AI-ASSSEMNT.KZ1
AI in Assessment and Feedback
Intermediate Selbstgesteuert · 1 Jahr Zugang
01 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
11 Interaktive Lektionen · 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 +
- 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
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
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