Educational Leadership and Management

Training Course on Leading AI Ethics and Bias Mitigation in Educational Tech

Introduction

In an era where artificial intelligence (AI) plays an increasing role in shaping educational experiences, it is imperative for leaders in education to prioritize ethical AI implementation and bias mitigation strategies. Training Course on Leading AI Ethics and Bias Mitigation in Educational Technology equips educators, edtech developers, administrators, and policymakers with the essential skills to lead the ethical use of AI in educational technology. By addressing data bias, algorithmic fairness, privacy, and transparency, this course fosters a responsible and inclusive learning ecosystem. With strong foundations in AI governance, digital ethics, and educational equity, participants will be empowered to drive innovation while safeguarding learners' rights.

This course leverages real-world case studies, regulatory frameworks like GDPR and FERPA, and insights from leading global institutions to explore critical aspects of ethical AI deployment in learning platforms, assessment tools, and adaptive technologies. It is designed to provide practical solutions for addressing AI-driven discrimination, enhancing accountability, and aligning with digital transformation trends in K–12 and higher education sectors.

Programme Curriculum

Training Course on Leading AI Ethics and Bias Mitigation in Educational Technology

Introduction

Course Objectives

Participants will:

Understand AI ethics and digital responsibility in educational settings.
Identify and mitigate algorithmic bias in edtech tools.
Explore the impact of machine learning on student assessment and feedback.
Assess data privacy laws and compliance (FERPA, COPPA, GDPR).
Develop inclusive AI-driven strategies for diverse learners.
Analyze ethical dilemmas in adaptive learning systems.
Create frameworks for AI transparency and accountability.
Use AI audit tools to evaluate edtech platforms.
Promote equity-first digital transformation in classrooms.
Investigate human-in-the-loop AI models for safe use.
Build policies for fair and explainable AI.
Foster stakeholder engagement in AI policy development.
Lead discussions on future-ready ethical AI leadership in education.

Target Audiences

School administrators
Curriculum and instructional leaders
Educational policymakers
Edtech entrepreneurs and startups
AI developers working in education
Teacher trainers and professional development coordinators
Educational researchers
Compliance and data privacy officers

Course Duration: 5 days

Course Modules

Module 1: Foundations of AI Ethics in Education

Definition and principles of AI ethics
Importance of ethics in educational AI systems
Core ethical challenges in learning environments
Risks of unregulated AI usage in classrooms
Global ethical frameworks (UNESCO, OECD)
Case Study: Cambridge Analytica & its implications for edtech ethics

Module 2: Understanding Algorithmic Bias

What is algorithmic bias in education?
Types of bias: historical, data, representation
Bias in assessment algorithms and recommendation engines
Tools to detect and measure bias
Equity-centered design approaches
Case Study: Racial bias in predictive admissions algorithms

Module 3: Data Privacy and Legal Compliance

Overview of student data protection laws
Implications of FERPA, GDPR, and COPPA
Privacy-by-design in edtech product development
Data minimization and informed consent
Student data governance and stakeholder roles
Case Study: FERPA violation case in a U.S. district using AI tools

Module 4: Inclusive AI Design for Learning

Universal design for learning (UDL) in AI
Culturally responsive edtech development
Addressing the digital divide and accessibility
Preventing exclusion in adaptive learning systems
Designing for neurodivergent and multilingual learners
Case Study: Inclusive AI in Google’s Read Along app

Module 5: AI Transparency and Explainability

Black box vs. explainable AI in education
Building trust through algorithmic transparency
Interpretable models and decision logic
Reporting tools for educators and learners
Engaging stakeholders with explainable feedback
Case Study: IBM’s Watson Education and explainability efforts

Module 6: Ethical Implementation of Adaptive Systems

Role of AI in personalization and adaptive learning
Avoiding overreliance on automation
Bias risks in personalization algorithms
Educator oversight and intervention models
Balancing personalization with student agency
Case Study: Knewton and ethical concerns around adaptive learning

Module 7: Auditing and Evaluating AI Systems

AI audit frameworks and benchmarks
Checklist for bias and risk assessment
Internal vs. third-party AI audits
Metrics for fairness, accuracy, and impact
Audit reporting to regulatory bodies
Case Study: European AI audit standards applied in Finland schools

Module 8: Leading Change and Shaping Policy

Building AI ethics policies in education systems
Change management for responsible AI adoption
Forming multidisciplinary AI ethics committees
Advocating for student-centered AI governance
Collaborative policymaking with educators, tech, and parents
Case Study: NYC Department of Education AI Ethics Advisory Council

Training Methodology

Interactive lectures and expert guest sessions
Group discussions and ethical dilemma role-playing
Real-life case study analysis
AI ethics simulations using edtech tools
Hands-on audit practice with open-source bias detection software
Reflective journaling and policy drafting exercises

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

AI ethics bias detection policy frameworks

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000