GenAI Survey for Higher Education Assessment Professionals

Executive Summary

Compared to prior administrations, 76% (234 of 307) identify as assessment leads. Among Fall 2025 leaders (n=201, 84% of respondents), 71% use GenAI regularly or occasionally, primarily self-taught (84%), while navigating concerns about privacy, accuracy, and limited support. The data reveal a collaboration gap (only 10% frequently collaborate with faculty) and a possible influence paradox (48% office/team-level vs. 10% institutional-level influence), suggesting structural barriers to broader integration.*

71%

Regular + Occasional Users

87%

Report Efficiency Gains

84%

Self-Taught

71%

Privacy and Ethical Concerns

Key Themes from the Field

Theme 1: Efficiency & Creative Capacity

The dominant benefit remains efficiency (87%), but respondents describe deeper value: GenAI "allows us to quickly become the expert in the room on a diverse range of topics" and "increases my creativity and skills level in areas where I am typically weak." Communication-related benefits surged from 54% to 78% from summer to fall.

"I am excited to learn and be a part of using AI to assist with assessment."

Theme 2: Tool Diversification & Payment Realities

From spring to fall, ChatGPT's dominance is cooling (82%→74%) as professionals discover alternatives. Claude doubled (13%→27%); Copilot reached 61%. Overall, fewer report using free versions. Copilot leads institutional support (86%), followed by Gemini (46%), and ChatGPT (33%).

"Easier to find troubleshooting help for data visuals/data analysis tools."

Theme 3: The Faculty Collaboration Gap

Frequent faculty collaboration dropped from 12% in summer to 8% in fall, while "never collaborate" surged from 30% to 45%. Yet 58% believe faculty GenAI integration should be part of their role somewhat or to a great extent.

"My main concern is lack of training on how to use AI as a tool and how to correctly prompt."

Theme 4: High Interest, Limited Influence

Assessment professionals have the most influence within their immediate office or teams (46%) and less at the institutional (10%) and external (2%) levels—a 36-point gap.

"Primarily I need a community of others validating my own usage and collaborating on strategies both for use by my office and implementation institution-wide."

Note: This convenience sample captures how some of us are experiencing GenAI at the intersection of institutional/program assessment and student affairs roles. While not representative, these data offer the field's only window into adoption patterns among assessment professionals.

*Data represent assessment leaders only (76% of Fall = 234 of 307).

Demographics

Respondents are predominantly staff/administrators (82%) working in academic affairs (79%), with strong representation of mid-career professionals (6–20 years: 56%). Women comprise about two-thirds; approximately three-quarters identify as White.

Primary Role

Spring n=199, Summer n=164, Fall n=234

Reporting Line

Reporting Lines

Spring n=199, Summer n=164, Fall n=234

Assessment Leadership

Lead Assessment at Institution?

Fall n=307 pre-filter • Dashboard = leaders only (n=234)

Years in Profession

Years in Higher Ed Assessment

Spring n=177, Summer n=151, Fall n=218

Gender Identity

Spring n=218, Summer n=151, Fall n=216

Race/Ethnicity

Spring n=171, Summer n=150, Fall n=216

Institution

Respondents represent diverse institutional contexts: nearly half from public institutions (47%), a quarter from private non-profits (27%), and less community college representation (17%). Medium to large institutions (10K–50K students) account for about half. Geographic reach spans all regions with notable shifts between survey administrations.

Institution Size

Student Enrollment

Spring n=178, Fall n=216 • Not asked Summer

Institution Type

Institution Type*

Spring n=177, Fall n=217 • MSI includes HBCUs, HSIs, AANAPISIs • Not asked Summer

Geographic Distribution

Top 15 States

Spring n=173, Fall n=210 • Not asked Summer

Key Shifts Spring to Fall

Massachusetts ↓8 pts

12% (n=21/173) → 4% (n=8/210)

California ↑4 pts

6% (n=11/173) → 10% (n=22/210)

Indiana ↑4 pts

2% (n=3/173) → 6% (n=12/210)

Texas ↓6 pts

9% (n=15/173) → 3% (n=6/210)

All States (Spring → Fall)

