AI-Supported Teaching and Learning Practice Course

Overview

AI-Supported Teaching and Learning Practice Course is a five-day course for teachers, academic coordinators, instructional designers, learning technology teams, and education quality personnel, who leave with an AI-Supported Teaching Implementation Plan. Participants design lessons and activities, review resources, support learners, plan formative assessment and feedback, address academic integrity and accessibility, and maintain educator oversight. Agile Leaders Training Center provides training in AI-supported teaching and learning practice.

Who Should Attend

• Teaching personnel responsible for lessons, activities, learner guidance, and classroom decisions
• Academic coordination personnel responsible for curriculum alignment, assessment, and teaching quality
• Instructional design personnel responsible for learning activities, resources, and course structure
• Learning technology personnel responsible for digital tools, access, and educator support
• Education quality personnel responsible for evidence, review, integrity, and improvement

The course assumes participants contribute to teaching or learning design, and it leaves out education policy, school administration, general digital transformation, and technical AI development.

Departments and Industries

The course supports responsible AI-assisted practice across education and workplace learning.

• Schools and classroom teaching functions
• Universities and academic departments
• Vocational and professional learning providers
• Curriculum and instructional design teams
• Corporate learning and development functions
• Education quality and learning technology teams

Learning Objectives

By the end of this course, participants will be able to:

• Apply a suitability screen to teaching and learning cases
• Build lesson and activity designs with educator oversight
• Evaluate AI-assisted learning resources and learner support
• Use formative assessment and feedback tools
• Analyze integrity, accessibility, privacy, and bias risks
• Build an AI-Supported Teaching Implementation Plan

Course Agenda

Day 1: Teaching Use Cases and Oversight

• AI Teaching Use-Case and Suitability Map
• Learning Purpose and Educator Decision Canvas
• Human Oversight and Intervention Matrix
• Learner Impact and Participation Screen
• Responsible Use Boundary Checklist

Day 2: Lessons, Activities, and Resources

• AI-Assisted Lesson Design Canvas
• Learning Activity Alignment Grid
• Learning Resource Accuracy Review Checklist
• Source, Context, and Bias Review Sheet
• Educator Approval and Revision Record

Day 3: Learner Support and Accessibility

• Learner Need and Support Planning Map
• Personalization Boundary Decision Guide
• Learning Accessibility and Inclusion Screen
• Student Question and Guidance Workflow
• Support Escalation and Human Handoff Log

Day 4: Assessment, Feedback, and Integrity

• Formative Assessment Design Sheet
• Feedback Quality and Actionability Rubric
• Assessment Evidence Verification Checklist
• Academic Integrity Response Guide
• Learning Progress and Adjustment Record

Day 5: AI-Supported Teaching Practice

• Suggested Exercise: Screen a Teaching Use Case
• Suggested Exercise: Design and Review a Lesson Activity
• Suggested Exercise: Build Learner Support and Accessibility Steps
• Suggested Exercise: Review Assessment, Feedback, and Integrity Evidence
• Capstone Exercise: AI-Supported Teaching Implementation Plan

Practical Exercises

The course uses suggested activities that convert teaching cases into responsible AI-supported practice.

• Suggested activity: map a teaching case, learning purpose, educator decisions, learner impact, and responsible-use boundaries
• Suggested activity: design a lesson activity, review resource accuracy and bias, and record educator revisions
• Suggested activity: plan learner support, check accessibility, route questions, and document human handoffs
• Suggested activity: design formative assessment, review feedback, verify evidence, and address an integrity concern

FAQs

Who suits AI-supported teaching and learning practice, and what does it assume?

AI-supported teaching and learning practice suits personnel responsible for lessons, curriculum, resources, learner support, assessment, feedback, quality, or learning technology. It assumes familiarity with teaching or learning responsibilities.

How does AI-supported teaching differ from instructional design training?

AI-supported teaching focuses on educator use of AI across lessons, resources, learner support, assessment, feedback, integrity, accessibility, and oversight, while instructional design training centers on the broader design of learning experiences.

How can educators review AI-assisted learning resources?

Educators should check accuracy, source quality, context, bias, accessibility, curriculum alignment, learner suitability, privacy boundaries, and required revisions before using an AI-assisted resource.

How should AI support formative assessment and feedback?

AI should assist with question design, evidence organization, feedback drafting, and learning-pattern review while educators verify evidence, interpret learner needs, and retain responsibility for judgments.

What belongs in an AI-Supported Teaching Implementation Plan?

The plan should contain selected use cases, learning purposes, lesson tools, resource checks, learner-support steps, assessment and feedback controls, integrity responses, accessibility checks, oversight roles, and review measures.

Conclusion

Participants take back an AI-Supported Teaching Implementation Plan linking learning purposes, lesson activities, resources, learner support, assessment, feedback, integrity, accessibility, and oversight. It changes how educators select, review, and control AI-supported teaching practices. The plan supports traceable classroom decisions and continued human responsibility.