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

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

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.

Course Objectives

Participants will:

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

Target Audiences

1. School administrators
2. Curriculum and instructional leaders
3. Educational policymakers
4. Edtech entrepreneurs and startups
5. AI developers working in education
6. Teacher trainers and professional development coordinators
7. Educational researchers
8. 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@datastatresearch.org or call +254724527104 

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 DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.