AL1% → 2%

AK0% → 0%

AZ3% → 2%

AR1% → 0%

CA6% → 10%

CO0% → 3%

CT2% → 1%

DE1% → 0%

DC0% → 1%

FL4% → 5%

GA3% → 2%

HI1% → 0%

ID0% → 0%

IL4% → 3%

IN2% → 6%

IA1% → 1%

KS1% → 2%

KY2% → 1%

LA1% → 1%

ME1% → 1%

MD2% → 5%

MA12% → 4%

MI2% → 4%

MN2% → 2%

MS1% → 0%

MO1% → 2%

NE3% → 0%

NV1% → 0%

NH1% → 0%

NJ3% → 1%

NM1% → 0%

NY9% → 5%

NC3% → 5%

ND0% → 0%

OH3% → 3%

OK1% → 0%

OR1% → 1%

PA5% → 2%

RI1% → 1%

SC1% → 2%

SD0% → 0%

TN2% → 1%

TX9% → 3%

UT0% → 1%

VT0% → 0%

VA3% → 5%

WA1% → 1%

WV0% → 0%

WI0% → 1%

WY0% → 0%

Terr0% → 0%

Intl1% → 1%

Highlighted = ≥3% either wave

* Institution type is "select all that apply." Geographic data not collected Summer 2025.

Use & Tools

GenAI adoption appears to be maturing: regular use increased to 33% while non-use dropped to 7%. ChatGPT remains dominant but declining (82%→74%) as alternatives gain traction. Copilot benefits from institutional support (86–90%), while Claude and others rely heavily on free tiers or personal payment.

Current Use

Current GenAI Use in Assessment Work

Spring n=199, Summer n=162, Fall n=233

Perceived Enhancement

How Much Has GenAI Enhanced Your Practice?

Summer n=112, Fall n=164 (users only) • Not asked Spring

Tools Used

GenAI Tools/Platforms Used

Spring n=166, Summer n=112, Fall n=163 (users)

Payment Source

Summer Payment

Who pays? (Summer users)

Fall Payment

Who pays? (Fall users)

* "Considering" added Fall. Grok/LLaMa not asked Spring 2025.

Tasks

Assessment professionals use GenAI most for learning outcomes (56%), rubrics (54%), and data analysis/reporting (54% each). Administrative applications center on communications (78%) and ideation (62%). Notable: Coding/Excel tasks jumped from 1% to 44% between Spring and Fall.

Assessment Tasks

Assessment Tasks Using GenAI

Spring n≈190, Summer n=109, Fall n=162

Administrative Tasks

Administrative Tasks Using GenAI

Spring n≈190, Summer n=109, Fall n=161

* Spring "Meetings" aggregated from separate meeting-related items.

Benefits & Training

Efficiency dominates (87%), with communications benefit surging 24 points (54%→78%). Training is largely self-directed: 84% are self-taught, though 72% have attended formal sessions. Top needs: more training (79%), peer examples (72%), and clear guidelines (58%).

Benefits Experienced

Benefits from GenAI Use

Summer n=106, Fall n=160 • Not asked Spring

Supports Needed

Supports Needed for More Effective Use

Summer n=152, Fall n=219, • Open-ended in Spring

Training Participation

GenAI Training Activities

Summer n=154, Fall n=227

Training by Type (Spring)

Training: Taken vs Developed vs Neither

Self-guided n=181, Courses n=183, One-on-one n=164

* "Expands access" benefit added Fall 2025. Benefits not asked Spring 2025.

Concerns

Privacy and ethical concerns lead (71%), followed by lack of institutional policy (50%) and environmental impact (43%). Qualitative responses emphasize accuracy issues (12+ mentions of "hallucination"), concerns about cognitive offloading, and equity implications of AI's environmental footprint.

Concerns

Concerns About Using GenAI

Summer n=155, Fall n=228

71%

Privacy/Ethics (↓3)

50%

Policy Lacking (↑5)

43%

Environment (↓7)

43%

Lack of Training (↑3)

* Spring used open-ended format. "Other" (11%) emphasized accuracy/hallucination.

