Specialty · Specialty exam
AI for Teaching
Verifies whether educators protect students and uphold pedagogical judgment when using AI for instructional design, learning materials, grading, feedback, student data, and academic integrity.
Daily practice · Free
10 questions · 25 min · No sign-up needed
Specialty exam · Proctored
35 questions · 60 min · Raw score of 75 or more · $3.99 / one attempt
Specialty exam detailsExam overview
- Questions
- 35
- Time
- 60 min
- Pass mark
- 75 pts
- Recommended experience
- White Belt (AI Basics) proficiency and classroom teaching experience
Every question has five choices and is marked on an OMR answer sheet. Multiple-choice and true-or-false questions ask for the right combination from a box; ordering and matching questions ask for the right arrangement. Judgment questions (L3) are worth 3 points and all others 2; the raw score is converted to a 100-point scale and graded 1 to 9.
Exam domains
1. Instructional Design and Learning Materials20%
2. Grading, Feedback, and Student Assessment (High-Impact Area)25%
3. Student Data and Child Protection20%
4. Academic Integrity and AI Use Policies20%
5. Institutional Adoption and Operations15%
Weights follow the Specialty exam. Practice and mock exams may mix domains differently.
What this exam verifies
- Develop instructional materials and classroom activities aligned with learning objectives using AI, and verify their factual accuracy and target level
- Identify risks and establish human judgment checkpoints when incorporating AI grading and feedback into student evaluations
- Select AI tools while complying with student privacy and child protection requirements
- Design and implement context-appropriate academic integrity and AI use policies
- Establish institutional frameworks for AI adoption, educator support, and operational monitoring
Who it is for
- K-12 teachers and university faculty using AI in instruction and assessment
- Administrators and coordinators developing AI use policies for schools, academies, and corporate education
- Educational institution leaders evaluating EdTech adoption
Does not cover general curriculum development, EdTech product engineering, legal counsel for individual disciplinary cases, or legal determinations regarding foreign regulations, university admissions, and degree evaluations.
Domain details
Domain 1. Instructional Design and Learning Materials
- Knowledge: Prompting aligned with learning objectives and achievement standards, Factual errors and bias in AI-generated learning materials, Differentiating and adapting materials by student proficiency level, Copyright compliance and source attribution
- Skills: Review AI-generated learning materials against objective alignment and accuracy criteria, Revise activity sheet instructions for differentiated learning levels
Domain 2. Grading, Feedback, and Student Assessment (High-Impact Area)
- Knowledge: High-impact areas in student assessment under the Framework Act on Artificial Intelligence, Prior notice and output labeling requirements for generative and high-impact AI, Common error patterns in automated grading, Rubric-based feedback design, Student appeals and explanation procedures
- Skills: Determine valid criteria and conditions for factoring AI-assisted grading into student marks, Design workflows for educators to review, adjust, and finalize AI-drafted feedback
Domain 3. Student Data and Child Protection
- Knowledge: Personal data processing and legal guardian consent requirements for children under 14, Restrictions on using student records beyond their intended educational purpose, Data retention and model-training terms of third-party AI services, Third-party service delegation and cross-border data transfers, Statutory restrictions on disclosing school records to third parties, Age restrictions and parental notification standards
- Skills: Audit data terms and privacy conditions of AI services evaluated for classroom use, Select and implement methods to utilize AI without exposing student personal information
Domain 4. Academic Integrity and AI Use Policies
- Knowledge: Permissible boundaries of AI assistance categorized by assignment type, Disclosure and citation guidelines for AI tool usage, Technical limitations and false-positive risks of AI detection tools, Investigation protocols and due process for suspected academic misconduct
- Skills: Define and communicate assignment-specific AI permission levels and guidelines, Design inquiry and review procedures that do not rely solely on automated AI detector scores
Domain 5. Institutional Adoption and Operations
- Knowledge: Adoption objectives and key performance indicators, Professional development and ongoing support structures for educators, UNESCO Guidance for Generative AI in Education and Research, Operational monitoring (NIST AI RMF) and incident response by risk classification, Knowledge base management for document-grounded AI tutors
- Skills: Evaluate the comparative risks and instructional benefits of institutional AI adoption proposals, Establish post-adoption audit criteria, monitoring checklists, and review cadences
Official documentation
Specialty Course · AI for Teaching
Practice, mock exams and every course book are free. Pay only when you take an official test.