AI for Education Course

Lesson 1 • How AI Systems Learn

Explains training data, model outputs, and prediction logic. Connects learning mechanisms to observable AI behaviour in educational tools.

Lesson 2 • Types of AI Used in Education

Surveys generative AI, recommendation engines, and NLP tools deployed in classrooms. Helps educators match tool types to instructional needs.

Lesson 3 • Capabilities and Limitations of AI

Contrasts what AI does reliably with where it fails. Prepares educators to set realistic expectations for learners and administrators.

Lesson 1 • Administrative and Communication Tools

Covers AI tools for scheduling, email drafting, and parent communication. Shows how reducing administrative load frees time for instruction.

Lesson 2 • Learner-Facing Learning Tools

Examines AI tutors, writing assistants, and practice platforms used directly by learners. Connects tool selection to learning objectives and learner autonomy.

Lesson 3 • Evaluating and Selecting AI Tools

Provides a framework for comparing tools on accuracy, privacy, cost, and accessibility. Enables informed procurement and adoption decisions.

Lesson 4 • Grading

Reviews AI tools that mark, flag plagiarism, and provide formative feedback. Links automated feedback to educator review workflows.

Lesson 1 • Prompting for Instructional Content

Applies prompting techniques to generate lesson plans, explanations, and examples. Builds fluency in translating instructional goals into prompt language.

Lesson 2 • Quizzes

Uses prompts to create tests, rubrics, and feedback templates. Connects assessment design prompts to learning objectives and Bloom's taxonomy.

Lesson 3 • Iterative Prompting and Refinement

Teaches follow-up prompting, constraint-setting, and output revision strategies. Moves educators from single-shot prompts to productive multi-turn workflows.

Lesson 4 • Advanced Prompting Techniques

Introduces chain-of-thought, few-shot, and persona-based prompting for complex tasks. Expands educator capability for nuanced and specialised content generation.

Lesson 1 • Sequencing and Pacing with AI Support

Uses AI to map content sequences, identify prerequisite gaps, and plan pacing guides. Strengthens curriculum coherence across units and courses.

Lesson 2 • Designing Differentiated Instruction with AI

Uses AI to generate tiered materials, scaffolds, and enrichment tasks. Addresses diverse learner needs without multiplying educator workload.

Lesson 3 • Building Engaging Learning Experiences

Applies AI to create scenarios, simulations, and interactive elements. Enhances learner engagement while keeping the educator in the design role.

Lesson 4 • Reviewing and Validating AI-Generated Lessons

Establishes a quality-review process for AI-produced instructional materials. Ensures accuracy, bias awareness, and pedagogical soundness before classroom use.

Lesson 1 • Bias in AI Educational Tools

Identifies how training data bias produces inequitable AI outputs in educational contexts. Equips educators to detect and mitigate bias in tools they deploy.

Lesson 2 • Transparency and Accountability

Establishes norms for disclosing AI use to learners, parents, and institutions. Builds educator accountability practices around AI-generated content and decisions.

Lesson 3 • Developing an Ethical AI Use Policy

Guides educators through drafting classroom and institutional AI use policies. Produces a practical policy document grounded in ethical principles.

Lesson 4 • Learner Data Privacy and Consent

Covers data collection practices, consent requirements, and learner privacy protections. Connects privacy principles to tool selection and classroom policy.

Lesson 1 • Analysing Learner Performance Data

Interprets AI-generated dashboards and learning analytics to identify trends. Translates data insights into targeted instructional adjustments.

Lesson 2 • Automating Formative Feedback

Deploys AI tools to deliver real-time, personalised feedback on learner work. Connects automated feedback to educator review and follow-up instruction.

Lesson 3 • Designing Assessments with AI Assistance

Generates varied assessment formats aligned to cognitive levels using AI prompts. Reduces design time while maintaining assessment validity and rigor.

Lesson 4 • Maintaining Assessment Integrity

Addresses AI-assisted cheating, detection tools, and redesigned assessment strategies. Balances academic integrity with fair and authentic evaluation practices.

Lesson 1 • Critical Evaluation of AI Outputs

Teaches learners to fact-check, question, and verify AI-generated content. Develops habits of critical thinking that transfer across subjects and tools.

Lesson 2 • Designing AI Learning Activities for Learners

Creates structured classroom activities where learners practise AI use with guidance. Builds a reusable activity library aligned to curriculum goals.

Lesson 3 • AI as a Learning Partner

Frames AI as a tool for exploration, feedback, and self-directed learning. Teaches learners to use AI to deepen understanding rather than bypass effort.

Lesson 4 • Building Learner AI Literacy

Introduces age-appropriate frameworks for teaching learners how AI works. Establishes foundational AI literacy as a prerequisite for responsible learner use.

Lesson 1 • Building an AI Integration Roadmap

Develops a phased plan for scaling AI tools across programmes and departments. Aligns the roadmap to institutional goals, budgets, and timelines.

Lesson 2 • Assessing Institutional AI Readiness

Evaluates infrastructure, staff capacity, and cultural readiness for AI adoption. Produces a readiness profile that informs a realistic integration roadmap.

Lesson 3 • Communicating AI Strategy to Stakeholders

Prepares educators and leaders to communicate AI plans to parents, boards, and staff. Builds trust through transparent, evidence-based messaging.

Lesson 4 • Professional Development for AI Adoption

Designs training programmes that build educator AI competency at scale. Connects professional development to ongoing practice and peer learning communities.