AI-ASSSEMNT.KZ1 ISBN: 979-8-90059-174-2
AI in Assessment and Feedback
Master AI in Assessment and Feedback with this comprehensive course featuring hands-on labs, practice exercises, and expert-led modules.
What you will be able to do
- AI-Assisted Assessment Design: Architecting robust evaluation strategies that leverage generative AI while maintaining cognitive demand and Bloom's Taxonomy alignment.
- Adaptive Testing Implementation: Building personalized assessment models and competency frameworks that utilize dynamic difficulty and mastery-based progression for learners.
- Ethical AI Governance: Managing the complexities of privacy, intellectual property, and algorithmic transparency within high-stakes educational assessment environments.
- AI-Aware Redesign: Developing authentic assessment workflows that address academic integrity concerns through graduated AI-use frameworks and security analysis.
Intermediate Self-paced · 1 year access
1 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / About
About This Course
Try Free →
No credit card required
This AI in Assessment and Feedback course provides a rigorous framework for integrating generative AI into educational evaluation. You will move beyond theory to build actual assessment evidence strategies, design rubrics, and implement adaptive testing models. We cover the technical realities of LLM limitations, prompt engineering for scoring, and the critical balance between human agency and automation. Expect to navigate complex challenges regarding academic integrity, data privacy, and algorithmic bias. By the end, you will deploy an end-to-end AI-enhanced assessment solution. Note that while AI increases efficiency, it requires constant human oversight to mitigate hallucination risks and ensure valid, equitable learner outcomes across diverse disciplines.
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
11 Interactive Lessons · 95 topics01 Preface 8 topics +
- About This Course
- Target Audience and Prerequisites
- What This Course Deliberately Does Not Cover (Scope Boundaries)
- What You Will Learn
- Course Overview
- Learning with uCertify
- Standards or Certification Alignment
- Closing
02 Foundations of Assessment, Learning, and Feedback 9 topics +
- Assessment, Evaluation, and Evidence
- Learning Outcomes and Constructive Alignment
- Purposes and Forms of Assessment
- Course-, Program-, and Curriculum-Level Assessment
- Stakeholders, Roles, and Quality Assurance
- Feedback, Feedback Literacy, and Evaluative Judgment
- Validity, Reliability, Fairness, and Measurement Uncertainty
- Item Quality and Applied Psychometrics
- Summary
03 AI Foundations, Literacy, and Generative AI Workflows 9 topics · 1 LiveLab +
- AI Technologies Used in Assessment
- Generative AI, Large Language Models, and Multimodality
- Capabilities, Limitations, and Failure Modes
- Prompt and Context Design for Assessment Tasks
- Structured Outputs, Exemplars, and Reusable Templates
- Context-Aware and Retrieval-Grounded Assessment
- AI Agents and Multi-Step Assessment Workflows
- Human–AI Collaboration and Student AI Literacy
- Summary
1 LiveLab in this lesson — see the labs panel →
04 AI-Assisted Assessment Design 8 topics +
- Building an Assessment Evidence Strategy
- Cognitive Demand and Bloom's Taxonomy in the AI Era
- Assessment Blueprints and Balance
- Objective, Short-Answer, and Extended-Response Assessment
- Rubrics and Scoring Criteria
- Clarity, Accessibility, and Inclusive Assessment
- Quality-Assuring AI-Generated Assessment Material
- Summary
05 AI-Assisted Scoring, Feedback, and Learner Judgment 9 topics +
- Foundations of AI-Assisted Scoring
- Scoring Objective and Written Responses
- Rubric-Based AI Scoring
- Diagnostic Feedback and Feedforward
- Characteristics of Effective Feedback
- Errors, Misconceptions, and Remediation
- Peer Review, Self-Assessment, and Evaluative Judgment
- Calibration, Consistency, and Error Correction
- Summary
06 Personalized Assessment, Adaptive Testing, and Learning Analytics 9 topics +
- Personalized and Adaptive Assessment Models
- Learner Profiles, Competency Models, and Skill Tracking
- Adaptive Item Selection and Dynamic Difficulty
- Computerized Adaptive Testing and High-Stakes Assessment
- Mastery-Based Progression and Digital Credentials
- Assessment Data and Learning Analytics
- Patterns, Risk Signals, and Responsible Intervention
- Using Analytics to Improve Assessment and Learning
- Summary
07 Evaluating AI Assessment Systems 9 topics +
- Why AI Assessment Systems Should Be Evaluated
- Evaluation Design, Test Sets, and Benchmarks
- Metrics, Reliability, Validity, and Acceptance Thresholds
- Evaluating Questions, Rubrics, Scores, and Feedback
- Human, SME, and Inter-Rater Evaluation
- Bias, Fairness, and Differential Outcomes
- Error Analysis, Edge Cases, and Adversarial Conditions
- Version Sensitivity, Monitoring, and Educational Impact
- Summary
08 Academic Integrity and AI-Aware Assessment Redesign 9 topics +
- Academic Integrity in the Generative AI Era
- The AI Assessment Scale and Graduated AI-Use Frameworks
- AI Detection, Evidence, and Due Process
- Assessment Vulnerability and Security Analysis
- Authentic and AI-Aware Assessment Design
- Process, Portfolio, Version, and Reflection Evidence
- Oral Defense, Viva Voce, and Demonstration
- Policy, Attribution, Learner Agency, and Rigor
- Summary
09 AI Assessment Across Disciplines and Professional Practice 8 topics +
- Writing, Communication, and Humanities
- Social Sciences and Qualitative Work
- Mathematics, Science, and Quantitative Reasoning
- Programming and Computational Thinking
- AI, Machine Learning, and Data Science
- Technical, Laboratory, and Practice-Based Competencies
- Disciplinary Transfer and Assessment Choice
- Summary
10 Responsible AI, Governance, Tool Selection, and Implementation 9 topics +
- Ethical Principles, Human Agency, and Accountability
- Accessibility, Inclusion, and Equitable Participation
- Privacy, Security, Intellectual Property, and Vendor Data Practices
- Transparency, Explainability, Review, and Appeal
- Selecting AI Assessment Tools and Vendors
- Cost, Infrastructure, and Sustainable Scale
- Institutional Readiness, Governance, and Staff Capability
- Operational Implementation, Monitoring, and Continuous Improvement
- Summary
11 Capstone: An End-to-End AI-Enhanced Assessment Solution 8 topics +
- Needs Analysis and Problem Definition
- Learning Outcomes, Evidence, and Assessment Strategy
- AI Approach, Tools, and Human Responsibilities
- Building the Assessment, Rubric, and Feedback System
- Testing and Evaluating the Solution
- Assurance and Implementation Planning
- Communication, Portfolio, and Capstone Defense
- Summary
Hands-On Labs Our edge
1 LiveLabs- Designing Effective Prompts for Assessment Tasks
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
Does this course include practical coding or just theory?+
This course is highly practical, featuring hands-on labs and 50 practice exercises designed to help you build real-world assessment systems.
How does this course address the risk of AI hallucinations in grading?+
We focus heavily on evaluation design, human-in-the-loop verification, and rubric-based scoring to ensure AI outputs remain reliable and valid.
Is this training suitable for higher education faculty?+
Yes, the curriculum is specifically designed for higher education professionals looking to modernize assessment workflows while maintaining academic integrity.
Keep going
You might also like
Master AI-Powered Assessment
Learn to design AI-enhanced assessments, implement adaptive testing, and deliver ethical, effective feedback for better learning outcomes.