Policy

Only 23% report having institutional GenAI policy, though 29% say one is actively developing. Spring data revealed a policy vacuum at division (66% no policy) and office levels (75% no policy). The gap between institutional attention and local guidance persists.

Institution-Wide Policy

Has Institution Developed Formal GenAI Policy?

Summer n=154, Fall n=227

Policy by Level (Spring)

Policy at Each Organizational Level

Institution n=187, Division n=184, Office n=185

23%

Have Policy (Fall)

29%

Actively Developing

15%

Not Expecting Policy

Spring 2025 Policy Landscape

Institution: 39% Yes, 39% No, 23% Unsure
Division: 8% Yes, 66% No, 27% Unsure
Office: 9% Yes, 75% No, 16% Unsure

* Spring asked at three levels; Summer/Fall asked institution-wide with development status.

Collaboration

Faculty collaboration is declining—frequent collaboration dropped from 12% in summer to 8% in fall while "never" rose from 30% to 45%—despite majority agreement (58%) that such collaboration should be part of assessment roles.

Collaboration Frequency

How Often Collaborate with Faculty on GenAI

Summer n=154, Fall n=227 • Not asked Spring

12%

Frequently (Summer)

→

Frequently (Fall)

While "Never" increased:

30%

Never (Summer)

→

45%

Never (Fall)

Should This Be Part of Role?

Fall: Should Faculty Integration Be Part of Role?

Fall n=226

Summer: Role Includes Supporting Faculty

Summer n=130 (excluding N/A)

Collaboration Effectiveness

Fall: Effectiveness

Fall n=125 (collaborators only)

Summer: Effectiveness

Summer n=83 (excl. 46% N/A)

* Summer 2025 role used agree/disagree.

Influence

Forty-six percent of assessment professionals report substantial individual office or team influence, but only 10% at institutional level and 2% externally.

Sphere of Influence (Fall)

Degree of Influence on GenAI Integration

Fall n=215–220 per item • Excludes "Unsure"

The Influence Paradox: Substantial Influence by Level

46%

Team

→

15%

Course

→

13%

Program

→

10%

Institution

→

External

36-point gap reveals localized authority without systemic voice.

Purpose

To track adoption, application, and implications of GenAI in assessment practice and identify needs for policy, training, and infrastructure. Given the rapidly evolving technology landscape, the survey recurs approximately every four months (Spring, Summer, Fall).

Scope & Methods

Respondents represent diverse institutional types and assessment roles. The initial launch (January–April 2025) yielded 199 valid responses after data cleaning. The second pulse survey (launched and closed in August 2025) produced 164 valid responses. The third pulse survey (October–November 2025) produced 234 valid responses from assessment leaders. The study employs a mixed-methods approach combining descriptive statistics with qualitative thematic coding of open-ended responses using constant comparative analysis.

Limitations

Convenience sampling may favor certain practitioner types. Recruitment likely introduced selection bias toward professionally engaged individuals. Geographic and demographic distributions may not fully represent all U.S. assessment professionals.

Survey Instrument

Download the full survey instrument: GenAI Pulse Survey Fall 2025 Instrument

Next Survey

The Spring 2026 pulse survey will be available late Febuary.

Thank You

Thank you to Tyton Partners for granting permission to adapt their policy question from Time for Class 2025.

Definitions

• Assessment professional: Individual with central role in developing, implementing, managing, and reporting academic, co-curricular, or student affairs assessment practices in higher education

• Generative AI (GenAI): Large language model-based tools that create new content (text, images, music, video, code). Examples: ChatGPT, Claude, Copilot, Gemini

Research Team

Leads: Ruth Slotnick, Ph.D.; Joanna Boeing, Ed.M.; Bobbijo Grillo Pinnelli, Ed.D.

Team: Yu Bao, Ph.D.; John Hathcoat, Ph.D.; Will Miller, Ph.D.; Naima Wells, Ph.D.

Results generously hosted by the Center for Leading Improvements in Higher Education, Indiana University Indianapolis by the Assessment Institute in Indianapolis . For more information, contact Dr. Ruth Slotnick, rslotnick@bridgew.edu. IRB #2025055, Bridgewater State University.