Education & Research

Research-grade data collection, student assessments, and AI-powered early intervention.

Standardized student assessments, research participant intake, and program evaluations — post-processed by AI to identify at-risk students, automate grading, screen research participants, and power institutional accreditation evidence collection.

Browse Apps

The challenge in education data collection

✕Student wellbeing assessments done on paper — data never analyzed
✕Research participant intake inconsistent and hard to validate
✕Accreditation evidence collection ad-hoc and time-consuming
✕At-risk student identification reactive, not proactive
✕Standardized assessments (essays, quizzes) graded manually at scale
✕Longitudinal research data tracked in spreadsheets — fragile and error-prone

Structured assessment → AI post-processing for education

Standardized student assessments and research instruments are post-processed by AI to automate grading, identify at-risk individuals, and power longitudinal analytics.

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STEP 01

Structured Assessment

Student, participant, or faculty completes standardized digital assessment on any device.

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STEP 02

AI Post-Processing

Responses auto-scored, at-risk flags generated, eligibility screened, patterns detected.

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STEP 03

Educator/Researcher Review

Instructor or researcher reviews AI-generated insights, not raw responses.

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STEP 04

Longitudinal Tracking

Progress tracked across time; trends inform program improvement and intervention.

Key use cases

Student Wellbeing Screening & Early Intervention

Universities deploy structured wellbeing screening at enrollment and ongoing. AI flags at-risk students for early outreach — before academic or personal crises occur.

Admissions and enrollment intake with conditional wellbeing screening
AI-powered early intervention flagging across student population
Accessibility needs assessment and accommodation workflows
Advisor referral routing based on AI-identified risk level
Longitudinal student wellbeing trend analytics

Clinical & Academic Research Data Collection

Ethics-compliant participant intake, validated assessment instruments, and longitudinal survey scheduling — with research-grade data exports for SPSS, R, and Python.

Ethics-compliant participant consent and intake workflows
Validated assessment instruments (PHQ-9, cognitive assessments, etc.)
Longitudinal survey scheduling and tracking across study timeline
Protocol deviation flagging and audit trail
Research-grade export: SPSS, CSV, REDCap integration

Standardized Assessment & Automated Grading

AI post-processes structured student assessments (essays, quizzes, proctored tests) for automated grading, competency scoring, and learning outcome measurement.

Structured assessment design with rubric-based scoring
AI grading of written responses against learning outcomes
Competency-based assessment with skill-level mapping
Analytics: learning outcome achievement by cohort and program
Accreditation evidence collection and reporting

Program Assessment & Accreditation Evidence

Structured program review workflows capture faculty, student, and employer feedback for accreditation. AI analyzes trends and identifies program improvement opportunities.