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Computer Science at University of Richmond

University of Richmond, VA · Private nonprofit · Bachelor's Degree · Computer and Information Sciences, General
76 /100
DegreeOutlook Score (Base Case)
77
Optimistic
76
Base Case
70
Pessimistic
Earnings $76,763/yr (23% vs median)
AI Risk Very High (69% exposed)
Job Market Very Large (338,800 openings/yr)
ROI 4.5x earnings multiple
Ranked #239 of 443 Computer and Information Sciences, General programs

How AI Changes the Outlook

Three scenarios based on how aggressively AI disrupts the career paths available to Computer Science graduates.

Optimistic
No Disruption		 Base Case
Gradual AI		 Pessimistic
Aggressive AI
10-Year Earnings $1,191K $1,135K $830K
Earnings Multiple 4.8x 4.5x 3.3x
Probability of Field Employment 80% 74% 42%
DegreeOutlook Score 77 76 70

10-Year Earnings Projection

*Year 1 uses actual reported earnings. Scenarios diverge as AI impact compounds over time.

4-Year Tuition (Sticker)
$250,400
4-Year Net Price (After Aid)
$133,668
47% less than sticker · See by income
Median Debt at Graduation
$24,500
3.8 months of Year 1 earnings
Reported Earnings (5 Year)
$121,774
59% growth from Year 1

Program Analysis

At $76,763 per year, Computer Science graduates from University of Richmond earn slightly above the $62,617 national median. The premium is real but not dramatic.

The 4.5x return on tuition is positive but not overwhelming. Financial outcomes depend on keeping costs close to in-state rates.

AI exposure is significant at 69% of job tasks, producing a 30% spread between best and worst-case decade earnings. The field isn't immune to disruption.

The median debt load of $24,500 represents less than half a year of starting salary — among the lightest debt-to-income ratios we track.

At #239 out of 443 programs, University of Richmond's financial outcomes for Computer Science trail the majority of peers. The value case depends on other factors.

Earnings growth from $76,763 to $121,774 over five years (59% increase) indicates that graduates in this field see meaningful salary progression.

About University of Richmond

A 23% acceptance rate puts University of Richmond in competitive admissions territory, a smaller institution with 3,002 students in University of Richmond, VA. Financial aid reduces the effective four-year cost to $133,668 — 47% less than the list price.

See all programs and financial aid at University of Richmond →

Top Career Paths

Computer and information systems managers $171,200/yr
Computer and information research scientists $140,910/yr
Database architects $135,980/yr
View all 14 career paths with salary ranges and AI risk →

Compare & Explore

Compare this program → View all 443 schools offering Computer Science →

Computer Science at Other Schools

University of Pennsylvania ($146,204) University of California-Los Angeles ($136,099) Yale University ($133,293) Rice University ($131,154) Northeastern University ($115,587) Northeastern University Professional Programs ($115,587) University of Michigan-Ann Arbor ($113,634) University of Illinois Urbana-Champaign ($113,503)

Other Majors at University of Richmond

Accounting and Related Services ($71,508) Business Administration, Management and Operations ($68,151) Economics ($61,027) Human Resources Management and Services ($55,123) Political Science and Government ($43,253) Journalism ($43,157) English Language and Literature, General ($38,333) Psychology, General ($36,309)

Is a Trade Program a Better Fit?

Compare Computer Science trade programs on TradeSchoolWorth →

For students who prefer applied learning, trade programs can deliver strong earnings with significantly less debt and shorter time to employment.

Frequently Asked Questions

How does University of Richmond's Computer Science program score?
A score of 76/100 reflects decent absolute metrics, but University of Richmond trails the majority of Computer Science programs on relative rankings. Context matters more than the raw number.
How vulnerable is Computer Science to AI automation?
AI won't 'replace' Computer Science careers outright, but it is likely to reduce the number of job openings. We model 69% task exposure, which compresses field employment probability in our scenarios.
Scores use College Scorecard earnings, BLS employment projections, and AI task-exposure research. See full methodology →