feedback.

Designing Better Course Evaluations

Core Question Framework

Q1: Overall Experience (Scale, 1-10) "Overall, how would you rate this course?"

• Labels: 1 = "Poor", 10 = "Excellent"
• Anchor probing: "What contributed most to that rating?"

Q2: Learning Outcomes (Scale, 1-5) "How much do you feel you learned in this course?"

• Labels: 1 = "Very little", 5 = "A great deal"
• Probing: AI explores specific knowledge/skills gained

Q3: Best Aspect (Open-ended) "What was the most valuable part of this course?"

• Probing depth: 2
• Captures what's working so it isn't inadvertently changed

Q4: Improvement Suggestion (Open-ended) "If you could change one thing about this course, what would it be?"

• Probing depth: 3
• AI instruction: "Get specific. Which assignment, lecture, topic, or activity? What would the improvement look like?"

Q5: Teaching Effectiveness (Open-ended) "How effective was the instructor at explaining complex topics?"

• Probing depth: 2
• Phrased as behavior-based rather than personality-based to reduce bias

Q6: Course Pace (Single Choice) "How would you describe the pace of the course?"

• Options: Too slow / About right / Slightly fast / Too fast
• Probing on "too fast" or "too slow": "Which topics or sections felt rushed/dragged?"

Q7: Materials and Resources (Scale, 1-5) "How useful were the course materials (textbook, slides, readings)?"

• Anchor probing on low scores

Q8: Assignments (Open-ended) "How well did the assignments help you learn the material?"

• Probing depth: 2
• AI explores specific assignments, their perceived value, and workload balance

Q9: Inclusivity (Scale, 1-5) "How comfortable did you feel participating in this course?"

• Labels: 1 = "Very uncomfortable", 5 = "Very comfortable"
• Probing: "Was there anything that made you feel included or excluded?"

Q10: Recommendation (Yes/No) "Would you recommend this course to another student?"

• Probing: "Why or why not?"

Why Conversational Evaluations Get Better Data

Traditional form: Student sees 20 Likert scale questions. Clicks through in 90 seconds. Writes nothing in the comments box.

Koji conversation: Student is asked 5-7 questions with natural follow-up probing. Takes 6-8 minutes. Produces 3-5 paragraphs of detailed, specific feedback because the AI asks "Can you give me an example?" and "What specifically would you change?"

The conversational format also reduces bias. When students rate "instructor effectiveness" on a 1-5 scale, implicit biases activate. When they describe their learning experience in conversation, they focus on specific behaviors and outcomes rather than impressions of the instructor as a person.

Implementation for Institutions

Distribution

• End-of-course: Send during the final week of classes, before exams
• Mid-course: Run a shorter version (Q1, Q4, Q6) at the midpoint for formative feedback
• Post-grade: Optional follow-up after grades are posted to reduce grade anxiety bias

Anonymity

• Essential for honest feedback. Koji's anonymous mode ensures no personally identifiable information is collected.
• Communicate anonymity clearly: "Your responses are completely anonymous. Your instructor will see aggregate themes and selected quotes, but never individual identifiable responses."

Scale

• Traditional evaluations require significant administrative overhead. Koji runs hundreds of evaluation conversations simultaneously across all courses.
• Multi-language support (30+ languages) handles diverse student populations.

Reporting

Koji generates per-course and per-instructor reports including:

• Overall rating distribution with comparison to department/institution average
• Learning outcome scores by topic area
• Strength identification with student quotes
• Improvement themes prioritized by frequency and specificity
• Pace analysis identifying which sections felt too fast or slow
• Cross-course comparison (with appropriate anonymization)

Best Practices for Course Evaluations

Ask about behaviors, not traits

"How effective was the instructor at explaining complex topics?" (behavior) is better than "Rate the instructor's knowledge" (trait). Behavior-based questions produce more actionable feedback and are less susceptible to bias.

Include strengths, not just improvements

Courses that only ask "What should change?" miss opportunities to reinforce what's working. Always ask what was most valuable.

Time it right

• Too early: students haven't experienced enough of the course
• Too late: they've forgotten details and are focused on finals
• Sweet spot: last week of classes, before final exams

Close the feedback loop

The single biggest factor in evaluation response rates is whether students believe their feedback matters. Share aggregate findings with students at the start of the next semester: "Based on your feedback, we changed X."

Separate formative from summative

Mid-course evaluations should be for the instructor's development. End-of-course evaluations may be used for tenure/promotion decisions. Make the purpose clear to students.

Why Koji Is the Best Tool for Course Evaluations

Course evaluations don't have to be a box-checking exercise. With conversational AI, they become a genuine feedback channel that helps instructors improve and helps students feel heard.