Training Course on Ethical Considerations for Emerging Technologies in Education
Training Course on Ethical Considerations for Emerging Technologies in Education is designed to equip educators, administrators, and technologists with the ethical frameworks, policy guidelines, and best practices necessary for responsibly integrating emerging technologies into teaching and learning processes.
Course Overview
Training Course on Ethical Considerations for Emerging Technologies in Education
Introduction
In today’s fast-evolving digital learning environments, emerging technologies such as Artificial Intelligence (AI), Virtual Reality (VR), Blockchain, and adaptive learning systems are transforming education. However, with these advancements come pressing ethical challenges — including student data privacy, algorithmic bias, equity in access, and responsible AI implementation. Training Course on Ethical Considerations for Emerging Technologies in Education is designed to equip educators, administrators, and technologists with the ethical frameworks, policy guidelines, and best practices necessary for responsibly integrating emerging technologies into teaching and learning processes.
This course emphasizes responsible innovation, ethical digital transformation, and technology equity while aligning with global standards and regulations. Participants will explore real-world case studies, develop ethical evaluation skills, and create action plans that ensure inclusive, safe, and fair tech integration. With increasing reliance on edtech tools, this course empowers leaders to make informed, values-driven decisions that prioritize student welfare, digital citizenship, and institutional accountability.
Course Objectives
Participants will:
- Understand the ethical implications of AI in educational settings.
- Evaluate data privacy and security concerns in edtech applications.
- Analyze algorithmic bias and fairness in adaptive learning tools.
- Explore informed consent practices in digital learning environments.
- Address accessibility and inclusion in tech-enhanced education.
- Navigate legal frameworks around student data use and surveillance.
- Promote digital well-being and screen time balance.
- Develop AI literacy and ethical risk assessment skills.
- Examine deepfake risks and content authenticity in education.
- Create ethical edtech implementation policies.
- Apply global standards like GDPR, FERPA, and COPPA.
- Encourage interdisciplinary collaboration for ethical decision-making.
- Formulate institution-specific ethical guidelines for emerging technologies.
Target Audience
- K-12 Educators
- Higher Education Faculty
- Curriculum Developers
- EdTech Entrepreneurs
- School Administrators
- Government Policy Makers
- Instructional Designers
- ICT Coordinators and Technologists
Course Duration: 5 days
Course Modules
Module 1: AI and Machine Learning in Education
- Introduction to AI tools in classrooms
- Understanding algorithmic bias and fairness
- Student profiling and predictive analytics risks
- Ethical uses of AI chatbots and tutoring bots
- Policy frameworks for responsible AI
- Case Study: Bias in AI Grading Systems
Module 2: Data Privacy and Student Surveillance
- Types of educational data collected
- Consent and student data ownership
- Risks of third-party data sharing
- Legal frameworks: GDPR, FERPA
- Building a data protection policy
- Case Study: Proctoring Tools and Student Trust
Module 3: Accessibility and Inclusion
- Designing tech for students with disabilities
- Closing the digital divide in low-resource schools
- Culturally responsive edtech
- Language barriers and localization challenges
- UDL (Universal Design for Learning) principles
- Case Study: Remote Learning in Rural Areas
Module 4: Digital Well-Being and Screen Time Ethics
- Mental health and screen overuse
- Gamification ethics in learning apps
- Setting digital boundaries for learners
- Encouraging offline learning practices
- Educator responsibilities in managing tech use
- Case Study: Screen Addiction in Primary Schools
Module 5: Deepfakes, Misinformation, and Content Authenticity
- Recognizing manipulated media in learning
- Teaching media literacy and fact-checking
- Authenticity in student submissions
- Security against impersonation in virtual spaces
- Policies for monitoring content integrity
- Case Study: Deepfakes in Online Assignments
Module 6: Blockchain, NFTs, and Digital Credentials
- Introduction to decentralized credentialing
- Ownership of digital learning records
- Ethical concerns in blockchain implementation
- NFT-based student achievements: hype or help?
- Long-term access and security of blockchain data
- Case Study: Blockchain Degrees in Universities
Module 7: Virtual Reality, Augmented Reality, and Ethics
- Sensory overload and cognitive impacts
- Safety and boundaries in immersive learning
- Privacy concerns in AR/VR data collection
- Equity of access to expensive tech
- Content appropriateness for young users
- Case Study: VR Trauma in Simulated History Lessons
Module 8: Crafting Ethical EdTech Policies
- Components of an ethical edtech policy
- Stakeholder involvement in policy creation
- Training educators on ethical tech use
- Continuous review and impact monitoring
- Creating a tech ethics audit checklist
- Case Study: Institutional Ethics Guidelines at MIT
Training Methodology
- Interactive lectures and expert presentations
- Hands-on group activities and scenario-based roleplays
- Real-world case study discussions
- Digital ethics audit simulation exercises
- Peer-to-peer knowledge sharing and reflection
- Policy drafting workshop and final presentation
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 LD account, as indicated in the invoice so as to enable us prepare better for you.